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ISSN 1664-8714
ISBN 978-2-8325-4037-4
DOI 10.3389/978-2-8325-4037-4

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Biomarkers and immunotherapy of hepatic-biliary-pancreatic cancers

Topic editors

Yunfei Xu – Shandong University, China

Hongda Liu – Nanjing Medical University, China

Xuesong Gu – Beth Israel Deaconess Medical Center, Harvard Medical School, United States

Zheng Gong – Jackson Laboratory, United States

Citation

Xu, Y., Liu, H., Gu, X., Gong, Z., eds. (2023). Biomarkers and immunotherapy of hepatic-biliary-pancreatic cancers. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-8325-4037-4

Table of Contents

Prognostic stratification based on m⁵C regulators acts as a novel biomarker for immunotherapy in hepatocellular carcinoma

Ping Liu, Ziqing Zhu, Jiayao Ma, Le Wei, Ying Han, Edward Shen, Xiao Tan, Yihong Chen, Changjing Cai, Cao Guo, Yinghui Peng, Yan Gao, Yongting Liu, Qiaoqiao Huang, Le Gao, Yin Li, Zhaohui Jiang, Wantao Wu, Yihan Liu, Shan Zeng, Wei Li, Ziyang Feng and Hong Shen

CEP192 is a novel prognostic marker and correlates with the immune microenvironment in hepatocellular carcinoma

Yanli Liu, Wanmei Liang, Yabin Chang, Zehui He, Meijian Wu, Haozhi Zheng, Xinrong Ke, Minjia Lv, Qingqian Liu, Qinyu Liu, Waner Tang, Qiaoling Huang, Yu Lu, Min He, Qijun Yang, Chunpan Mo, Jiefan Wang, Kunwei Peng, Zhiqun Min, Hang Su and Jingqi Chen

Comprehensive characterization of extracellular matrix-related genes in PAAD identified a novel prognostic panel related to clinical outcomes and immune microenvironment: A silico analysis with in vivo and vitro validation

Xu Chen, Qihang Yuan, Jifeng Liu, Shilin Xia, Xueying Shi, Yuxin Su, Zhizhou Wang, Shuang Li and Dong Shang

Research progress of bile biomarkers and their immunoregulatory role in biliary tract cancers

Yun-cheng Li, Kang-shuai Li, Zeng-li Liu, Yong-chang Tang, Xiao-Qiang Hu, Xing-yong Li, An-da Shi, Li-ming Zhao, Li-Zhuang Shu, Shuo Lian, Zhang-di Yan, Shao-hui Huang, Guo-li Sheng, Yan Song, Yun-jia Liu, Fan Huan, Ming-hui Zhang and Zong-li Zhang

Prognostic model of immune checkpoint inhibitors combined with anti-angiogenic agents in unresectable hepatocellular carcinoma

Xiaomi Li, Wei Sun, Xiaoyan Ding, Wei Li and Jinglong Chen

Overexpression of SMS in the tumor microenvironment is associated with immunosuppression in hepatocellular carcinoma

Lin Xiang, Longhuan Piao, Dong Wang and Li-Feng-Rong Qi

Budd-Chiari syndrome and its associated hepatocellular carcinoma: Clinical risk factors and potential immunotherapeutic benefit analysis

Kang-Shuai Li, Sen Guo, Yu-Xin Chen and Zong-Li Zhang

Efficacy of immune checkpoint inhibitors plus molecular targeted agents after the progression of lenvatinib for advanced hepatocellular carcinoma

Fucun Xie, Bowen Chen, Xu Yang, Huaiyuan Wang, Ge Zhang, Yanyu Wang, Yunchao Wang, Nan Zhang, Jingnan Xue, Junyu Long, Yiran Li, Huishan Sun, Ziyu Xun, Kai Liu, Xiangqi Chen, Yang Song, Xiaobo Yang, Zhenhui Lu, Yilei Mao, Xinting Sang, Yinying Lu and Haitao Zhao

Conversion therapy for advanced intrahepatic cholangiocarcinoma with lenvatinib and pembrolizumab combined with gemcitabine plus cisplatin: A case report and literature review

Wei Zhang, Chu Luo, Zun-Yi Zhang, Bi-Xiang Zhang and Xiao-Ping Chen

Identification of a CD4+ conventional T cells-related lncRNAs signature associated with hepatocellular carcinoma prognosis, therapy, and tumor microenvironment

Lin Zhu, Xiu-Ping Zhang, Shuai Xu, Ming-Gen Hu, Zhi-Ming Zhao, Guo-Dong Zhao, Zhao-Hui Xiao and Rong Liu

The effect of anti-PD-1/PD-L1 antibodies combined with VEGF receptor tyrosine kinase inhibitors versus bevacizumab in unresectable hepatocellular carcinoma

Hui Zeng, Qi Xu, Jinyu Wang, Xiaoqing Xu, Jun Luo, Lei Zhang, Cong Luo, Jieer Ying and Jingjing Li

Alkaline phosphatase combined with γ-glutamyl transferase is an independent predictor of prognosis of hepatocellular carcinoma patients receiving programmed death-1 inhibitors

Lei Xu, Lin Chen, Bin Zhang, Zhicheng Liu, Qiumeng Liu, Huifang Liang, Yifa Chen, Xiaoping Chen, Chao Leng and Bixiang Zhang

Tumor infiltrating T cell states and checkpoint inhibitor expression in hepatic and pancreatic malignancies

Shanshan Wan, Ende Zhao, Daniel Freeman, Daniel Weissinger, Benjamin A. Krantz, Gregor Werba, Lauren G. Khanna, Despina Siolas, Paul E. Oberstein, Pratip K. Chattopadhyay, Diane M. Simeone and Theodore H. Welling

Corrigendum: Tumor infiltrating T cell states and checkpoint inhibitor expression in hepatic and pancreatic malignancies

Shanshan Wan, Ende Zhao, Daniel Freeman, Daniel Weissinger, Benjamin A. Krantz, Gregor Werba, Lauren G. Khanna, Despina Siolas, Paul E. Oberstein, Pratip K. Chattopadhyay, Diane M. Simeone and Theodore H. Welling

Research into the characteristic molecules significantly affecting liver cancer immunotherapy

Junhong Chen, Hengwei Jin, Hao Zhou, Xufei Hei and Kai Liu

Intensity-modulated radiotherapy combined with systemic atezolizumab and bevacizumab in treatment of hepatocellular carcinoma with extrahepatic portal vein tumor thrombus: A preliminary multicenter single-arm prospective study

Kang Wang, Yan-Jun Xiang, Hong-Ming Yu, Yu-Qiang Cheng, Zong-Han Liu, Jing-Ya Zhong, Shuang Feng, Qian-Zhi Ni, Hong-Fei Zhu, Wei-Wei Pan, Jing-Jing Li, Chao Liang, Hong-Kun Zhou, Yan Meng, Wan Yee Lau and Shu-Qun Cheng

Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy

De-Zhen Guo, Shi-Yu Zhang, San-Yuan Dong, Jia-Yan Yan, Yu-Peng Wang, Ya Cao, Sheng-Xiang Rao, Jia Fan, Xin-Rong Yang, Ao Huang and Jian Zhou

A novel liver zonation phenotype-associated molecular classification of hepatocellular carcinoma

Tao Zhang, Jian Gu, Xinyi Wang, Yaoyao Lu, Kailin Cai, Huili Li, Yingli Nie, Xiangdong Chen and Jiliang Wang

SMC4, a novel tumor prognostic marker and potential tumor therapeutic target

Zonglei Zhao, Xixiu Wang, Yan Ding, Xuefeng Cao and Xingyuan Zhang

Case report: Preliminary response to tislelizumab plus S-1 in patients with metastatic gallbladder carcinoma: A report of five cases and a literature review

Yuzhu Zhang, Yuchen Liu, Jing Liu, Tiande Liu, Hu Xiong, Wen Li, Xiaowei Fu, Fan Zhou, Shousheng Liao, Lu Fang and Bo Liang

Immune-related adverse events correlate with the efficacy of PD-1 inhibitors combination therapy in advanced cholangiocarcinoma patients: A retrospective cohort study

Yanfang Zhang, Xiaoting Wang, Yinyan Li, Yun Hong, Qingwei Zhao and Ziqi Ye

Molecular subtype identification and signature construction based on Golgi apparatus-related genes for better prediction prognosis and immunotherapy response in hepatocellular carcinoma

Liang Sun, Zitao Liu, Zhengyi Wu, Ke Ning, Junwen Hu, Zhendong Chen, Zhipeng Wu and Xiangbao Yin

Chromosome 11q13 amplification correlates with poor response and prognosis to PD-1 blockade in unresectable hepatocellular carcinoma

Kai Yan, Ding Zhang, Yanan Chen, Wenfeng Lu, Mengli Huang, Jinping Cai, Shiqing Chen, Ting Bei, Yuezong Bai, Jian Lv, Yong Fu and Haibin Zhang

Multicenter, single-arm, phase II study (CAP) of radiotherapy plus liposomal irinotecan followed by camrelizumab and anti-angiogenic treatment in advanced solid tumors

Jie Shen, Jing Yan, Juan Du, Xiaoqin Li, Jia Wei, Qin Liu, Hongmei Yong, Xiaolu Wang, Xiaofeng Chang, Zhou Ding, Wu Sun, Chenxi Liu, Sihui Zhu, Jingyi Guo, Huajun Li, Ying Liu, Wulou Zhang, Zonghang Liu, Rutian Li and Baorui Liu

Transmembrane protein 88 suppresses hepatocellular carcinoma progression and serves as a novel prognostic factor

Lin Cai, Yu Du, Kai Song, Peng Peng and Fei Han

Prognostic value of programmed cell death ligand 1 expression in patients with intrahepatic cholangiocarcinoma: a meta-analysis

Feng Xian, Dacheng Ren, Jun Bie and Guohui Xu

Functional reprogramming of peripheral blood monocytes by soluble mediators in patients with pancreatic cancer and intraductal papillary mucinous neoplasms

Austin M. Eckhoff, Michael C. Brown, Karenia Landa, Ibtehaj Naqvi, Eda K. Holl, David Boczkowski, Ashley Fletcher, Kristen E. Rhodin, Minh Huy Giang, Bruce Sullenger, Georgia M. Beasley, Peter J. Allen and Smita K. Nair

ORIGINAL RESEARCH
published: 09 September 2022
doi: 10.3389/fimmu.2022.951529

Prognostic stratification based on m⁵C regulators acts as a novel biomarker for immunotherapy in hepatocellular carcinoma

Ping Liu ¹,², Ziqing Zhu ¹,², Jiayao Ma ¹,², Le Wei ¹,², Ying Han ¹,², Edward Shen ³, Xiao Tan ¹, Yihong Chen ¹,², Changjing Cai ¹,², Cao Guo ¹,², Yinghui Peng ¹,², Yan Gao ¹,², Yongting Liu ¹,², Qiaoqiao Huang ¹,², Le Gao ¹,², Yin Li ¹,², Zhaohui Jiang ¹,², Wantao Wu ¹,², Yihan Liu ¹,², Shan Zeng ¹,²,⁴, Wei Li ¹,²*, Ziyang Feng ¹,²* and Hong Shen ¹,²,⁴*

¹ Department of Oncology, Xiangya Hospital, Central South University, Changsha, China, ² Key Laboratory for Molecular Radiation Oncology of Hunan Province, Xiangya Hospital, Central South University, Changsha, China, ³ Department of Life Science, McMaster University, Hamilton, ON, Canada, ⁴ National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School, United States

Reviewed by:
Guo Wen Zhi, Zhengzhou University, China
Qingyuan Zheng, First Affiliated Hospital of Zhengzhou University, China
Haihong Zhu, Zhejiang University, China

*Correspondence:
Wei Li
wli1550@csu.edu.cn 
Ziyang Feng
2204140320@csu.edu.cn 
Hong Shen
hongshen2000@csu.edu.cn

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 24 May 2022

Accepted: 22 August 2022

Published: 09 September 2022

Citation:
Liu P, Zhu Z, Ma J, Wei L, Han Y, Shen E, Tan X, Chen Y, Cai C, Guo C, Peng Y, Gao Y, Liu Y, Huang Q, Gao L, Li Y, Jiang Z, Wu W, Liu Y, Zeng S, Li W, Feng Z and Shen H (2022) Prognostic stratification based on m⁵C regulators acts as a novel biomarker for immunotherapy in hepatocellular carcinoma. Front. Immunol. 13:951529. doi: 10.3389/fimmu.2022.951529

Background

Immunotherapy is a promising anti-cancer strategy in hepatocellular carcinoma (HCC). However, a limited number of patients can benefit from it. There are currently no reliable biomarkers available to find the potential beneficiaries. Methylcytosine (m⁵C) is crucial in HCC, but its role in forecasting clinical responses to immunotherapy has not been fully clarified.

Methods

In this study, we analyzed 371 HCC patients from The Cancer Genome Atlas (TCGA) database and investigated the expression of 18 m⁵C regulators. We selected 6 differentially expressed genes (DEGs) to construct a prognostic risk model as well as 2 m⁵C-related diagnostic models.

Results

The 1-, 3-, and 5-year area under the curve (AUC) of m⁵C scores for the overall survival (OS) was 0.781/0.762/0.711, indicating the m⁵C score system had an ideal distinction of prognostic prediction for HCC. The survival analysis showed that patients with high-risk scores present a worse prognosis than the patients with low-risk scores (p< 0.0001). We got consistent results in 6 public cohorts and validated them in Xiangya real-world cohort by quantitative real-time PCR and immunohistochemical (IHC) assays. The high-m⁵C score group was predicted to be in an immune evasion state and showed low sensitivity to immunotherapy, but high sensitivity to chemotherapy and potential targeted drugs and agents, such as sepantronium bromide (YM-155), axitinib, vinblastine and docetaxel. Meanwhile, we also constructed two diagnostic models to distinguish HCC tumors from normal liver tissues or liver cirrhosis.

Conclusion

In conclusion, our study helps to early screen HCC patients and select patients who can benefit from immunotherapy. Step forwardly, for the less likely beneficiaries, this study provides them with new potential targeted drugs and agents for choice to improve their prognosis.

Keywords: m⁵C, immunotherapy, biomarker, drug sensitivity, HCC, precision medicine

Introduction

Primary liver cancer is a common gastrointestinal malignancy and the fourth leading cause of cancer-related death worldwide, with approximately 782,000 deaths each year (1). HCC is the most prevalent subtype of primary liver cancer, as the 5-year overall survival rate is less than 20% (2, 3).

The recent achievement of immune checkpoint blockers (ICBs) has established immunotherapy as the most promising cancer treatment strategy. Despite the advancement achieved in therapeutic modalities for HCC, the overall survival (OS) remains poor (4). The reason is that many patients are diagnosed at an advanced stage, an abundance of HCC patients exhibit hyposensitivity to these therapies, and the nearly unavoidable drug resistance stands in the way of an eventual cure (5). There are currently no reliable and validated biomarkers or methods available to early screen the HCC patients and correctly forecast clinical responses to immunotherapy. Therefore, the development of meaningful diagnosis and treatment response stratification biomarkers for achieving precision medicine is urgently needed.

The TME is a heterogeneous system that is comprised of cancer cells, immune cells, extracellular matrix, microvessels, and a variety of cytokines and chemokines (6, 7). It plays an important role in the pathogenesis of HCC (8). Patients with the same pathological stage and grade have considerably diverse functional hallmarks, which might lead to variable clinical responses to the same therapy. In particular, substantial TME heterogeneity makes precision medicine in HCC difficult to obtain. Thus, displaying TME heterogeneity might disclose several aspects of HCC biology and expand our understanding of HCC therapy. In the setting of TME heterogeneity, developing innovative therapeutic response predictors and therapeutic targets could be a prospective way to promote precision medicine in HCC.

According to etiological research, a range of environmental stresses generates changes in liver RNA, which eventually bring about changes in the liver epigenome and transcriptome, which also implies that epigenetic modifications accelerate the onset and development of HCC (9, 10). Exploring the role of RNA modification in a range of biological processes has recently emerged as a new research focus (11). The most common modifications currently include N6-methyladenosine (m⁶A), m⁵C, 5-hydroxymethylcytosine (hm⁵C), N7-methylguanosine (m⁷G), N1-methyladenosine (m¹A), and pseudouridine (ψ) (12). The role of m6A modification in regulating RNA processing and functioning has been studied extensively in the past (13). Evolving data suggest that m⁵C plays a significant part in posttranscriptional regulation (14). Furthermore, m⁵C modification, characterized by the insertion of a methyl group at the carbon-5 position of the cytosine base, was discovered to be copious in mammalian cells (15). m⁵C regulation is a dynamic process governed by three key regulators, methyltransferases (writers), demethylases (erasers), and binding proteins (readers) (16). Increasing data have demonstrated that m⁵C modification emerges as a contributor to shaping TME heterogeneity, as well as the progression of HCC through interaction with various m⁵C regulators (14). In addition, there is growing evidence that methylation regulators can serve as prognostic and diagnostic markers for cancer (17). A better knowledge of different m⁵C modification patterns in HCC would contribute to the analysis of HCC diagnosis and prognosis, thus guiding individual clinical diagnosis and treatment strategies.

The past research on m⁵C regulators-mediated methylation modification model in HCC was focused on the effect of a single TME cell type or single m⁵C regulator alone on tumor development, and the overall TME infiltration portrait mediated by several m⁵C regulators remains to be comprehensively recognized. In addition, present predictive models are limited to predicting the prognosis and the immunotherapy response but overlook the diagnostic role. And to better achieve precision medicine, more attention should be paid to patients who are less susceptible to immunotherapy. Moreover, existing researches lack real-world cohort validation and the selected regulators are incomprehensive (18, 19).

As a result, we took the mRNA expression levels of 18 m⁵C regulators into consideration to assess their comprehensive relevance to diagnosis, TME heterogeneity, drug sensitivity, and therapeutic opportunities and identified robust risk signatures for HCC prognosis. Then, we (I) constructed 2 diagnostic and a prognostic model based on comprehensive 18 m⁵C regulators; (II) built an m⁵C score to predict immunotherapy response, and (III) determined potential drugs in HCC (Figure 1).

Figure 1 | The workflow of our study.

Methods and materials

Data collection and processing

The TCGA data included TCGA-LIHC and other TCGA digestive cancers (TCGA-COAD, TCGA-READ, TCGA-STAD, TCGA-CHOL, and TCGA-PAAD) with clinical information were obtained from the Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/). ICGC-LIRI-JP datasets were downloaded from the International Cancer Genome Consortium (ICGC) Data Portal (https://dcc.icgc.org/). GSE14520 datasets were obtained from Gene Expression Omnibus databases (GEO). The IMvigor210 cohort (20) data were obtained from the “IMvigor210CoreBiologies” R package. Riaz et al. Cell 2017 (GSE91061) (21) and Lauss et al. Nat Commun 2017 (GSE100797) (22) were gathered from the TIDE website (http://tide.dfci.harvard.edu/) with a detailed clinical information and gene expression data. The transcripts per kilobase million (TPM) values of gene expression were used for further analysis.

Unsupervised clustering for 18 m⁵C regulators

Sum up to 18 m⁵C regulators were selected to identify different m⁵C clusters by m⁵C regulators. Based on the gene expression data of these m⁵C regulators in the training cohort (TCGA-LIHC cohort), we used an unsupervised cluster algorithm via the R package “ConsensuClusterPlus”. The R package “NbClust” and Silhouette algorithm were applied to verify the optimal cluster number further. Principal component analysis (PCA) and tSNE were used to visualize different clusters in two-dimensional.

Identifying DEGs between m⁵C cluster

We distinguished the DEGs between two m⁵C clusters via the “DESeq2” R package. The DEGs were defined as adjusted p-value< 0.05 and |log2FoldChange| > 1. A total of 1267 DEGs between two m⁵C clusters were extracted for further analysis.

Functional enrichment for DEGs

To explore the functions of DEGs, enrichment analysis of these genes was conducted by R package “clusterprofiler” R package, based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. False discovery rate (FDR)< 0.25 and p-value< 0.05 were considered statistically significant.

Gene set variation analysis

To investigate the differential pathway of m⁵C clusters, the gene set variation analysis “GSVA” R package was used to perform GSVA. The gene sets of hallmarks were downloaded from the MSigDB database for GSVA analysis. The adjusted p-value< 0.05 was considered statistically significant.

Gene signature identification and establishment of the prognostic risk model

We performed the prognostic analysis for DEGs in two m⁵C clusters using a univariate Cox regression. As a result, 142 DEGs with a significant prognosis while a p-value ≤ 0.0001 were extracted for further analysis. The prognostic risk model was constructed by using the “glmnet” and “My.stepwise” R package based on LASSO and stepwise Cox method. Finally, we obtained a 6-gene-based prognostic risk model to calculate each patient’s risk score by weighting the Cox regression coefficients. The formula is as follows:

RiskScore = coefficient of each modeled gene * expression of each modeled gene, where “ i “ represents the modeled gene, “ Coef “ represents the coefficients of regression, and “ expr “ represents the expression of gene.We verified the prognostic risk model in validation cohorts. The time-dependent receiver operating characteristic (ROC) curves were used to evaluate the prognostic prediction accuracy of the risk model and the area under the curve (AUC) was measured by the R package “survivalROC”.

Estimation of TME characterization

To explore the correlation between the prognostic risk model and the immune cell infiltration level, we use the CIBERSORT algorithm to assess the infiltration abundance of 22 immune cells. And we obtained collected 20 inhibitory immune checkpoints from Auslander’s study (23). The Tumor Immune Dysfunction and Exclusion (TIDE) and IPS algorithms were used to evaluate tumor immune escape status. To compare the TME characterization of two different clusters, first, the R package “IOBR” was used to analyze the TME pathway via the single-sample gene set enrichment analysis (ssGSEA) (24, 25) algorithm. Meanwhile, a set of gene signatures that represent a non-inflamed TME, and tumor therapy-associated response were collected from Jiao Hu’s study (26).

Drug sensitivity and potential compounds

Broad Institute-Cancer Cell Line Encyclopedia (CCLE) project (https://portals.broadinstitute.org/ccle/) contained 1019 cancer cell line RNA expression profile data. Drug sensitivity data of cancer cell lines were obtained from the Genomics of Drug Sensitivity in Cancer (GDSC2, https://www.cancerrxgene.org/) and the PRISM Repurposing dataset (19Q4, released December 2019, https://depmap.org/portal/prism/). The GDSC2 contains 809 cell lines and sensitivity data for 198 compounds, and the secondary PRISM contains 499 cell lines and drug susceptibility data for 1448 compounds. All three of these drug datasets provide the area under the dose-response curve (area under the curve — AUC) values as a measure of drug susceptibility, and lower AUC values indicate increased susceptibility to compound responses. The drug susceptibility of the TCGA-LIHC cohort was estimated via the R package “oncoPredict”.

Connectivity Map (CMap) analysis was a complement to further investigate the therapeutic potential of drugs in HCC. We first analyzed differential expression genes between the TCGA-LIHC tumor and normal samples. And 150 up-regulated genes and 150 down-regulated genes were submitted to the CMap website (https://clue.io/query). The CMap analysis yielded a connectivity score for each perturbation, a negative score represents a gene expression pattern of a certain perturbation that is oppositional to the disease-specific expression pattern, suggesting a potential therapeutic effect of this perturbation in this disease.

Xiangya cohort

55 fresh hepatocellular carcinoma tissues and part of paired normal liver tissues were collected from Xiangya hospital and immediately frozen in liquid nitrogen. A retrospective cohort was created, with a total of 55 patients enrolled as of September 1, 2021. After the follow-up period, the clinical characteristics were obtained, and survival analysis and multivariate Cox regression were performed. This study was reviewed and approved by the Xiangya Hospital Medical Ethics Committee of Central South University (No.201806928), and got consent from all participants.

Quantitative reverse-transcription PCR

The AG RNAex Pro Reagent (AG21102, Accurate Biology, Changsha, China) was used to extract total RNA from 55 fresh hepatocellular carcinoma tissues and part of paired normal liver tissues according to the manufacturer’s instructions. Before reverse transcription to cDNA, genomic DNA is eliminated by treatment for 2 min at 42°C with gDNA Clean Reagent. Evo M-MLV RT Kit (AG11705, Accurate Biology, Changsha, China) was used to synthesize the complementary RNA. The SYBRR Premix Pro Taq HS qPCR Kit (AG11708, Accurate Biology, Changsha, China) was utilized to achieve real-time quantification. The 2-△△CT approach was applied to assess the relative expression levels of target genes, which were normalized by GAPDH. The PCR primers are listed in Supplementary Table S6.

Immunohistochemical staining

The protein expression levels of Programmed death-ligand 1 (PD-L1; also called B7-H1 or CD274) and cytotoxic T-lymphocyte antigen-4 (CTLA4) were estimated via immunohistochemical (IHC) staining. Briefly, after deparaffinization and rehydration, antigen retrieval was performed by heating the slides in EDTA buffer (G1203; Servicebio, Wuhan, China). Then the slides were treated with 3% hydrogen peroxide (Annjet, Shandong, China) for 25 mins to eliminate endogenous peroxidase and blocked with 3% BSA (G5001; Servicebio) for 30 minutes at room temperature to decrease nonspecific binding. The slides were then incubated overnight at 4°C with rabbit anti-CD274 (PDL1) and anti-CTLA4 primary antibodies at a dilution of 1:200 and 1:300, respectively (CD274: catalog no. Ab205921, Abcam, UK; CTLA4: catalog no. ab237712, Abcam, UK). Next, the slides were incubated with Goat Anti-rabbit IgG/HRP secondary antibody (G1215; Servicebio) for 50 minutes at 37°C. The DAB solution was used for coloration, and hematoxylin was used for counterstaining.

Immunostaining of CD274 (PDL1) and CTLA4 were defined on a scale of 0 to 3 in which 0 means no staining, 1 means mild staining, 2 means medium staining, and 3 means intense staining. The percentage score of stained cells were also calculated on a scale of 1 to 4 in which 1 represents (0–25%), 2 = (26–50%), 3 = (51–75%) and 4 = (76– 100%). To obtain the final score, the intensity score and percentage score were added to reach the final score ranging from 0 to 7.

Statistical analysis

Correlations between variables were explored with Pearson correlation coefficients. Continuous variables were compared between two groups through the Wilcoxon rank-sum test. Survival analysis including Kaplan-Meier and Cox regression analysis was performed by “survival” R package. The optimal cut-off value in training cohort TCGA-LIHC and validation cohorts were determined by the “surv_cutpoint” function in the “survminer” R package. Unless otherwise stated, P-value< 0.05 was regarded as statistically significant. The whole work was conducted in R 4.1.2 software.

Result

The landscape of expression and genetic variation of m⁵C regulators in HCC

We collected 18 m⁵C regulator genes from previous studies (27–31). The multi-omics landscape of these 18 m⁵C regulator genes was summarized from the TCGA-LIHC cohort (Figure 2). Most of the m⁵C regulator genes were significantly differentially expressed in tumor and normal tissues. For instance, ALKBH1, ALYREF, DNMT1, DNMT3A, DNMT3B, NOP2, NSUN2, NSUN3, NSUN4, NSUN5, TET1, TET2, TET3, TRDMT1, and YBX1 were up-regulated in HCC, while DNMT3L, NSUN6, and NSUN7 were down-regulated (Figure 2A). We then explored the comprehensive correlations and prognostic value of m⁵C regulators in the TCGA-LIHC cohort (Figure 2B). The univariate Cox regression analysis further demonstrated that most of these m⁵C regulator genes played a risk factor role in HCC (Figure 2C). Then we found out that these genes had widespread copy number variations (CNVs) (Figure S1) but infrequent mutations (Figure 2D). For example, ALYREF, DNMT3A, DNMT3B, DNMT3L, NOP2, NSUN2, NSUN4, NSUN5, NSUN6, NSUN7, TET1, and TRDMT1 focused on copy number amplification, whereas ALKBH1, DNMT1, TET2, and YBX1 preferred deletion, which suggests that CNVs’ dominant role in the regulation of m⁵C relative to mutation. Metascape (32) is a web tool for gene annotation and analysis, the enrichment analyses of 18 m⁵C regulators are displayed in Figure 2E.

Figure 2 | The landscape of expression and genetic variation of m⁵C regulators in HCC. (A) The expression profiles of m⁵C regulator genes in tumor tissues and normal tissues in TCGA-LIHC cohort. (* p<0.05, *** p<0.001, ns: no significant difference). (B) The interactions of 18 m⁵C regulator genes and their prognostic value. (C) Univariate Cox regression analysis of the 18 m⁵C regulator genes in patients from TCGA-LIHC cohort. (D) The mutation frequency of 18 m⁵C regulators in 364 patients from the TCGA-LIHC cohort. (E) Metascape enrichment network visualization of 18 m⁵C regulators. Cluster annotations were shown in the color code.

Clustering of HCC based on 18 m⁵C regulators

A total of 371 samples from TCGA-LIHC were defined as the training cohort, and further divided into k clusters (k = 2~6) via the “ConsensusClusterPlus” R package. We found out that k = 2 was the best number of clusters according to the CDF curve of the consensus score (Figures S2A, B). The Silhouette algorithm and “NbClust” R package further confirmed the result (Figures S2C, D). The principal component analysis and tSNE method of 18 m⁵C regulator gene expression showed significant separation between two clusters (Figures S2E, F). Finally, 248 patients were classified as Cluster 1, and the rest 123 patients were classified as Cluster 2. Compared to Cluster 1, the Kaplan-Meier analysis showed a worse OS and progression-free survival (PFS) in Cluster 2 (Figures S2G–H, log-rank test, p< 0.001).

Clinical characteristics of gene set enrichment between clusters

To further explore the differences between the two clusters, we compared the clinical characteristics between the two clusters. The result obtained shows that showed Cluster 2 had a higher proportion of advanced disease stage, tumor stage, and histologic grade (Figures 3A–C, Fisher’s exact test, p< 0.01). We then analyzed the differentially expressed genes (DEGs) by using the “DESeq2” R package, a total of 1267 DEGs between two m⁵C clusters were obtained according to the adjusted p-value less than 0.05, and the absolute value of log2 fold change less than 1. Next, these DEGs were used for biological functional enrichment analysis. These DEGs were primarily enriched in stromal and catabolic as well as metabolic pathways (Figures 3D, E), such as small molecule catabolic process (GO term), response to xenobiotic stimulus (GO term), organic acid catabolic process (GO term), complement and coagulation cascades (KEGG pathway), metabolism of xenobiotics by cytochrome P450 (KEGG pathway), and drug metabolism - cytochrome P450 (KEGG pathway). Further GSVA analysis on hallmark pathway analysis (Figure 3G) revealed that patients in Cluster 2 exhibited an obvious enrichment of pathways involved in cell cycle, DNA repair, and carcinogenic activation pathways for example WNT-β-Catenin signaling, TGF-β signaling, PI3K-AKT-MTOR signaling, MYC targets, etc. Moreover, we found out that Cluster 2 had a higher expression of immune checkpoints than Cluster 1 (Figure 3F). Our analysis also revealed that Cluster 2 had a significantly increased TIDE score and Exclusion score but decreased Dysfunction score (Figure 3H), which suggests that these patients in Cluster 2 may have an immune evasion mechanism.

Figure 3 | Clinical Characteristics Gene Sets Enrichment between clusters. (A-C) The clinical characteristics included Neoplasm Disease Stage. (A), Cancer Tumor Stage (B), and Neoplasm Histologic Grade (C) between two m⁵C clusters(Chi-square test, p< 0.01). (D, E) The top 10 significant GO analysis terms (D) and KEGG pathways (E) of DEGs in two clusters. (F). The relative expression levels of 20 immune checkpoints in two m⁵C clusters. (* p<0.05, ** p<0.01, *** p<0.001, ns: no significant difference). (G). HALLMARK pathway enrichment analysis of two m⁵C clusters by GSVA. (H). The relative distribution of TIDE was compared between two m⁵C clusters.

Construction of the prognostic risk model

We constructed a prognostic risk model to quantify the risk for each patient based on our m⁵C clustering. After univariate Cox regression, 142 DEGs with a significant prognosis at a p-value ≤ 0.0001 were analyzed by the LASSO regression algorithm in the TCGA-LIHC cohort (Figures 4A, B). In stepwise variable selection procedures, the Cox regression algorithm was used to filter the variables further. Finally, a total of 6 candidate genes including CBX2, SOCS2, LCAT, PBK, KRT17, and FTCD (Figures 4C, D) were selected to construct the prognostic risk model. The patients in the training cohort TCGA-LIHC were separated into low- and high-m⁵C score groups by the optimal cut-off value calculated by the “surv_cutpoint” function in the “survminer” R package. In the training cohort, TCGA-LIHC (Table S1), the Kaplan–Meier analysis revealed that the high-m⁵C score group had a significantly worse OS and PFS than the low-m⁵C score group (Figures 4E, G). Multivariate Cox analysis of the training cohort revealed that the m⁵C score can be an independent prognostic factor (Figure S3). The 1-, 3-, and 5-year AUC of m⁵C scores for OS was 0.781/0.762/0.711 (Figure 4F), and for PFS, the 1-, 3-, and 5-year AUC was 0.724/0.629/0.657 (Figure 4H). Likewise, in the validation cohorts ICGC-LIRI-JP and GSE14520, our risk model still had a stable prognostic capability, the high-m⁵C score patients manifested a significantly worse OS than the low-m⁵C score patients (Figure 4I, Figure S4A), and the 1-, 3-, and 5-year AUC were 0.717/0.694/0.755 and 0.619/0.622/0.627 (Figure 4J and Figure S4B).

Figure 4 | Construction of the prognostic risk model. (A, B) The least absolute shrinkage and selection operator (LASSO) regression was performed, calculating the minimum criteria. (C). Coefficients plot of six selected genes. (D). Differential expression of six selected genes in tumor and normal tissues in TCGA-LIHC cohort. (*** p<0.001). (E, F) Kaplan-Meier analysis for OS (E) and time-dependent ROC curve (F) of the risk score in the TCGA-LIHC cohort. (G, H) Kaplan-Meier analysis for DFS (G) and time-dependent ROC curve (H) of the risk score in the TCGA-LIHC cohort. (I, J) Kaplan-Meier analysis for OS (I) and time-dependent ROC curve (J) of the risk score in the ICGC cohort.

Assessment of immune microenvironment characterization with the prognostic risk model

To investigate the role of our risk model in the immune microenvironment of HCC, we looked into the correlation among the m⁵C score and expression level of immune checkpoints, immune cells infiltration, TIDE, and IPS scores in the training cohort TCGA-LIHC. The relative expression of most of the 20 immune checkpoints was significantly elevated in the high- m⁵C score group (Figure 5A). To further analyze the correlations between the immune microenvironment and our prognostic risk model, we used the CIBERSORT algorithm to calculate 22 types of immune cells, Figure 5B shows the correlation between the m⁵C score and immune infiltration cells. The m⁵C score displayed a significantly positive correlation with macrophages (M0), activated memory CD4 T cells, follicular helper T cells, and Eosinophils (p< 0.01), and a significantly negative correlation with resting memory CD4 T cells, resting NK cells, monocytes, macrophages (M2), and resting mast cells (p< 0.01). Not only CIBERSORT, but we also used TIMER and MCPcounter to analyze immune cell infiltration in high and low-risk groups, the result were displayed in Figure S5. Additionally, we also analyzed the correlation between the expression of immune checkpoints and our risk model score. Most of the immune checkpoints had a significantly positive correlation with the m⁵C score (Figure 5C). We hypothesized that the m⁵C score might be related to immune evasion mechanisms, so we compared the TIDE and IPS scores between the low- and high- m⁵C score groups. The patient in the high-m5C score group had a higher inhibitory immune checkpoints expression level, TIDE and Exclusion score, and a lower Dysfunction score (Figuress 5D–G, p< 0.001). Myeloid-derived suppressor cells (MDSC) (33) are a group of cells defined by their T cell immunosuppressive functions. We found out that the high-m⁵C score group also presented a higher MDSC score (Figure 5H, p< 0.001), which further suggests that the high-m⁵C score group may be in an immunosuppressed state. IPS was significantly elevated in the low-m⁵C score group (Figure S5, p< 0.001). These findings indirectly demonstrate that our risk model had a valuable relevance in the immune microenvironment of HCC.

Figure 5 | Assessing immune microenvironment characterization with the prognostic risk model. (A) The relative expression levels of 20 immune checkpoints in the High- and Low- m⁵C score group.(* p<0.05, ** p<0.01, *** p<0.001, ns: no significant difference). (B) The correlations between the m⁵C score and immune cell infiltration were estimated by using the CIBERSORT algorithm. (C) The correlations between the m⁵C score and immune checkpoints expression. (D) Immunoinhibitory cytokines expression between High- and Low- m5C score groups. (E–H) TIDE (E) Exclusion (F) Dysfunction (G) and MDSC score between High- and Low- m⁵C score groups. (H) Relative distribution of tumor mutation load in High- versus Low- m5C score groups. (* p<0.05, ** p<0.01, *** p<0.001, ns: no significant difference).

To further explore the correlation between TME and our risk model, we also analyzed the TME pathway and several therapeutic signatures with our m⁵C score. The result obtained shows a significant positive correlation between the m⁵C score and most TME and therapeutic pathways (Figures 6A, B). Growing evidence has displayed an association between somatic mutations in tumor genomes and response to immunotherapy (34–36). We found that though there was no notable difference in the total mutation frequency between the two m⁵C score groups, the high-m⁵C score group exhibited more TP53 mutation than the low-m⁵C score group (42% versus 15%, Fisher’s exact test, p< 0.001) (Figures 6C, D). Recent research has revealed that TMB may act as a biomarker of response to immune checkpoint inhibitors (37–40). We then compared the TMB between the two m⁵C score groups and found out that the high-m⁵C score group had a higher TMB than the low-m⁵C score group (p< 0.05) (Figure 6E).

Figure 6 | Assessing tumor microenvironment characterization with the prognostic. (A). The correlations between the m5C score and the enrichment scores of TME pathways. (B) The correlations between the m5C score and the enrichment scores of immunotherapy- predicted pathways. (C, D) The mutational landscape between High- (C) and Low- (D) m5C score groups. (E) Relative distribution of tumor mutation load in High- versus Low- m5C score groups. (* p<0.05).

Assessing the association of potential drug sensitivity with the prognostic risk model

To extend our model for clinical translation, we further explored the possible association of the m⁵C score with potential drug sensitivity. We performed analyses of GDSC2 and PRISM drug sensitivity databases in an attempt to find new potential compounds associated with the m⁵C score (Figure 7A). By correlation analysis, we screened the compounds in these two drug sensitivity databases that were negatively correlated with m⁵C scores, and the absolute value of the Pearson’s correlation coefficient obtained > 0.3. Also a significantly different AUC (Wilcoxon test, p< 0.05) was observed between the two groups of high- and low-m⁵C scores (Tables S2, S3). The top 10 associated compounds with m⁵C scores in two drug databases are presented in Figure 7B. Although these 20 candidate compounds showed increased drug sensitivity in patients with higher m⁵C scores, the above analyses alone can not lead to the conclusion that these chemicals had a therapeutic effect on HCC. Thus, we used the CMap analysis to find compounds in which gene expression patterns were oppositional to the HCC-specific expression patterns. If CMap scores< −1, it represents that these compounds might bring therapeutic benefits to HCC. Secondly, we calculated fold-change differences in the mRNA expression levels of candidates’ drug target genes between tumor and normal tissue, and a higher fold change value indicated a greater potential for a candidate agent for HCC treatment. Finally, a comprehensive literature search was performed in PubMed to find out the experimental and clinical evidence of candidate compounds for treating HCC. All results were presented in Figure 7C (Table S4). Four compounds, including Sepantronium bromide (YM-155), axitinib, vinblastine, and docetaxel, which had strong in vitro and silico evidence, were thought to be the most promising therapeutic candidates in HCC patients with high m⁵C scores. Differences in the sensitivity of these four drugs between the high- and low-m⁵C score groups were displayed in Figure 7D-G. Our results can provide some reference for further clinical translation.

Figure 7 | Assessing the association of potential drug sensitivity with the prognostic risk model. (A) Venn diagram for summarizing included compounds from GDSC and PRISM datasets. (B) Top 10 associated compounds with m⁵C score in two drug databases (GDSC2, PRISM). (C) Identification of the most promising therapeutic agents for high m5C score patients according to the evidence from multiple sources, 10 GDSC-derived agents and 10 PRISM-derived agents were shown on the left and right of the diagram, respectively. (D-G) Differential drug response AUC analysis of 4 selected compounds (Wilcoxon test, *** p< 0.001).

Validation of risk score in IMvigor210 cohort and other TCGA digestive cancers

To validate the feasibility of our prognostic risk model in immunotherapy and other GI tumors, we applied our model to three immunotherapy cohorts that included IMvigor210, Riaz, et al. Cell 2017 (GSE91061), and Lauss et al. Nat Commun 2017 (GSE100797), and the other five additional TCGA digestive cancer cohorts that also included TCGA-CHOL, TCGA-PAAD, TCGA-STAD, TCGA-COAD, and TCGA-READ. We then found out that our prognostic risk model can distinguish between the high and low-risk groups with a completely different OS or PFS in three immunotherapy cohorts, and TCGA-CHOL, and TCGA-PAAD cohort (Figures S6A–E, log-rank test, p< 0.05), but showed no significant difference in TCGA-STAD, TCGA-COAD, and TCGA-READ (Figures S6F–H) cohorts.

Constructing diagnostic models by six candidate genes

To increase the probability of early detection of HCC, we developed a tumor diagnostic model by these six genes of our prognostic risk model. 50 HCC tumor samples and 50 paired normal tissues from TGCA-LIHC as the training cohort, 247 HCC tumor samples and 241 normal tissues from GSE14520 as the validation cohort to verify the reliability of the model. The tumor diagnostic model was formulated as follows: logit (P-HCC) = 6.906 - 0.494 * SOCS2 expression level - 1.250 * LCAT expression level – 0.493 * FTCD expression level + 0.602 * KRT17 expression level + 1.950 * PBK expression level + 7.243 * CBX2 expression level. The results obtained are shown in Figures 8A–F. The AUCs of the training cohort and the validation cohort are 0.99 (Figure 8B) and 0.960 (Figure 8E), the diagnostic model achieved 94% sensitivity and 100% specificity in the training cohort (Figure 8C) and 93.9% sensitivity and 90.9% specificity in the validation cohort (Figure 8F). Meanwhile, we also constructed 2 diagnostic models of HCC tumors and liver cirrhosis. The training cohort GSE63898 contained 228 HCC samples and 168 liver cirrhosis samples, and the validation cohort GSE25097 contained 268 HCC samples and 40 liver cirrhosis samples. The tumor diagnostic model was formulated as follows: logit (P-HCC) = -2.534 – 1.240 * SOCS2 expression level - 1.320 * LCAT expression level + 0.335 * FTCD expression level – 1.911 * KRT17 expression level + 4.422 * PBK expression level + 2.006 * CBX2 expression level. The results obtained are shown in Figures 8G–L. The AUCs of the training cohort and the validation cohort are 0.973 (Figure 8H) and 0.929 (Figure 8K), the diagnostic model achieved 91.7% sensitivity and 97.0% specificity in the training cohort (Figure 8I) and 86.6% sensitivity and 92.5% specificity in the validation cohort (Figure 8L).

Figure 8 | Constructing Diagnostic Models by six candidate genes. (A-F) Diagnostic model of HCC and normal tissues.Heatmaps of six genes expression between HCC and normal tissues in the training (A) and validation (D) testing cohorts.Receiver operating characteristic (ROC) curves and the associated areas under curves (AUCs) of the diagnostic prediction models in the training (B) and validation (E) testing cohorts.Confusion matrices were built from the diagnostic model prediction in the training (C) and validation (F) testing cohorts. (G-L) Diagnostic model of HCC and cirrhosis tissues.Heatmaps of six genes expression between HCC and cirrhosis tissues in the training (G) and validation (J) cohorts.Receiver operating characteristic (ROC) curves and the associated areas under curves (AUCs) of the diagnostic prediction models in the training (H) and validation (K) cohorts. Confusion matrices were built from the diagnostic model prediction in the training (I) and validation (L) cohorts.

Validation of the prognostic risk model in Xiangya HCC cohort

In the Xiangya HCC cohort (Table S5), the prognoses were approximately the same as those seen in the training set. Survival analysis verification testified that the prognostic risk score model was a great independent prognostic factor in HCC (Figures 9A–C). The expression of 6 candidate genes in HCC and paired normal liver tissues, CBX2, PBK, and KRT17 were higher in tumors than in normal tissues. On the contrary, FTCD, LCAT, and SOCS2 were lower in tumors than in normal tissues (Figure S7, p< 0.05).

Figure 9 | Validation of the prognostic risk model in Xiangya HCC cohort. (A) Progression-free survival time in high- and low- m⁵C score groups in Xiangya HCC cohort. (B) Time-dependent ROC curve (H) of the risk score in Xiangya HCC cohort. (C) Multivariate Cox regression analysis of PFS in Xiangya HCC cohort. (D–G) The expression level of CD274 (D, E) and CTLA4 (F, G) in high- and low- m⁵C score groups in the Xiangya HCC cohort. (*p < 0.05, **p < 0.01, ***p < 0.001, ns, no significant difference).

To evaluate the predictive value of the prognostic risk score model on immunotherapy response, we selected CD274 and CTLA4, two immune checkpoints approved for clinical treatment, for further verification of the model. We collected pathological tissues from the 55 patients in the Xiangya HCC cohort. The tissues were used to make paraffin sections and for immunohistochemical staining. The results obtained show that higher CD274 and CTLA4 expression in the high prognostic risk score group compared to that of the low prognostic risk score group, a finding in line with the results of our bioinformatics analysis (Figures 9D–G). Our research provides further verifications that patients with a low prognostic risk score could benefit from immunotherapy to a higher degree than those with a high prognostic risk score. Hence, a prognostic risk score can be utilized as a potential biomarker for immunotherapy response.

Discussion

Advanced HCC has a poor prognosis. There are some reasons behind it. Firstly, the diagnosis is often delayed. Many patients are diagnosed at an advanced stage. Then, due to the tumor heterogeneity, a large proportion of patients develop varying degrees of drug resistance. Immunotherapy, while powerful, does not work for everyone (41). Therefore, it is a critical challenge to detect early-stage HCC and find the best-personalized treatment in the field.

To our knowledge, this study is the first to (I) construct one prognostic and two diagnostic models based on comprehensive 18 m⁵C regulators; (II) build an m⁵C score to predict immunotherapy response, and (III) find the potential targeted drugs for patients who are less susceptible to ICBs. The past researches on m⁵C regulators-mediated methylation modification model in HCC need to be refined. This study used 18 m⁵C regulators to construct models that better improved the predictive accuracy of early diagnostic, immune contexture, and characterizations in HCC and validated the model by Xiangya real-world cohorts and three immunotherapy cohorts, and two TCGA digestive cancers cohorts to strengthen the persuasion. More importantly, our study represents a step toward individualized chemotherapy and immunotherapy for patients with HCC and provides new potential drugs for those ICB-insensitive patients.

6 candidate genes have been reported in HCC. It was reported that CBX2 overexpression occurs in a wide range of human tumors, including lung cancer, HCC, breast cancer, and so on. Previous studies presented that it could promote HCC progression via the phosphorylation of YAP (42). SOCS2 is reported to be highly expressed in HCC, associated with the N6-methyladenosine (m⁶A) in tumor pathogenesis and progression (43). According to past studies, LCAT (44) and FTCD (45) were expressed low, while PBK and KRT17 were overexpressed in HCC (46). All these findings were consistent with our results. We applied the prognostic model to immunotherapy and other GI tumor cohorts and found out that our prognostic risk model can distinguish between high and low-risk groups with a completely distinct OS or PFS in three immunotherapy cohorts, TCGA-CHOL, and TCGA-PAAD cohorts. These results indicate that our prognostic model has wider feasibility.

Most HCCs develop in stages, starting with chronic hepatitis, cirrhosis, and dysplastic nodules (DN) and ending with HCC. Radiological and pathological exams are the most common methods for early detection of HCC. Cirrhosis and tiny nodules, however, are becoming more difficult to be characterized, according to the latest findings (47). To circumvent these limitations, molecular markers that can objectively and precisely characterize HCC need to be identified. Two diagnostic models including SOCS2, LCAT, FTCD, KRT17, PBK, and CBX2 expression were proved to accurately separate HCC from normal and liver cirrhosis samples in this study. This result shows indications that the model can enhance the early detection rate of HCC and is beneficial to early clinical management in patients with HCC, therefore improving patients’ prognosis and lowering the risk of relapse. Our findings pave the way for the use of biomarkers such as SOCS2, LCAT, FTCD, KRT17, PBK, and CBX2 in the early detection of HCC patients.

The immune microenvironment provides strategies for immunotherapy and its characteristics are closely associated with immunotherapy efficacy (48). In our study, the high-m⁵C score group has a higher immune checkpoints expression level and a higher TMB, but a probably worse prognostic in immunotherapy response. These findings project a different opinion contrary to some previous studies and they demonstrate that the analysis of immune checkpoints or TMB only is insufficient to achieve accurate outcome prediction (49), just like we don’t have to test the immune checkpoints expression level and TMB before administrating to the patients with immunotherapy in all cancers according to clinical guidelines. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm is a wildly applied computational method to comprehensively evaluate the ICB therapy efficacy in tumors. Immunophenoscore (IPS) is of significant value in predicting response to anti-CTLA-4 and anti-PD-1 regimens, by quantifying the determinants of tumor immunogenicity and depicting the intratumoral immune landscapes and cancer antigenomes. The high-m⁵C score group has a higher TIDE and Exclusion score, MDSC score and lower Dysfunction score and IPS score. We could speculate that these patients in the high-m⁵C score group may have an immune evasion mechanism. Accordingly, patients with a low m⁵C score have a greater chance to benefit from ICB therapy.

Since patients in the high-m⁵C score group are insensitive to the ICB therapy, are there any other options for them? Greater attention should be paid to patients who are less susceptible to immunotherapy. By correlation analysis, the top 10 associated compounds with m⁵C scores in two drug databases are obtained. Although these 20 candidate compounds showed increased drug sensitivity in patients with higher m⁵C scores, the above analyses alone can not lead to the conclusion that these chemicals had a therapeutic effect on HCC. Thus, we used the CMap analysis to find compounds in which gene expression patterns were oppositional to the HCC-specific expression patterns. Secondly, we calculated fold-change differences in the mRNA expression levels of candidates’ drug target genes between tumor and normal tissue. Finally, a comprehensive literature search was performed in PubMed to find out the experimental and clinical evidence of candidate compounds for treating HCC. Four compounds, including Sepantronium bromide (YM-155), axitinib, vinblastine, and docetaxel, which had strong in vitro and silico evidence, were thought to be the most promising therapeutic candidates in HCC patients with high m⁵C scores. YM-155 is a small imidazolium-based agent that works as a surviving inhibitor. In HCC cell lines, YM155 could suppress proliferation and induce cell cycle arrest and apoptosis, giving rise to DNA damage by dysregulating genes related to cell-cycle checkpoints regulation. Moreover, in a mouse model, YM155 demonstrated significantly better efficacy than sorafenib. However, its therapeutic efficacy has only been moderate so far (50). By identifying potential YM155-responsive patients, the current study shed fresh light on how to improve the therapeutic efficacy of YM-155, and therefore on how to deliver precision medicine for HCCs. Axitinib is a potent and selective vascular endothelial growth factor receptors 1-3 inhibitor that has been extensively used as the first-line treatment in several anticancer regimens. In clinical trials, it contributes to remarkable longer PFS and TTP and a higher clinical benefit rate, with acceptable adverse effects in patients with advanced HCC (51). Gemcitabine and docetaxel for HCC seem to have potential benefits, as measured by OS, but their toxicity is an unignorable issue (52). Vinblastine has been widely known as a prominent agent in cancer chemotherapy. And it was reported to sustain antitumor activity by co-targeting mTOR and the microtubule in HCC (53). However, their association with HCC progression or m⁵C modification is still unknown. In addition, the high m⁵C score group has a lower AUC, which means that this group of patients is more sensitive to the predicted drugs. Since patients in the high-m⁵C score group have a poorer prognosis and are less likely to benefit from ICB therapy. We can presume that the predicted drugs may offer new options to patients in the high m⁵C score group who are less susceptible to immunotherapy.

This study investigates the expression level of 18 m⁵C regulators and survival data from TCGA. After univariate Cox regression and LASSO regression, a total of 6 candidate genes were obtained, including CBX2, SOCS2, LCAT, PBK, KRT17, and FTCD. Using the 6 candidate genes, 2 diagnostic and a prognostic models were established and they displayed a great predictive accuracy of early diagnostic, prognostic, immune contexture, and drug sensitivity in HCC. The prognostic model showed that patients with a high m⁵C score had a poorer prognosis and shorter survival time when compared to patients with a low m⁵C score. The AUC in the training set, validation set, and real-world cohort are most above 0.7. All of these results suggest that both the prognostic model and the diagnostic model are powerful predictive factors in HCC. In addition, the high m⁵C score group has a higher (I) inhibitory immune checkpoints expression level (such as IL-10、TGF-β、IL-4, and IL-35), (II) TIDE and Exclusion score (III) MDSC score, and a lower (I) Dysfunction score and (II) IPS score, which suggests that patients with a high m⁵C score have a higher chance to experience an immune evasion state and may have a worse response to immunotherapy. For further clinical translation, potential targeted drugs for high m⁵C score samples can be designed. Our research suggests that the diagnostic model plays a significant role in early diagnosis and the m⁵C score is an independent prognostic factor for HCC patients. Furthermore, the m⁵C score can serve as a potential biomarker that helps screen the best beneficiaries of immunotherapy and helps to find potential targeted drugs.

However, there are some limitations to our study. Firstly, due to the time constraints and budget limits, our research failed to thoroughly investigate the fundamental mechanism of m⁵C regulators involved in HCC, and further experimental and clinical validations are in need to facilitate the clinical application of our findings. Secondly, we were unable to directly assess the relationship between the m⁵C score and the response of HCC patients to immunotherapy because of the lack of overall clinical information in the datasets involved. Thirdly, the sample size of the Xiangya HCC cohort should be further enlarged in the future.

Conclusion

To conclude, by integrating expression data from TCGA, GEO, and a real-world cohort, we successfully identified 6 candidate genes and constructed 2 diagnostic models that show great performance in early screening of HCC and an m⁵C score model that enable effective prediction of the prognosis of HCC patients. Patients with low m⁵C scores are more sensitive to ICBs and thus can experience a better life quality with a satisfactory prognosis. On the other hand, our study provided patients with high m⁵C scores with potential therapeutic targets and agents, which could significantly improve their prognosis. Overall, this research has shed fresh light on individualized prognostic methods as well as the integration of tailored prognosis prediction for precision medicine.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Ethics statement

The studies involving human participants were reviewed and approved by the Xiangya Hospital Medical Ethics Committee of Central South University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

WL, ZF and HS supervised the study. PL, WL, ZF and HS designed the study. ES, SZ, PL, YHL and WL drafted the manuscript. LG, YL, ZJ and WW did the statistical analysis. CG, YP, YG, YTL and QH analyzed and interpreted the data. ZZ, JM, LW, YH, XT, YC and CC collected the data and performed the major analysis and experiments. All authors contributed to the article and approved the submitted version.

Funding

This study was supported by grants from the National Key R & D Program of China (No. 2018YFC1313300), National Natural Science Foundation of China (No.: 81070362, 81172470, 81372629, 81772627, 81874073 & 81974384), key projects from the Nature Science Foundation of Hunan Province (No. 2015JC3021 & 2016JC2037), the projects from Beijing CSCO Clinical Oncology Research Foundation (No. Y-HR2019-0182, Y-2019Genecast-043).

Acknowledgments

The authors would like to give their sincere appreciation to the reviewers for their helpful comments on this article and research groups for the TCGA and CEO, which provided data for this collection, and the included patients and their family members.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2022.951529/full#supplementary-material

Supplementary Figure 1 | The CNV of m5C regulator genes in TCGA-LIHC.

Supplementary Figure 2 | TClustering of HCC based on 18 m5C regulators. (A). The consensus score matrix of all samples when k = 2. A higher consensus score between two samples indicates they are more likely to be grouped into the same cluster in different iterations. (B). The cumulative distribution functions of the consensus matrix for each k (indicated by colors). (C-D). The best number of clusters k = 2, calculated by Silhouette algorithm (C) and R package “NbClust” (D). (E). PCA plot by the expression of 18m5C regulator genes in the two m5C clusters. The blue dots represented Cluster1, and the red dots represented Cluster2. (F). tSNE plot by the expression of 18 m5C regulator genes in two m5C clusters. The blue dots represented Cluster1, and the red dots represented Cluster2. (G, H) Kaplan-Meier analysis for OS (G) and PFS (H) of two m5C clusters in the TCGA-LIHC cohort.

Supplementary Figure 3 | Multivariate Cox analysis of training cohort TCGA-LIHC.

Supplementary Figure 4 | (A, B) Kaplan-Meier analysis for OS (A) and time-dependent ROC curve (B) of the risk score in the GSE14520 cohort. (C, D) Kaplan-Meier analysis for RFS (C) and time-dependent ROC curve (D) of the risk score in the GSE14520 cohort.

Supplementary Figure 5 | Immune cell infiltration by CIBERSORT(A), TIMER(B) and MCPcounter(C) between high- and low-m⁵C score groups.

Supplementary Figure 6 | Immunophenoscore (IPS) between high- and low- m⁵C score groups.

Supplementary Figure 7 | Validation of risk score in immunotherapy cohorts and other TCGA digestive cancers. (A-H) Kaplan-Meier curves for patients with high- and low- m5C scores in three immunotherapy cohorts IMvigor210 cohort (A), Riaz et al., Cell 2017 (B), Lauss et al., Nat Commun 2017 (C), and other TCGA digestive cancer cohorts included TCGA-PAAD (D), TCGA-CHOL (E), TCGA-STAD (F), TCGA-COAD (G), and TCGA-READ (H).

Supplementary Figure 8 | Differential expression of six selected genes in HCC and adjacent non tumor liver tissues in Xiangya HCC cohort.

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ORIGINAL RESEARCH
published: 27 September 2022
doi: 10.3389/fimmu.2022.950884

CEP192 is a novel prognostic marker and correlates with the immune microenvironment in hepatocellular carcinoma

Yanli Liu ¹, Wanmei Liang ¹, Yabin Chang ¹, Zehui He ¹, Meijian Wu ¹, Haozhi Zheng ¹, Xinrong Ke ¹, Minjia Lv ¹, Qingqian Liu ¹, Qinyu Liu ¹, Waner Tang ², Qiaoling Huang ², Yu Lu ¹, Min He ¹, Qijun Yang ¹, Chunpan Mo ³, Jiefan Wang ³, Kunwei Peng ¹, Zhiqun Min ⁴, Hang Su ⁴ and Jingqi Chen ¹*

¹ Guangzhou Key Laboratory for Research and Development of Nano-Biomedical Technology for Diagnosis and Therapy and Guangdong Provincial Education Department Key Laboratory of Nano-Immunoregulation Tumour Microenvironment, Department of Oncology and Translational Medicine Center, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China, ² Department of Gynecology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China, ³ The Second Clinical Medical School, Guangzhou Medical University, Guangzhou, China, ⁴ Central Laboratory, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Daniel Patten, University of Birmingham, United Kingdom
Jianxun J. Song, Texas A&M Health Science Center, United States

*Correspondence:
Jingqi Chen
chenjingqi2002@163.com

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 23 May 2022

Accepted: 02 September 2022

Published: 27 September 2022

Citation:
Liu Y, Liang W, Chang Y, He Z, Wu M, Zheng H, Ke X, Lv M, Liu Q, Liu Q, Tang W, Huang Q, Lu Y, He M, Yang Q, Mo C, Wang J, Peng K, Min Z, Su H and Chen J (2022) CEP192 is a novel prognostic marker and correlates with the immune microenvironment in hepatocellular carcinoma. Front. Immunol. 13:950884. doi: 10.3389/fimmu.2022.950884

Hepatocellular carcinoma (HCC) responds poorly to standard chemotherapy or targeted therapy; hence, exploration for novel therapeutic targets is urgently needed. CEP192 protein is indispensable for centrosome amplification, which has been extensively characterized in both hematological malignancies and solid tumors. Here, we combined bioinformatics and experimental approaches to assess the potential of CEP192 as a prognostic and therapeutic target in HCC. CEP192 expression increased with tumor stage and was associated with poor clinicopathologic features, frequent recurrence, and higher mortality. Upon single-cell RNA sequencing, CEP192 was found to be involved in the proliferation and self-renewal of hepatic progenitor-like cells. This observation was further evidenced using CEP192 silencing, which prevented tumor cell proliferation and self-renewal by arresting cells in the G0/G1 phase of the cell cycle. Notably, CEP192 was highly correlated with multiple tumor-associated cytokine ligand–receptor axes, including IL11–IL11RA, IL6–IL6R, and IL13–IL13RA1, which could promote interactions between hepatic progenitor-like cells, PLVAP+ endothelial cells, tumor-associated macrophages, and CD4+ T cells. Consequently, CEP192 expression was closely associated with an immunosuppressive tumor microenvironment and low immunophenoscores, making it a potential predictor of response to immune checkpoint inhibitors. Taken together, our results unravel a novel onco-immunological role of CEP192 in establishing the immunosuppressive tumor microenvironment and provide a novel biomarker, as well as a potential target for therapeutic intervention of HCC.

Keywords: hepatocellular carcinoma, CEP192, cell cycle, cancer stem cell, immune suppression

Introduction

According to Global Cancer Statistics 2020 produced by the International Agency for Research on Cancer, primary liver cancer is the sixth most common incident cancer and the third leading cause of cancer-related mortality worldwide, with about 906,000 new cases and 830,000 deaths (1). Hepatocellular carcinoma (HCC), accounting for approximately 75%–85% of primary liver cancers, has an extremely poor prognosis with a mortality rate almost equal to the incidence rate and a 5-year overall survival (OS) rate below 12% (2–4). Evidence suggests that early diagnosis and prompt treatment could improve long-term outcomes; however, HCC is typically diagnosed at advanced stages where liver function is compromised and treatment options are limited (5). Thus, effective strategies for diagnosis, prognostication, and treatment of HCC remain urgently needed.

Surveillance using abdominal ultrasonography and serum alpha-fetoprotein (AFP) was recommended in high-risk populations to detect HCC early for potentially curative surgical resection or transplantation (6). However, the accuracy of surveillance is relatively low (40%–50%), and approximately half of the patients are diagnosed with advanced stages when systemic therapy remains the only standard treatment option. Notably, in the last decades, the landscape of systemic therapeutic options has considerably expanded, with the recent impressive advances in molecular-targeted therapies and immunotherapies (5). Immune checkpoint inhibitors (ICIs), with good tolerable safety profiles and durable antineoplastic effects in a broad spectrum of malignancies, have been the focus of a surge in clinical trials in combination with other immunomodulators or conventional systemic anticancer therapies, including molecular-targeted therapy or chemotherapy (7). Both preclinical studies and clinical trials have shown that antiangiogenic agents could sensitize patients to ICIs via reprogramming the tumor microenvironment, leading to the approval of bevacizumab (anti-VEGFA) plus atezolizumab or sintilimab (anti-PD-L1/PD-1) in the first-line settings, based on the superior OS and progression-free survival (PFS) versus current standard care (sorafenib) for patients with unresectable HCC (8–11). However, objective response rates (ORR) of these regimens remain low in the order of 10%–50% (7). Therefore, discovering and validating predictive biomarkers of outcome and response to immunotherapy can assist in selecting patients who may derive the greatest therapeutic benefit.

Tumor aneuploidy, alone or in combination with tumor mutation burden (TMB), is a biomarker of response to ICIs. It is noteworthy that tumor aneuploidy was positively correlated with immune evasion markers and negatively correlated with cytotoxic immune cell infiltration (12). Centrosome abnormalities and amplification are frequently observed in cancer cells and are closely linked to genomic instabilities, which may serve as a mechanism for aneuploidy development (13). A recent study suggested that targeting Polo-like kinase 4 (PLK4), a centrosome duplication regulator, could suppress tumor proliferation via inhibiting the cell cycle and eliciting anti-tumor immunity, with durable effect even in late-stage mouse HCC (14). PLK4 appears to be the center of centriole duplication, and CEP192, a centrosomal scaffold protein localized to nearly the entire length of mother and daughter centrioles, is indispensable for PLK4-mediated centriole duplication through binding and recruiting of PLK4 to centrosomes (15). In recent years, CEP192 has been identified as a new gene in the progression of non-alcoholic fatty liver disease (NAFLD) to HCC (16). Nevertheless, until recently, still only very little is known about the carcinogenic mechanism of CEP192 in tumor progression.

Here, to explore the role and underlying mechanisms of CEP192 in HCC progression, we performed large-scale bioinformatic analyses of 2,151 samples from the TCGA, ICGC, and GEO liver cancer datasets. We found that increased expression of CEP192 was associated with advanced HCC stage and poor survival, suggesting an oncogenic function of CEP192. The tumor-promoting effect of CEP192 was subsequently confirmed using the siRNA silencing experiment in vitro. Importantly, based on single-cell RNA sequencing data, we observed an abundant expression of CEP192 in hepatic progenitor-like cells, which could respond to hypoxia and secrete high levels of VEGFA. Moreover, the HPC-like cells, PLVAP+ ECs, TAMs, and CD4+ T cells of patients with high levels of CEP192 may interact through various cytokine ligand–receptor axes, including IL11–IL11RA, IL6–IL6R, and IL13-IL13RA, perhaps contributing to an immunosuppressive ecosystem. Taken together, our work reveals a novel function of CEP192 in HCC progression.

Materials and methods

mRNA expression analysis of CEP192 gene in different HCC datasets

To analyze the expression difference of CEP192 between HCC tumor tissues and adjacent non-tumor tissues, a total of 8 HCC messenger RNA (mRNA) expression datasets were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database as well as Gene Expression Omnibus (GEO) datasets (GSE14520, GSE45267, GSE121248, GSE36376, GSE76427, and GSE65372). Detailed information of these datasets is listed in Supplementary Table S1. Graphical presentation and statistical analyses of the CEP192 expression were conducted using GraphPad Prism v 8.0 (17).

Correlation analysis of CEP192 and clinicopathological characteristics

GSE89377 (including 13 paired nontumor samples, 32 chronic hepatitis/cirrhosis samples, 22 samples with dysplastic nodules, 14 HCC samples with T1 stage, and 26 HCC samples with T2 and T3 stage, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89377) was downloaded to examine CEP192 expression in preneoplastic lesions and HCC tissues. Moreover, clinical information of TCGA-HCC was downloaded to assess the correlation of CEP192 with HCC patients’ clinicopathologic characteristics (https://portal.gdc.cancer.gov/). The association between CEP192 expression and clinicopathological variables, including T stage, N stage, M stage, pathologic stage, tumor status, gender, race, age, weight, height, BMI, residual tumor, histologic grade, adjacent hepatic tissue inflammation, AFP (ng/ml), albumin (g/dl), prothrombin time, Child-Pugh grade, fibrosis Ishak score, vascular invasion, OS event, disease-free survival (DFS) event, and PFS event, was estimated via a Chi-square or a Fisher’s exact test. All variables with a p-value less than 0.05 in Chi-square or Fisher’s exact test were included in the univariate logistic regression model to infer the odds ratio for each variable. Additionally, Kaplan–Meier survival estimate and log-rank test were conducted to evaluate the impact of CEP192 on OS, DSS, and PFS.

Construction of prognostic nomogram

To provide a clinically quantitative tool for predicting the survival outcome of HCC patients, a nomogram based on clinical risk factors was necessary. Firstly, the univariate Cox proportional hazard model was used to assess the hazard ratios (HRs) of CEP192 expression and some clinicopathological variables (T stage, N stage, M stage, tumor status, pathologic stage, gender, age, histologic grade, AFP, and vascular invasion) on OS, DSS, and PFS of HCC patients. Furthermore, the variables with significance (p-value < 0.1) in univariable analysis were subjected to multivariate Cox regression analysis. Meanwhile, independent risk variables based on the univariate model were integrated to construct a nomogram. Secondly, receiver operating characteristic (ROC) curves were plotted and the areas under the ROC curves (AUCs) were also calculated using the package “timeROC” to test the specificity and sensitivity of the nomogram in predicting 1-year, 3-year, and 5-year survival probabilities. Thirdly, calibration curves of the nomogram were created to measure the agreement of the predicted and observed survival. Moreover, decision curve analysis (DCA) was carried out to evaluate the clinical performance of the prognostic nomogram.

Immunohistochemical analysis of CEP192 expression and CD8⁺ T-cell infiltration level in HCC tissues and normal tissues

Fifteen paraffin-embedded HCC tissues and adjacent non-tumor tissue were collected for immunohistochemistry (IHC) staining. Each tissue block was serially sectioned (4 μm) onto glass slides. These sections were baked (65°C, 1 h), deparaffinized in xylene, and rehydrated in a sequential ethanol gradient (from 95% to 50%). Subsequently, antigen retrieval was performed in boiling sodium citrate buffer (10 mM, pH 6.0, BOSTER, 16H10A24) for 10 min and endogenous peroxidase activity was blocked with a blocking buffer (Reagent 1, MXB Biotechnologies, KIT-9720) for 10 min, followed by nonspecial staining blocking (Reagent 2, MXB Biotechnologies, KIT-9720) for 15 min at room temperature. The primary antibody for CEP192 (1:200 dilution, #18832-1-AP; Proteintech) and CD8 (1:1,000 dilution, #66868-1-Ig, Proteintech) was incubated overnight at 4°C. After washing thrice with phosphate-buffered saline solution (PBS), biotinylated goat anti-mouse/rabbit antibody (Reagent 3, MXB Biotechnologies, KIT-9720) was added for 10 min, and then streptavidin peroxidase (Reagent 4, MXB Biotechnologies, KIT-9720) was applied for 10 min. Lastly, the cells were visualized with 3,3′-diaminobenzidine (DAB, MXB Biotechnologies, DAB-0031), counterstained with hematoxylin (abs9217, absin), dehydrated, and mounted. The IHC staining results were captured through laser microdissection microscopes (Leica, LMD6, German) and evaluated using ImageJ-IHC Profiler software (18). In brief, the staining intensity (0, negative; 1, weakly positive; 2, positive; 3, high positive) and the proportion of stained cells (0, 0%; 1, <10%; 2, 11%–50%; 3, 51%–80%; 4, >80%) were calculated and multiplied to provide an IHC score for CEP192 expression.

Cell culture and small interfering RNA transfection

The liver HCC cell lines Hep3B and SK-Hep1 (ATCC) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) at 37°C in a 5% CO₂ humidified incubator. Cells were seeded at ~40% confluency and transfected with small interfering RNA (siRNA) after 18–24 h using the riboFECT CP Transfection Kit (C10511-05, RIBOBIO Biotechnology) according to the manufacturer’s protocol. The siRNA duplexes for negative control (NC) (forward: 5′-UUCUCCGAACGUGUCACGUdTdT3′; reverse: 5′-ACGUGACACGUUCGGAGAAdTdT-3′), CEP192-siRNA#1 (forward: 5′-CCAGGAGCCUAUAGAUGAAdTdT-3′; reverse: 5′-UUCAUCUAUAGGCUCCUGGdTdT-3′), and CEP192-siRNA#2 (forward: 5′-GAUGCCAUUUGGUCACCAAdTdT-3′; reverse: 5′-UUGGUGACCAAAUGGCACdTdT-3′) were synthesized in RIBOBIO Biotechnology (Guangzhou, China).

Real-time quantitative reverse transcription polymerase chain reaction

On day 3 post-transfection, total RNA was extracted from the transfected cells using the RNAeasy™ RNA Isolation Kit (R0024, Beyotime) according to the manufacturer’s instructions. Reverse transcription was completed with RT primer mix (oligo dT primer and random 6 mers) using the PrimeScriptTM RT reagent Kit (RR047A, Takara). Quantitative real-time RT-PCR (qRT-PCR) was implemented on a QuantStudio 7 Flex system (Applied Biosystems, USA) with TB Green Premix Ex Taq (RR820A, TaKaRa, Japan). Primers designed with Primer-BLAST (National Center for Biotechnology Information, NCBI) were as follows: human CEP192, forward: 5’-CACTTGCTAGGGATAGATCCAGC-3’ and reverse: 5’-ACCCGGATGGAACTGAAAATC-3’; human GAPDH, forward: 5’-ACAACTTTGGTATCGTGGAAGG-3’ and reverse: 5’-GCCATCACGCCACAGTTTC-3’. Relative quantification was computed using the 2−ΔΔCT method. The experiments were performed in triplicate.

Western blotting

On day 3 post-transfection, total protein was extracted from the transfected cells using cell lysis buffer (P0013, Beyotime) with PMSF (ST506, Beyotime) and protease phosphatase inhibitor cocktail (P1046, Beyotime), followed by BCA assay for protein quantification (P001, Beyotime). Protein lysates in InstantView™ SEMS-PAGE protein staining and loading buffer (Beyotime, P0280) were loaded on 10% sodium dodecyl sulfate-polyacrylamide (SEMS-PAGE) gels and then transferred to polyvinylidene difluoride (PVDF) membranes (cat#1620177, Bio-Rad), blocked with 5% skimmed milk, and incubated with primary antibodies for CEP192 (1:1,000; Bethyl, A302–324A) and GAPDH (1:1,000; #8884, Cell Signaling Technology) overnight at 4°C. The membranes were washed and incubated with horse anti-rabbit IgG coupled with horseradish peroxidase (HRP) (1:10,000; #7076, Cell Signaling Technology) for 1 h, then developed with SuperSignal TM West Pico PLUS Chemiluminescent Substrate (WB322159, Thermo). Images were acquired with ChemisScope 6100 Touch (Clinx, China).

Immunofluorescence staining

The SK-Hep1 cells were grown on sterile coverslips and then fixed with 4% paraformaldehyde. After blocking with Intercept® Blocking Buffer (927-70001, LI-COR, USA), cells were incubated with primary antibodies against CEP192 (1:200, 18832-1-AP, Proteintech, China) and γ-tubulin (1:200, 66320-1-AP, Proteintech, China), followed by incubation with Alexa Fluor 488-conjugated goat anti-rabbit IgG (1: 200, 2112, EMAR, China) and CY3-conjugated goat anti-mouse IgG (1:200, S0013, Affinity, US). Finally, cells were stained with 4,6-diamidino-2-phenylindole (DAPI, 20180627, Solarbio, China), and images were captured using a laser scanning confocal microscope (LMD6, LEICA, Germany).

Cell proliferation and colony formation assays

Seven days post-transfection, cell viability was measured using MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide], which is metabolized to formazan crystals in live cells. This metabolite could be dissolved in dimethyl sulfoxide (DMSO) and measured at 570 nm in a microtiter plate reader (Berthold-LB943). Cell proliferation and self-renewal ability were further detected through colony formation assay. Briefly, after transfection with CEP192 or NC siRNAs, 300 cells were seeded into each well of a 12-well plate and incubated at 37°C for 14 days in a 5% CO₂ atmosphere. Then, colonies were fixed with 4% paraformaldehyde, stained with 0.1% crystal violet, and counted with ImageJ software.

Cell cycle analysis

After 7 days, the transfected cells were collected, washed once with PBS, and fixed with precooled 70% ethanol at 4°C overnight. Subsequently, ethanol was removed and cells were washed twice with PBS and stained with propidium iodide (PI)/RNase A buffer (ST511, Beyotime) for 30 min in the dark. Then, the cell cycle distribution was detected using flow cytometry (Quantenon 4025, Agilent).

Association analysis of CEP192 and tumor-infiltrating immune cells

The association between CEP192 expression and the abundance of tumor-infiltrating immune cells was estimated using data downloaded from Tumor and Immune System Interaction Database (TISIDB, http://cis.hku.hk/TISIDB/) (19) and Tumor IMmune Estimation Resource (TIMER, http://www.cistrome.shinyapps.io).

Correlation analysis of CEP192 and immune checkpoints

ICI therapy against CTLA-4 or PD-1/PD-L1 has achieved remarkable clinical outcomes and revolutionized cancer treatment, but not all patients respond to this therapy. Therefore, reliable biomarkers to predict response to ICIs are urgently needed. Tumor-mutation burden (TMB) and immunophenoscore (IPS) have been reported as potential predictive biomarkers in patients receiving ICIs (20). To explore whether CEP192 was a predictive biomarker for ICI response, the correlation of CEP192 with TMB and IPS score was examined using data derived from The Cancer Immunome Atlas (TCIA) (https://tcia.at/home).

Single-cell RNA sequencing analysis

Raw ScRNA-seq data from 19 liver cancer samples (GSM4050085, GSM4050086, GSM4050087, GSM4050088, GSM4050089, GSM4050090, GSM4050091, GSM4050092, GSM4050093, GSM4050094, GSM4050095, GSM4050096, GSM4050098, GSM4050100, GSM4050102, GSM4050104, GSM4050106, GSM4050108, and GSM4050110) was downloaded from Gene Expression Omnibus (GEO) (dataset GSE125449) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE125449). R package Seurat V4.0 was used to process the single-cell data expression matrix (21). At first, cells with <50 genes and >5% mitochondrial gene proportion were excluded from analysis. After normalizing the scRNA-seq data using the LogNormalize method, 1,500 highly variable genes (HVGs) were identified using the “FindVariableGenes” function. Then, dimensionality reduction was obtained using principal component analysis (PCA). Next, the K-nearest neighbor was automatically weighted with “FindNeighbors” and cells were combined in a highest resolution (resolution = 1) with “FindClusters”. Afterward, these clusters were visualized using “UMAP” and annotated according to the marker genes reported previously (22). Single-cell pseudotime trajectories of the PLVAP+ endothelial cells (PLVAP+ ECs) were reconstructed and analyzed with the package “Monocle” (23).

Statistical analysis

For significance analysis, normality and homoscedasticity assumptions are tested for each variable first. Unpaired or paired t-test was employed to assess the expression difference of CEP192 between HCC tumor tissues and adjacent non-tumor tissues. Mann–Whitney U test (Wilcoxon rank-sum test) was used to evaluate the abundance differences of tumor-infiltrated immune cells/genes between CEP192 high and CEP192 low groups. The distinct CEP192 groups were divided using the median expression of CEP192 in 836 HCC patients, which was calculated with the function “median” in R. Moreover, Chi-square or Fisher’s exact test was applied for inferring the association between CEP192 expression and clinicopathological variables. A univariate logistic regression model was also adopted to calculate the odds ratio for each variable. As for prognosis analysis, the Kaplan–Meier method was used to estimate survival probabilities and the log-rank test was conducted to determine the differences in the survival curves. Univariate or multivariate analyses with the Cox proportional hazards regression model, along with hazard ratios (HRs) and 95% confidence intervals (CIs), were performed to search for risk factors for survival. In addition, one-way or two-way ANOVA multiple comparisons were carried out to compare the differences in cell biological characteristics between the control group and the CEP192-knockdown group. Statistical significance was shown with asterisks (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001).

Results

Increased CEP192 expression associated with advanced clinicopathological characteristics in HCC patients

To evaluate the potential role of CEP192 in HCC, we first examined CEP192 mRNA expression in GEO, TCGA, and ICGC databases. As shown in Figure 1A and Figure S1A, CEP192 was significantly increased in HCC tissues compared with that in adjacent non-tumor tissues based on seven HCC datasets, namely, GSE14520 (p < 0.001), GSE45267 (p < 0.001), GSE121248 (p < 0.001), GSE36376 (p < 0.001), GSE76427 (p < 0.001), GSE65372 (p < 0.001), and ICGC (p < 0.001). Moreover, CEP192 was found to be upregulated in HCC tissues compared with that in the paired adjacent non-tumor tissues (TCGA, p < 0.001) (Figure 1B). In addition, CEP192 was increased not only in HCC but also in other types of cancer, including cholangiocarcinoma, esophageal carcinoma, head and neck squamous cell carcinoma, and lung adenocarcinoma (Figure S1B). Furthermore, 15 clinical tissues of HCC were collected to verify the protein expression levels of CEP192 using IHC analysis, and the results revealed a higher level of the CEP192 protein in HCC tissues than that in adjacent non-tumor tissues (Figure 1C). Taken together, these results indicate that CEP192 was significantly increased in HCC tissues.

Figure 1 | CEP192 was overexpressed in human HCC tissues. (A) CEP192 was highly expressed in human HCC tissues (T) versus adjacent non-tumor tissues (N) based on the RNA-seq data from the GEO database including GSE14520 (T, n = 225; N, n = 220; mean ± SEM, Mann–Whitney test), GSE45267 (T, n = 48; N, n = 39; mean ± SEM, t-test), GSE121248 (T, n = 70; N, n = 37; mean ± SEM, Mann–Whitney test), GSE36376 (T, n = 240; N, n = 193; mean ± SEM, Mann–Whitney test), GSE76427 (T, n = 115; N, n = 52; mean ± SEM, t-test), and GSE65372 (T, n = 39; N, n = 15; mean ± SEM, t-test); ***p < 0.001, ****p < 0.0001. (B) CEP192 expression was increased in 50 HCC samples compared to their matched non-tumor liver samples according to the RNA-seq data analysis of TCGA; paired t-test, ****p < 0.0001. (C) Representative IHC images demonstrated higher levels of CEP192 in HCC tumor tissues compared with adjacent non-tumor liver tissues (scale bar: 200 μm). The IHC images were quantified with an IHC score using ImageJ (n = 15); paired t-test, **p < 0.01.

The relationship between CEP192 expression and clinicopathological variables of HCC patients was further studied on 371 patients from the TCGA-LIHC dataset. Chi-square test and Fisher exact test illustrated a significant correlation of CEP192 expression levels with pathologic stage (p = 0.008), tumor status (p < 0.001), sex (p = 0.008), age (p = 0.034), AFP expression (p = 0.038), DFS event (p < 0.001), and PFS event (p = 0.007) of HCC patients (Table 1). Moreover, logistic regression (Figure 2A) and one-way ANOVA (Figures 2B–E) indicated that high CEP192 expression was notably associated with high T classification (T2/T3/T4 vs.T1, Figures 2B, C), advanced pathologic stage (II/III/IV vs. I, Figure 2D), and high AFP expression level (Figure 2E). In contrast, there was no significant relevance of CEP192 expression to precancerous lesions, such as fibrosis and cirrhosis (Table 1, Figure 2B). These results give a clue that CEP192 may be a specific biomarker for HCC. Therefore, the diagnostic ability of CEP192 in HCC patients was assessed using an AUC. As shown in Figure 2F, CEP192 had a slightly better AUC score than the gold standard diagnostic marker AFP (0.786 vs. 0.720, p < 0.05, DeLong’s test). Notably, CEP192 performed better for predicting advanced-stage HCC patients compared to those in early-stage HCC (AUC: 0.796 vs. 0.761, Figures 2G, H). These data indicated that CEP192 could be a potential diagnostic biomarker for HCC, especially for advanced-stage HCC.

Table 1 | Correlations between CEP192 expression and clinical characteristics of HCC patients.

Figure 2 | CEP192 was correlated with advanced clinicopathological features of HCC patients. (A) CEP192 was shown to be highly associated with the T stage (n = 371), pathologic stage (n = 350), tumor status (n = 355), gender (n = 374), age (n = 373), and AFP secretion level (n = 280); generalized linear model (GLM). (B) The expression levels of CEP192 in precancerous lesions including hepatitis (n = 13), cirrhosis (n = 32), and hyperplasia (n = 22), as well as in HCC tissues at different T stages (T1, n = 14; T2&T3, n = 26) (GSE89377); mean ± SEM, ordinary one-way ANOVA, *p < 0.05. (C) CEP192 gene expression (RNA-seq) of TCGA-LIHC samples at different T stages of liver cancer (Normal, n = 50; T1, n = 183; T2&T3&T4, n = 188); mean ± SEM, ordinary one-way ANOVA, **p < 0.01, ****p < 0.0001. (D) CEP192 gene expression (RNA-seq) of TCGA-LIHC samples at different pathologic stages of liver cancer (Normal, n = 50; Stage I, n = 173; Stage II&III&IV, n = 177); mean ± SEM, ordinary one-way ANOVA, **p < 0.01, ****p < 0.0001. (E) CEP192 gene expression (RNA-seq) from TCGA-LIHC samples with different AFP secretion levels [Normal, n = 50; AFP (ng/ml) ≤ 400, n = 215; AFP (ng/ml) > 400, n = 65]; mean ± SEM, ordinary one-way ANOVA, *p < 0.05, ****p < 0.0001. (F–H) Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate the sensitivity and specificity of CEP192 expression in diagnostic performance of HCC (DeLong’s test, *p = 0.049) (F), early-stage HCC (G), and late-stage HCC (H). NS, non significant.

High CEP192 expression was an independent prognostic factor for the overall survival and disease-free survival of HCC

Next, we evaluated the prognostic significance of CEP192 in HCC patients using Kaplan–Meier curves, along with the Cox proportional hazard model. Survival curves illustrated that HCC patients with higher expression of CEP192 had shorter OS, DFS, or PFS than those with lower expression of CEP192 (Figures 3A–C). Meanwhile, univariate and multivariate Cox regression analyses were carried out to determine the independent prognostic factors for poor survival in HCC patients. Through univariate Cox regression analysis, single variables including high CEP192 mRNA expression, late T stage, M stage, and advanced pathologic stage were significantly associated with poor OS (Figure 3D), DFS, and PFS of HCC patients (Table 2). Furthermore, variables with a significant difference of less than 0.1 in univariate Cox regression were included for multivariate Cox regression analysis. The result implied that CEP192 expression could serve as an independent prognostic factor for OS (Figure 3E, Table 3) and DFS in HCC patients (Table 3).

Figure 3 | CEP192 expression was correlated with poor survival outcomes in HCC patients. (A–C) Kaplan–Meier analysis for overall survival (A), disease-free survival (B), and progression-free survival (C) based on high versus low CEP192 expression in TCGA-LIHC samples; Log-rank (Mantel-Cox) test. (D) Forest plot showing the univariable Cox model results of T stage, M stage, tumor status, pathologic stage, and CEP192 expression in HCC; Cox proportional hazards regression. (E) Forest plot showing the multivariable Cox model results of the five risk factors in (D); Cox proportional hazards regression.

Table 2 | Univariate Cox proportional hazard models for overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS) in HCC patients.

Table 3 | Multivariate Cox proportional hazard models for overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS) in HCC patients.

Based on these five prognostic factors (T stage, M stage, tumor status, pathologic stage, and CEP192), we established a clinically prognostic nomogram for predicting the 1-year, 3-year, and 5-year OS in the TCGA-LIHC cohort (Figure S2A). Next, a time-dependent ROC curve was drawn and the AUC was calculated to evaluate the prognostic performance of the nomogram. The AUC scores of the 1-, 3-, and 5-year survival were 0.695, 0.747, and 0.713, respectively, suggesting a high sensitivity and specificity of this nomogram model (Figure S2B). Moreover, the discrimination accuracy of the nomogram was assessed with the concordance index (C-index, 0.667, 95% CI: 0.632–0.703), where 1.0 reflected perfect discrimination and 0.5 reflected chance alone. Furthermore, the nomogram calibration curves for 1-, 3-, and 5-year survival probabilities were very close to the ideal 45°C diagonal line, indicating that the nomogram was well calibrated, i.e., the nomogram fit the observed data well (Figure S2C). In addition, the DCA demonstrated a superior net benefit and clinical utility of the nomogram for 1-year and 3-year survival than other prognostic factors (Figures S2D, E).

CEP192 was associated with hepatic progenitor cell-driven immunosuppressive ecosystem in liver cancer

To elucidate the role of CEP192 during HCC progression, single-cell RNA sequencing (scRNA-seq) of 19 patients with liver cancer (GSE125449) was analyzed to profile CEP192 expression and the liver tumor niche (Figures 4A, B). Single-cell transcriptomes of 7,552 cells that passed quality control filtering were obtained, embedded into low-dimensional eigenvector space using PCA on the most variable genes across all cells, and then clustered cell populations with Uniform Manifold Approximation and Projection (UMAP), a recent dimensionality reduction algorithm that is popular in the scRNA-seq community. These cells were mapped to CD4+ T cells, CD8+ T cells, B cells, malignant cells, hepatic progenitor-like cells (HPC-like), endothelial cells that expressed PLVAP genes (PLVAP+ ECs, tumor-specific ECs with fetal-like property), endothelial cells that expressed ACKR1 genes (ACKR1+ ECs), cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), and TAMs that expressed FOLR2 (FOLR2+ TAMs, fetal-like TAMs with immunosuppressive property) according to cell-type-specific marker genes (Figures 4A, B). These results are remarkably consistent with previous reports (22, 24). Meanwhile, CEP192 showed a broad expression landscape in most clusters. Moreover, the exact expression level and proportion of CEP192 were visualized in Figure 4C, which indicated a greater proportion of cells expressed by CEP192 in HPC-like cells, PLVAP+ EC, CAF, and CD8+ T cells, so that the average CEP192 expression levels in these cells were higher.

Figure 4 | CEP192 was associated with hepatic progenitor cell-driven immunosuppressive ecosystem in liver cancer. (A) Dot plot depicting the typically cell-type-specific markers in liver cancer (GSE125449). (B) UMAP plot of cell types from 19 tumors (indicated by colors). Cells were annotated as CD4+ T cells, CD8+ T cells, B cells, malignant cells, hepatic progenitor-like cells (HPC-like), endothelial cells that expressed PLVAP genes (PLVAP+ ECs, tumor-specific ECs with fetal-like property), endothelial cells that expressed ACKR1 genes (ACKR1+ ECs), cancer-associated fibroblasts (CAFs), tumor-associated macrophages (TAMs), and TAMs that expressed FOLR2 (FOLR2+ TAMs, fetal-like TAMs with immunosuppressive property) according to cell-type-specific marker genes in (A). (C) The expression distribution of CEP192 in liver cancer. (D) Expression of gene sets involved in maintaining the immunosuppressive onco-fetal tumor microenvironment via the VEGFA/NOTCH signaling in liver cancer. Red boxes highlighted cell clusters that reprogrammed the immunosuppressive onco-fetal ecosystem. (E) Heatmap of positive correlation between CEP192 and liver cancer stem cell markers. (F) Sub-clustering of HPC-like cells identified 12 cell types. HPC clusters with high CEP192 expression were highlighted in red contours. (G) Dot plot of HPC cluster marker genes across HPC-like cells. Red boxes highlighted HPC clusters of high CEP192 expression. (H–J) Bubble plot showing the GO-BP enrichment results of HPC(6) (H), HPC(7) (I), and HPC(8) (J).

Previous studies have suggested that VEGF, secreted from hepatocytes in cell division, could regulate the re-emergence of fetal-like PLVAP+ endothelial cells, which, in turn, reprogramed immunosuppressive fetal-like FOLR2+ TAMs via the DLL4/NOTCH2 signaling axis, thereby maintaining an immunosuppressive onco-fetal ecosystem in HCC (24). Consistent with this phenomenon, we found that VEGFA was highly expressed in malignant/HPC-like cells, while PLVAP+ ECs notably expressed its receptor KDR (VEGFR2) (Figure 4D), suggesting that these cells may interact within the tumor microenvironment. More importantly, FOLR2+ TAMs significantly expressed typical immunosuppressive genes including TGFB1, TGBR1, IL10, and IL10RB, validating the immunosuppressive role of FOLR2+ TAMs in the HCC tumor niche (Figure 4D). In conclusion, our results suggest a nexus between malignant/HPC-like cells, onco-fetal PLVAP+ ECs, and FOLR2+ TAMs in the immunosuppressive tumor ecosystem.

Given the observed expression of CEP192 in HPC-like cells and PLVAP+ ECs, we hypothesized that CEP192 may participate in HPC-driven immunosuppressive niche in liver cancer. This concept was further supported by the positive correlation of CEP192 with characteristic onco-fetal genes, also known as cancer stem cell markers, including CD24, SOX9, CD47, and POU5F1 (OCT4) (Figure 4E). More importantly, several validated HCC-driver genes, such as YY1AP1 and ECT2, were highly expressed in HPC-like cells and markedly correlated with CEP192 expression in HCC tissues (Figure 4E). Subsequently, the HPC-like cells were classified into 12 subpopulations (Figure 4F), and CEP192 was expressed in HPC (6), HPC (7), and HPC (8) (Figure 4G). Next, GO enrichment analysis of HPC(6)-specific genes revealed a series of cellular responses to wounding, wound healing, toxic substance, and endoplasmic reticulum stress (Figure 4H). Moreover, HPC (6) weakly expressed the hepatocyte-specific gene ALB; therefore, HPC (6) may be hepatocyte precursor cells. Moreover, HPC(7) expressed relatively high levels of CEP192, VEGFA (vascular endothelial growth factor A, the central angiogenic cytokine), and the fetal-like gene CD24 (Figure 4G). Meanwhile, HPC(7) exhibited gene signatures in response to hypoxia based on GO term enrichment analysis (Figure 4I). These results suggested that HPC(7) represented a pro-angiogenic subpopulation of HPC-like cells. Additionally, GO analysis of differentially expressed genes (DEGs) in cluster HPC(8) revealed strong enrichment of functions related to cell cycle process, cell cycle phase transition, and microtubule cytoskeleton organization in mitosis (Figure 4J). This result confirmed the critical role of CEP192 in regulating the cell cycle progression of HPC-like cells. Taken together, CEP192 was expressed in hepatocyte precursor cells, pro-angiogenic VEGFA+ HPC-like cells, and proliferative MKI67+ HPC-like cells.

Next, to assess the effects of CEP192 on the functions of PLVAP+ ECs, we first divided PLVAP+ ECs into 14 subsets via dimensionality reduction and examined the expression of CEP192 in each subset (Figures 5A, B). CEP192 was highly expressed in cluster PEC(7) cells (Figure 5B). Pseudotime trajectory modeling was reconstructed using the “Monocle” R package to infer cell state transitions over time. Four main branch points dissected these trajectories into nine distinct state trajectory branches, and CEP192 was highly increased in state 3 (Figures 5C–E). Functional enrichment analysis revealed that upregulated genes in PEC(7) played roles in muscle tissue development, muscle contraction, and cellular responses to transforming growth factor beta (TGFB), mechanical, and external stimuli (Figure 5F). Consistently, the genes upregulated in state 3 were mainly involved in the development of muscle tissue/organ and migration of tissue/endothelial cells (Figure 5G). Studies have reported that endothelial cells could be transformed into smooth muscle-like cells in a transforming growth factor beta (TGFβ)-dependent manner (25). Therefore, CEP192 may play a role in the development of endothelium-derived smooth muscle-like cells.

Figure 5 | The effects of CEP192 on the function of PLVAP+ ECs. (A) UMAP was colored by 14 subpopulations of PLVAP+ ECs. (B) Expression of CEP192 and EC-specific marker genes in PLVAP+ EC subpopulations. Red boxes highlighted PLVAP+ EC clusters of high CEP192 expression. (C–E) Single-cell trajectory of pseudotime (C), state (D), and CEP192 expression (E). (F) Box plot showing the GO-BP enrichment results of PEC(7). (G) Chord diagram exhibiting the GO-BP enrichment results of PLVAP+ ECs in state 3.

Correlation analysis of CEP192 expression and tumor microenvironment in HCC

To further investigate the association between CEP192 expression and the tumor microenvironment of HCC, we calculated the immune score, stromal score, ESTIMATE score (that represented the sum of the immune score and stromal score and inferred tumor purity), and tumor purity using the ESTIMATE algorithm in 611 HCC patients downloaded from TCGA (n = 371) and ICGC (n = 240). The immune score, stromal score, and ESTIMATE score of the CEP192 high group were lower than those in the CEP192 low group (Figures 6A–C), whereas tumor purity was opposite (Figure 6D). Furthermore, we evaluated the infiltration of immune cells in the distinct CEP192 group based on the ssGSEA method and found that the vast majority of immune cells, including innate-immune cells [gamma delta T cells (Tgd), natural killer cells (NK), natural killer T cells (NKT), monocytes, myeloid-derived suppressor cells (MDSCs), macrophages, neutrophils, mast cells, dendritic cells (DCs), and eosinophils] and adaptive-immune cells [B cells, CD8+ T cells, type 1 T helper cells (Th1), and regulatory T cells (Treg)], were significantly decreased in the CEP192 high group of HCC (Figure 6E). In contrast, CD4+ T cells and type 2 T helper cells (Th2) were enriched in the CEP192 high group (Figure 6E). More importantly, patients with high CD8+ T-cell infiltration had lower CEP192 expression and better OS compared to those with low CD8+ T cell infiltration (Figures 6F, S3A). Moreover, the infiltrating CD8+ T cells in liver cancer tissues decreased with the stage (Figure S3B). Also, immunohistochemical analysis showed an increase in CEP192 expression accompanied by a decrease in the number of infiltrating CD8+ T cells in tumor tissues compared with that in non-tumor tissues (Figures 6G–I). Though not as dramatic, a tendency of negative correlation between CEP192 and CD8 was observed in the IHC staining samples (Figure 6J).

Figure 6 | Correlation analysis of CEP192 expression and immune cell infiltration in HCC. (A–D) Immune Score (A), Stromal Score (B), ESTIMATE Score (C), and Tumor Purity (D) between the CEP192 low (n = 306) and CEP192 high (n = 305) group based on HCC patients from TCGA-LIHC (n = 371) and ICGC-LIRI-JP (n = 240) datasets. Mean ± SEM, Mann–Whitney U test. (E) Distribution of 23 types of immune cells in the CEP192 low (n = 306) and high (n = 305) groups based on ssGSEA. Mean ± SEM, Mann–Whitney U test, *p < 0.05, **p < 0.01, ***p < 0.001. (F) Kaplan–Meier curve described the significant survival difference between CD8 high (n = 306) and low groups (n = 304). Log-rank (Mantel-Cox) test. (G) Immunohistochemical staining showing the level of infiltrating CD8+ T cells and CEP192 expression in HCC tumor tissues (n = 9) compared with adjacent non-tumor liver tissues (n = 9). Scale bar: 200 μm. (H) The CEP192 expression levels in (G) were quantified with an IHC score using ImageJ (n = 9); mean ± SEM, paired t-test, **p < 0.01. (I) The number of infiltrating CD8+ T cells in (G) was counted by a pathologist (n = 9); mean ± SEM, paired t-test, **p < 0.01. (J) Correlation analysis of CEP192 expression and CD8+ T-cell infiltration in (G); Spearman’s rank correlation rho. NS, non significant.

Contrary to the event of tumor-infiltrating CD8+ T cells, patients with high CD4+ T-cell infiltration had worse OS compared with those with low CD4+ T-cell infiltration (Figure S3C). Activated CD4+ T cells differentiate into different effector cells, including Th1, Th2, TFH, Th9, and Th17, based on the expression of costimulatory molecules, antigen presentation, and cytokine profiles (26). Here, the CEP192 mRNA level was increased in tumors with a high infiltrate of CD4+ T cells or Th2 cells, and decreased in tumors with a high infiltrate of Th1 cells or Tregs (Figure S3A). Although the median OS in patients with high tumor-infiltrating Tregs was shorter than that in patients with tumor-infiltrating Tregs, there was no statistically significant difference in survival outcomes between the two groups (Figure S3D). In contrast, patients with low Th1 cell infiltration exhibited worse OS and higher T classification (Figures 7A, S3E). Moreover, a high infiltration of Th2 cells correlated with decreased OS and advanced disease of patients with HCC (Figures 7B, S3F). Furthermore, survival analyses revealed a synergistic effect of CEP192 expression and Th2 cell infiltration on adverse clinical outcomes in HCC patients (Figures 7C, D). Additionally, expression levels of multiple critical immunosuppressive molecules, such as CD274, PDCD1, CTLA4, LAG3, CD244, TIGIT, ICOS, TNFSF9, TNFRSF9, TNFSF18, and TNFRSF18, were found to be higher in the Th2 high group than in the Th2 low group (Figure 7E). More importantly, CEP192 expression was also positively correlated with the expression of multiple immunosuppressive genes including CD274, PVR, ADORA2A, SIGLEC15, TNFSF4, TGFB2, TGFBR1, VEGFA, KDR, JAK1, and JAK2 (Figure 7E). Taken together, CEP192 might help establish an immunosuppressive microenvironment through interactions with Th2 cells in the HCC tumor niche.

Figure 7 | Correlation analysis of CEP192 expression with immunotherapy response and inflammatory cytokine levels. (A) Kaplan–Meier curves showing the survival difference in the patients with low versus high levels of Th1 cells (Th1 Low, n = 306; Th1 High, n = 304). Log-rank (Mantel-Cox) test. (B) Kaplan–Meier curve showing the survival difference between Th2 high (n = 304) and Th2 low groups (n = 306). Log-rank (Mantel-Cox) test. (C) Kaplan–Meier curve showing the survival difference between CP192 high (n = 119) and CP192 low (n = 186) groups in HCC patients with low infiltration of Th2 cells. Log-rank (Mantel-Cox) test. (D) Kaplan–Meier curve showing the survival difference between CP192 high (n = 186) and CP192 low (n = 119) groups in HCC patients with high infiltration of Th2 cells. Log-rank (Mantel-Cox) test. (E) Expression of 23 immune checkpoints in the Th2 high + CEP192 high (n = 186), Th2 high + CEP192 low (n = 119), Th2 low + CEP192 high (n = 119), and Th2 low + CEP192 low (n = 186) groups. Mean ± SEM, Kruskal–Wallis test, **p < 0.01, ***p < 0.001. (F) Comparison of IPS, IPS-PD1/PD-L1/PD-L2 blocker, IPS-CTLA4, and IPS-CTLA4 + PD1/PD-L1/PD-L2 blocker scores of Th2 high + CEP192 high (n = 127), Th2 high + CEP192 low (n = 71), Th2 low + CEP192 high (n = 62), and Th2 low + CEP192 low (n = 110) groups in HCC patients from TCGA-LIHC. Mean ± SEM, Kruskal–Wallis test. (G) Expression of interleukins and associated receptors in the Th2 high + CEP192 high (n = 186), Th2 high + CEP192 low (n = 119), Th2 low + CEP192 high (n = 119), and Th2 low + CEP192 low (n = 186) groups based on HCC patients from TCGA-LIHC (n = 371) and ICGC-LIRI-JP (n = 240) datasets. Mean ± SEM, Kruskal–Wallis test, *p < 0.05, **p < 0.01, ***p < 0.001. NS, non significant.

Considering the positive correlation between the immune checkpoint and CEP192, we further tested the relevance of CEP192 to ICIs. Both TMB and IPS have been proven to be effective in predicting the response of ICIs (27). Therefore, the correlation of CEP192 with TMB and IPS was conducted. There was no significant association between TMB and CEP192 level (Figure S3G). The immune checkpoint molecules programmed cell death protein 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T-lymphocyte-associated protein 4 (CTLA4) were targets of current clinically relevant immunotherapies. Thus, the scores of IPS, IPS-PD1/PD-L1/PD-L2 blocker, IPS-CTLA4, and IPS-CTLA4 + PD1/PD-L1/PD-L2 blocker were calculated to predict response to immunotherapy with CTLA-4 and PD-1 blockers. As expected, the scores of IPS-PD1/PD-L1/PD-L2 blocker, IPS-CTLA4, and IPS-CTLA4 + PD1/PD-L1/PD-L2 blocker were all lower in the CEP192 high group than in the CEP192 low group (Figure 7F), suggesting less immunogenicity on ICIs in the CEP192 high group. These data indicated that HCC tumors with low CEP192 expression were more likely to respond to PD-L1, PD-1, and CTLA4 blocker immunotherapy.

Interleukins and associated receptors are the main effectors in regulating cross-talk between immune and non-immune cells in the tumor milieu. We next explored the expression difference of these cytokines between cancer patients with low and high CEP192 expression. Some members of the IL-1 family (IL1R1), IL-2 family (IL4R, IL13RA1), IL-6 family (IL6R and IL23A), IL-10 family (IL10RB), IL-17 family (IL17RA, IL17RB, and IL17RC), and others (IL11, IL11RA) were upregulated in the CEP192 high group compared with the CEP192 low group (Figure 7G). Furthermore, scRNA-seq was used to predict the ligand–receptor interactions of interleukins among T cells, malignant cells, HPC-like cells, ECs, TAMs, and CAFs. IL11 and IL17D were highly expressed in HPC-like cells, whereas IL11RA and CD93 (receptors for IL11 and IL17D, respectively) were mainly expressed in PLVAP+ ECs. In turn, PLVAP ECs may interact with TAMs via IL6–IL6R, with CAFs via IL33–IL1RL1, and with themselves or CD8+ T cells via the IL12A–IL12R axis. Beyond, TAMs and CAFs may interact through the IL1B–IL1R1 and IL6–IL6R axes. Furthermore, CD4+ T cells may activate the immune response of TAMs through IL13–IL13R and may activate the immune response in self or CD8+ T cells through the IL23A–IL23R axis (Figure S3H). Therefore, these results suggested that CD4+ T cells, CD8+ T cells, ECs, TAMs, CAF malignant cells, and HPC-like cells may interact closely through the ligand–receptor signaling axes of interleukins to maintain an immunosuppressive microenvironment in the HCC tumor niche.

CEP192 affected HCC cell proliferation by regulating cell cycle

To gain a comprehensive insight into the biological function of CEP192 in HCC, the LinkFinder module of the LinkedOmics website was used to calculate the Spearman correlation coefficients (SCC) between CEP192 and other genes in TCGA-LIHC samples. As shown in the volcano plot (Figure 8A), 9,278 genes (red dots) correlated positively with CEP192, and 4,505 genes (blue dots) correlated negatively with CEP192. Furthermore, Gene Ontology (GO) term, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and gene set enrichment analyses (GSEAs) were performed to annotate the functions of CEP192 correlated genes with SCC > 0.5. This result revealed that CEP192 correlated genes may engage in the regulation of cell cycle progression, such as centrosome cycle, chromosome segregation, G1/S phase transition, G2/M phase transition, and metaphase/anaphase transition (Figures 8B, C, Figure S4A, B). Additionally, CEP192 was localized to the centrosome based on its colocalization with the centrosome marker, γ-tubulin, which was consistent with the previous report (Figure 8D).

Figure 8 | CEP192 was involved in cell cycle regulation. (A) Volcano plot showing the genes in the TCGA-LIHC dataset that were positively and negatively related to CEP192. (B, C) Biological processes (BP) (B) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (C) pathway enrichment analysis based on CEP192 correlated genes with Spearman correlation coefficients (SCC) > 0.5. (D) Immunofluorescence staining showing the co-localization of CEP192 with centrosome marker γ-tubulin in SK-Hep1 cells. Scale bar: 10 μm. (E) CEP192 knockdown efficiency was assessed by Western blotting in Hep3B and SK-Hep1 cell lines on day 3 after transfection with specific CEP192 siRNAs. (F) Cell proliferation ability was evaluated by MTT in Hep3B and SK-Hep1 cell lines on day 2, day 3, and day 7 from transfection; mean ± SD, SD represents three independent experiments (n = 3), two-way ANOVA, *p < 0.05, **p < 0.01, ***p < 0.001. (G, H) Cell self-renewal and colony formation ability of Hep3B and SK-Hep1 cells were determined 2 weeks after transfection with CEP192 siRNAs; mean ± SD, SD represents three independent experiments (n = 3), one-way ANOVA, **p < 0.01, ***p < 0.001, ****p < 0.001. (I) Flow cytometry was applied to detect the cell cycle distribution of Hep3B and SK-Hep1 cells; mean ± SD, SD represents three independent experiments (n = 3), two-way ANOVA, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.001. NS, non significant.

To further test the regulatory effect of CEP192 on the cell cycle, we knocked down endogenous CEP192 using CEP192-targeted siRNA sequences. The efficiency of siRNA knockdown was assessed using quantitative polymerase chain reaction (qPCR) and Western blotting (WB). Knockdown efficiency of 50% was observed at the mRNA level (Figure S4C), which corresponded with a knockdown effect at the protein level, as determined by WB (Figure 8E). We then examined the effect of CEP192 silencing on tumor cell proliferation and self-renewal using MTT and tumor colony formation assay. MTT results indicated that CEP192 silencing inhibited the proliferation of Hep3B and SK-Hep1 cells in a time-dependent manner, with ~50% inhibition efficiency at 7 days post-silencing (Figure 8F). Similarly, CEP192 silencing in Hep3B and SK-Hep1 cells markedly decreased the colony number (Figures 8G, H). More importantly, in contrast to the control clones, characterized by tightly connected cells, the CEP192-silencing clones were very loosely connected (Figure S4D). Furthermore, propidium iodide (PI) staining in combination with flow cytometry was used to determine the distribution of cells over the different phases of the cell cycle at 7 days post-silencing. Compared to cells with scramble siRNA, both Hep3B and SK-Hep1 cells with CEP192 siRNAs exhibited a marked different distribution of cell cycle, with a significant increase in the proportion of cells in the G0/G1 phase and a concomitant decrease in the proportion of cells in the G2/M phase (Figures 8I, S4E). Consistent with the previously presented KEGG, GO-BP, and GSEA, these results provided evidence that CEP192 regulated HCC cell proliferation by promoting cell cycle progression.

Discussion

CEP192 (centrosomal protein 192) was first cloned in 2000 by Nagase et al. using sequencing (28), which is composed of a PapD-like domain with two sites for serine phosphorylation and a homologous sequence to C. elegans Spd2, a protein that was considered to be indispensable for centriole duplication (29). Centrosome amplification has been extensively reported in both hematological malignancies and solid tumors (30). Moreover, CEP192 is highly expressed in the fetal liver but lowly expressed in the normal adult liver (28). Thus, an oncogenic role was proposed for CEP192 in HCC. Our study for the first time suggested that CEP192 was upregulated in HCC tissues and correlated significantly with tumor progression and adverse prognosis. Upregulation of CEP192 was further validated to be an independent prognostic factor for poor outcomes in HCC.

A small subset in the tumor population, termed cancer stem cells (CSC) or tumor-initiating cells, also known as hepatic progenitor-like cells (HPC-like cells) in liver cancer, possess stem-like properties including limitless proliferation, self-renewal, and multilineage differentiation, and it has been characterized in a variety of cancerous tissues. Accumulating evidence indicates that CSCs are emerging as critical mediators of therapy resistance including chemotherapy, targeted therapies, and immunotherapy (31). Here, based on scRNA-seq data, we found that CEP192 was notably expressed in HPC-like cells but very low in ALB+ malignant cells, an expression pattern consistent with mouse liver development. Moreover, CEP192 was also discovered to be positively correlated with cancer stem cell markers including CD24, SOX9, CD47, and POU5F1 (OCT4). Subsequently, we identified 3 hepatic progenitor-like subpopulations with high CEP192 expression in liver cancer tissues, which exhibited robust proliferative potential and response to hypoxia, wounding, wound healing, toxic substance, and endoplasmic reticulum stress. Therefore, CEP192 may be a potential biomarker for cancer stem-like cells in liver cancer.

Sharma et al. found that VEGFA secreted by proliferative hepatocytes induced the re-emergence of fetal-like PLVAP+ endothelial cells via directly regulating PLVAP expression, which, in turn, reprogrammed immunosuppressive fetal-like FOLR2+ tumor-associated macrophages (TAMs) via the DLL4/NOTCH2 signaling axis, thereby maintaining an immunosuppressive onco-fetal ecosystem in liver cancer (24). In our study, except for malignant cells (ALB+ hepatocyte), VEGFA was markedly expressed in CEP192-expressing HPC-like cells, and KDR (VEGFA receptor) was highly expressed in fetal-like PLVAP+ endothelial cells. In turn, PLVAP+ endothelial cells notably expressed DLL4, while FOLR2+ TAMs significantly expressed NOTCH2 (DLL4 receptor) as well as immune-suppressing genes (TGFB1, TGBR1, IL10, and IL10RB). Therefore, CEP192 may participate in HPC-driven immunosuppressive niche in liver cancer. Then, we evaluated the association between CEP192 expression and the tumor microenvironment in 611 HCC patients using ESTIMATE (32) and the ssGSEA algorithm (33), both of which were popular in the immune infiltration analysis community. Most immune cells were significantly decreased in the CEP192 high group than in the CEP192 low group, whereas type 2 T helper cell (Th2) and M2 macrophage were enriched in the CEP192 high group. Th2 as well as Th1, Th17, T follicular helper (TFH), and regulatory T (Treg) cells differentiate from naïve CD4+ T cells upon activation with cytokines and inducible transcription factors (34). Though there have been contradictory reports on the role of these T helper cells in cancer development, in general, Th1 exerts antitumor immunity (35), while Th2 seems to induce an immunosuppressive microenvironment (36). Here, we found that liver cancer patients with low levels of Th1 were associated with advanced disease and poor prognosis. Conversely, liver cancer patients with high levels of Th2 were correlated with advanced stage, poor prognosis, and higher expression of immune-inhibitory molecules, which confirmed the immunosuppressive role of Th2 in liver cancer.

Cytokines released into the tumor tissues were pivotal to shape the tumor milieu and facilitate tumor growth. We found that patients with high CEP192 levels showed a different cytokine profile compared with patients with high CEP192 levels. Of all cytokines increased in tumors with high CEP192 expression, IL11, a robust inducer of STAT3 activation, was reported to be increased in HCC tumors and play a critical role in postsurgical recurrence (37). We found that IL11 was specifically expressed in HPC-like cells and may interact with fetal PLVAP+ ECs and malignant cells through its receptor IL11RA. Then, the PLVAP+ ECs may express IL6 to activate TAMs, which, in turn, maintain an immunosuppressive tumor microenvironment via the IL10–IL10RB axis. IL6 and IL10 were known to be key pro-tumor cytokines that exerts multiple functions in the development of liver tumors, including promoting proliferation, EMT, angiogenesis, anti-apoptosis, and immune surveillance evasion (38, 39). IL13 is an immunomodulatory cytokine primarily produced by activated Th2 cells and may be involved in inhibiting the production of pro-inflammatory cytokines and chemokines (40). We found that IL13 was expressed in CD4+ T cells, while its receptor IL13RA1 was mainly expressed in TAMs, PLVAP+ ECs, and HPC-like cells, suggesting that CD4+ T cells may have an effect on TAMs, PLVAP+ ECs, and HPC-like cells. Referring to existing studies, our finding demonstrated close cytokine ligand–receptor interactions within the tumor niche to regulate HCC immune evasion and HCC progression, and that the expression of those cytokines was highly correlated with CEP192.

Centrosome, the best-known microtubule organizing centers (MTOCs), is recognized as the central signaling hub of the cell cycle, and CEP192 is believed to be the most critical centrosomal component in mitosis, because of its indispensable role in centriole duplication, pericentriolar material (PCM) recruitment, microtubule nucleation, centrosome maturation, and mitotic spindle assembly (41). In our research, bioinformatics prediction demonstrated that CEP192 was mainly located at the centrosome and may play a crucial role in the centrosome cycle, chromosome segregation, and cell cycle. Subsequent immunofluorescence staining validated its colocalization with the centrosome marker, γ-tubulin. Silencing CEP192 using siRNAs increased the proportion of cells in the G0/G1 phase and decreased the cell populations in the G2/M phase of the cell cycle, thereby preventing cell cycle progression and arresting tumor cell growth. This result confirmed a critical role of CEP192 in the cell cycle, which was consistent with previous reports (29, 42). Furthermore, we also observed that cep192-silenced cells exhibited elongated and loosely connected morphologies. Consistent with our study, Sharp et al. reported that depletion of CEP192 increased the cell axial ratio (length/width) and reduced cell migration (43). Possible reasons behind this proposed by Sharp et al. are a decrease in radial centrosomal microtubules that tend to maintain cell shape and an increase in directional Golgi apparatus or cytosolic microtubules that are involved in cellular elongation and polarization (43).

It is noteworthy that centrosome proteins, such as Aurora A, PLK1, and PLK4, have received substantial attention as biomarkers and drug targets. Thus, small-molecule kinase inhibitors (KIs) that target Aurora A, PLK1, and PLK4, respectively, have attracted intense interest, leading to dozens of KIs being designed, synthesized, and tested in preclinical and clinical settings, with some even being used in clinical trials (44–46). However, KIs of Aurora A, PLK1, and PLK4 as monotherapies in previous clinical trials were not satisfactory; enough evidence has suggested that KIs can significantly enhance the therapeutic efficacy of several established treatment regimens, including radiotherapies, chemotherapies, targeted therapies, and immunotherapies (45). Recently, the PLK4 inhibitor, CFI-400945, has been shown to potentially improve the efficiency of PD-1 blockade in a late-stage mouse HCC model. Notably, CFI-400945-mediated cell cycle arrest induced senescence and a senescence-associated secretory phenotype (SASP), which recruited anti-tumorigenic immune cells and converted immunologically “cold” tumor microenvironments to “inflamed” or “hot” microenvironments (14). Given the critical roles of CEP192 in the cell cycle, it is worth further exploring the impacts of CEP192 silencing on senescence and SASP factors, which may provide evidence for CEP192 as a therapeutic target to remodel the tumor microenvironment in HCC tissues.

Currently, most available KIs under clinical investigation are ATP-competitive inhibitors that target the ATP-binding pocket of the kinase domain (KD), with certain limitations, including poor specificity due to the high structural similarity among family members, high risk of certain adverse effects, and acquired resistance mutations frequently arise at the ATP-binding sites (45). Moreover, clinical trials of these KIs as single agents failed to meet therapeutic expectations, with marginal clinical efficacies in both hematologic malignancies and solid cancers (46). Consequently, short interfering RNAs (siRNA) targeting Aurora A and PLK1, respectively, have been proposed as alternative treatment options with enhanced selectivity and reduced side effects (47–50). Considering these observations, chemically modified or nanoparticle-formulated siRNA or aptamer designed to target CEP192 may be potential therapeutic strategies for tumors, especially for HCC.

In conclusion, we first uncovered a potential onco-immunological role of CEP192 in HCC. First, increased CEP192 expression in HCC was associated with tumor progression and predicted poor prognosis. Second, CEP192 was notably expressed in three hepatic progenitor-like subsets, which were characterized by robust proliferative potential and response to hypoxia, wounding, wound healing, toxic substance, and endoplasmic reticulum stress. Third, CEP192 was highly associated with a variety of pro-tumor cytokine ligand–receptor axes, including IL11–IL11RA, IL6–IL6R, and IL13–IL13RA1, which may promote interactions between HPC-like cells, PLVAP+ ECs, TAMs, and CD4+ T cells to drive immunosuppression. Accordingly, CEP192 expression was closely associated with an immunosuppressive tumor microenvironment and low IPS, making it a potential predictor of response to ICIs. Last but not least, CEP192 promoted cell proliferation and cell cycle progress by taking part in the regulation of centrosome function. Taken together, CEP192 may present a promising prognostic indicator and therapeutic target for HCC patients. However, much remains unknown about the underlying mechanisms of CEP192 to modulate the immunosuppressive microenvironment in HCC. Further research is needed to detect the tumor-infiltrating immune cell populations and cytokines in an orthotopic mouse HCC model with CEP192 inhibition, which will provide definitive evidence for the immunosuppressive role of CEP192 in HCC.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Ethics statement

This study was approved by the Academic Committee of No. 2 Affiliated Hospital, Guangzhou Medical University, Guangzhou, China, and written informed consent was obtained from all donors. The investigation was conducted according to the Declaration of Helsinki principles. In addition, all the datasets were collected from the publishing literature, so all written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

Conception and design: YLL and JC. Foundation support: YLL and JC. Acquisition and analysis of data: YLL, WL, YC, ZH, HZ, XK, MW, ML, QQL, QYL, WT, QH, YL, QY, CM, ZM, JW, and HS. Interpretation of data: YLL, KP, MH, and JC. Drafting the manuscript and revising for submission quality: YLL, XK, MW, and JC. Study supervision: JC. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by grants from National Natural Science Foundation of China, the Natural Science Foundation of China (81172537, 81272900, 81772828), Scientific and Technological Project of Guangdong Province (2016A020216028), the China Postdoctoral Science Fund (2018M643045), the Basic and Applied Basic Research Project of Guangzhou Science and Technology Bureau (202201011003), and the Ph.D. Start-up Fund of Second Affiliated Hospital of Guangzhou Medical University.

Acknowledgments

We appreciate researchers who have developed and maintained public databases such as TCGA, GEO, TISIDB, TIMER, TCIA, and LinkedOmics, which will accelerate the understanding and treatment of human cancer.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2022.950884/full#supplementary-material

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Glossary

ORIGINAL RESEARCH
published: 13 October 2022
doi: 10.3389/fimmu.2022.985911

Comprehensive characterization of extracellular matrix-related genes in PAAD identified a novel prognostic panel related to clinical outcomes and immune microenvironment: A silico analysis with in vivo and vitro validation

Xu Chen ¹,²†, Qihang Yuan ¹†, Jifeng Liu ¹†, Shilin Xia ¹,²,³, Xueying Shi ²,³, Yuxin Su ⁴, Zhizhou Wang ¹,²*, Shuang Li ¹,²,³* and Dong Shang ¹,²,³*

¹ Department of General Surgery, First Affiliated Hospital of Dalian Medical University, Dalian, China, ² Laboratory of Integrative Medicine, First Affiliated Hospital of Dalian Medical University, Dalian, China, ³ Institute (College) of Integrative Medicine, Dalian Medical University, Dalian, China, ⁴ Department of Medical Imaging, Cardiovascular Research Institute, Northern Theater Command General Hospital, Shenyang, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Yingkun Xu, Chongqing Medical University, China
Sam Senyo, Case Western Reserve University, United States
Gang Zhang, Zhejiang University, China

*Correspondence:
Dong Shang
shangdong@dmu.edu.cn
Shuang Li
lis10dmu@163.com 
Zhizhou Wang
wzz901102@126.com

†These authors have contributed equally to this work and share first authorship

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 04 July 2022

Accepted: 27 September 2022

Published: 13 October 2022

Citation:
Chen X, Yuan Q, Liu J, Xia S, Shi X, Su Y, Wang Z, Li S and Shang D (2022) Comprehensive characterization of extracellular matrix-related genes in PAAD identified a novel prognostic panel related to clinical outcomes and immune microenvironment: A silico analysis with in vivo and vitro validation. Front. Immunol. 13:985911. doi: 10.3389/fimmu.2022.985911

The extracellular matrix (ECM) is a vital component of the tumor microenvironment, which interplays with stromal and tumor cells to stimulate the capacity of cancer cells to proliferate, migrate, invade, and undergo angiogenesis. Nevertheless, the crucial functions of ECM-related genes (ECMGs) in pancreatic adenocarcinoma (PAAD) have not been systematically evaluated. Hence, a comprehensive evaluation of the ECMGs is required in pan-cancer, especially in PAAD. First, a pan-cancer overview of ECMGs was explored through the integration of expression profiles, prognostic values, mutation information, methylation levels, and pathway-regulation relationships. Seven ECMGs (i.e. LAMB3, LAMA3, ITGB6, ITGB4, ITGA2, LAMC2, and COL11A1) were identified to be hub genes of PAAD, which were obviously up-regulated in PAAD and considerably linked to tumor stage as well as prognosis. Subsequently, patients with PAAD were divided into 3 clusters premised on ECMG expression and ECM scores. Cluster 2 was the subtype with the best prognosis accompanied by the lowest ECM scores, further verifying ECM’s significant contribution to the pathophysiological processes of PAAD. Significant differences were observed for oncogene and tumor suppressor gene expression, immune microenvironment, and chemotherapy sensitivity across three ECM subtypes. After applying a variety of bioinformatics methods, a novel and robust ECM-associated mRNA-lncRNA-based prognostic panel (ECM-APP) was developed and validated for accurately predicting clinical outcomes of patients with PAAD. Patients with PAAD were randomly categorized into the train, internal validation, and external validation cohorts; meanwhile, each patient was allocated into high-risk (unfavorable prognosis) and low-risk (favorable prognosis) populations premised on the expression traits of ECM-related mRNAs and lncRNAs. The discrepancy in the tumor mutation burden and immune microenvironment might be responsible for the difference in prognoses across the high-risk and low-risk populations. Overall, our findings identified and validated seven ECMGs remarkably linked to the onset and progression of PAAD. ECM-based molecular classification and prognostic panel aid in the prognostic assessment and personalized intervention of patients with PAAD.

Keywords: pancreatic adenocarcinoma, ECM receptor interaction, molecular classification, prognosis signature, chemotherapy sensitivity

Introduction

Pancreatic adenocarcinoma (PAAD) is among the most prevalent digestive tract malignancy, which is recognized as the “king of cancers” with increasing morbidity in the whole world (1). PAAD is a deadly neoplasm and a defiant public health problem globally. As a result of the absence of obvious clinical manifestations in the preliminary phase of PAAD, 90% of patients have already advanced to metastasis during diagnosis, and as a consequence, they miss the chance for surgical therapy. At the same time, PAAD has a certain degree of resistance to radiotherapy, molecular targeted therapy, and chemotherapy which leads to PAAD being a tumor with a very high mortality rate. Despite the ongoing advancement in the early diagnosis and treatment of PAAD, the five-year survival is only around 9% for patients with this cancer (2). Therefore, developing and validating new prognostic signatures to improve the prediction of the clinical outcomes and chemotherapy sensitivity of PAAD patients is still urgently required.

The tumor microenvironment (TME) performs a critical function in the process of carcinogenesis and cancer progression. In addition, the extracellular matrix (ECM) is a vital component of the TME. The ECM not only helps cells by providing them with mechanical and physical support but also helps cells communicate with one another via the exchange of biochemical signals (3). Through interactions with stromal and tumor cells, the ECM helps cancer cells to proliferate, migrate, invade, underwent angiogenesis, and evade the immune system (4, 5). Accordingly, the ECM performs an integral function in the regulation of carcinogenesis. Excessive ECM deposition is recognized as one of the markers of cancer interaction that is associated with an unfavorable prognosis for the patient (6–9). Furthermore, the EMC supplies cancer cells with persistent proliferative signals, including chemokines and growth factors, which may protect cancer cells from growth inhibitors while also acting as a diffusion barrier for traditional anti-cancer medications (10, 11). Moreover, to the best of our knowledge, the ECM-related genes (ECMGs) have not been discovered to predicate clinical outcomes and chemotherapeutic strategies in patients with PAAD. Thus, the development of the PAAD risk stratification tool using ECMs is promising.

In this research, we comprehensively evaluated the expression levels and genomic variations of the ECMGs across different types of cancer. Through combining in silico analysis with in vitro validation, seven ECMGs (i.e. LAMA3, ITGB6, ITGB4, ITGA2, LAMC2, and COL11A1) were confirmed to be considerably up-modulated in pancreatic cancer cell lines and clinical samples and were strongly associated with the prognosis and tumor stage of the patients. Based on ECM scores and ECMG expression, we categorized PAAD patients into three distinct types and evaluated their relationships with prognosis, immune microenvironments, and drug sensitivities. We further established and verified a novel and independent prognostic panel premised on ECM-related mRNAs and lncRNAs. Overall, our new findings might lay the foundation for a deeper comprehension of the pathophysiologic mechanisms of PAAD, decision-making in the clinical setting, and individualized treatment.

Materials and methods

Pan-cancer landscape of ECM-related genes

First, 84 ECMGs were obtained based on the following dataset from the Molecular Signatures Database (MSigDB): KEGG_ECM_RECEPTOR_INTERACTION. Although many ECMGs have been explored in tumors, pan-cancer characterization of ECMGs is not well summarized. Thus, intensive exploration of the contributions of these genes in a wide range of human malignancies from the perspective of expression traits, prognostic values, methylation, mutation, and pathways would therefore be highly warranted. The CNV and SNV data that were generated from the TCGA database were processed and presented utilizing heatmaps to provide a pan-cancer overview of variations of ECMGs (12). Furthermore, a pan-cancer assessment of methylation levels and differential mRNA expression were performed. In addition to this, a univariate Cox regression analysis between mRNA expression and overall survival (OS) was carried out to determine the value of ECMGs as a prognostic marker in diverse malignancies. R and TBtools were used to perform all of these analyses similarly with our previous published articles (13).

To unveil the differential role of pathways influenced by ECMGs in human multiple cancers, single sample gene set enrichment analysis (ssGSEA) was utilized to compute pathway scores in each sample of each tumor. Gene set enrichment analysis (GSEA) was employed to examine the differences in pathway activity using the transcriptomes of two tumor groups with the bottom and top 30% of pathway scores (14).

Identification and validation of hub ECMGs related to the onset and advancement of PAAD

In this section, we devoted our attention to PAAD to conduct an in-depth and comprehensive investigation. The transcriptome data and clinical data of PAAD and normal pancreas tissues were acquired from Gene Expression Omnibus (GEO), Genotype-Tissue Expression (GTEx), International Cancer Genome Consortium (ICGC), and the Cancer Genome Atlas (TCGA) databases. After log2 transformation and batch correction, 178 PAAD and 171 normal pancreas samples in the GTEx and TCGA cohorts, 45 PAAD and 45 normal pancreas samples in the GSE28735 cohort, 69 PAAD and 61 normal pancreas samples in the GSE62452 cohort, and 82 PAAD samples in the ICGC database were obtained for further analysis.

To determine ECMGs that are expressed differentially between PAAD samples and normal pancreatic tissues, the ‘limma’ package in R was employed based on the cohorts of GSE28735, GSE62452, TCGA, and GTEx. The prognostic significance of differentially expressed ECMGs was examined utilizing univariate Cox regression analysis as well as Kaplan-Meier survival analysis. Gene Expression Profiling Interactive Analysis (GEPIA) platform was applied to evaluate the significant association of the above genes with tumor stage (15, 16). Generally speaking, the differentially expressed genes considerably linked to tumor prognosis and stage were often regarded as the hub genes of the onset and progression of tumors. Thus, we further intended to verify the results of differential expression of the above hub genes across PAAD and normal pancreas samples through immunohistochemistry (IHC) and quantitative real-time PCR (qRT-PCR).

Cell culture

The American Type Culture Collection (ATCC, Manassas, VA, USA) supplied the HPDE6-C7 normal human pancreatic ductal cell line. Our research laboratory provided the following human pancreatic cancer cell lines: PANC-1, CFPAC-1, and Bxpc-3. Cells of the HPDE6-C7, BxPC-3, and Panc-1 lines were incubated in Dulbecco’s modified Eagle’s medium (DMEM) (Gibco, USA) supplemented with 10% fetal bovine serum (FBS) (Gibco, USA) in line with the guidance provided by the ATCC. CF-PAC1 cells were incubated in Iscove’s Modified Dulbecco medium (IMDM) mixed with 10% FBS (Procell, China). All of the cell lines were grown at 37 °C in an environment that was humidified and contained 95% air and 5% CO2.

Collection of clinical samples

From the First Affiliated Hospital of Dalian Medical University, 6 pancreatic cancer tissues and 6 paired paracancerous tissues were obtained from 2022-01-01 to 2022-05-01. The tissues were derived from surgically resected specimens and rapidly froze in liquid nitrogen to prevent RNA from extracting. All patients signed informed consent forms, which were provided by the First Affiliated Hospital of Dalian Medical University. The paracancerous tissues were situated at a distance of over two centimeters from the neoplasm foci. The standard preoperative care was administered to all of the patients in the research, and neither chemotherapy nor radiation treatment was administered to them. The Ethics Committee of the First Affiliated Hospital of Dalian Medical University granted its approval to conduct this investigation.

Isolation of total RNA with quantitative real-time PCR

Using TRIzol (Accurate Biotechnology) as the extraction tool, total RNA was extracted from both primary pancreatic cancer cells and human pancreatic cancer cell lines, as per the guidelines stipulated by the manufacturer. Liquid nitrogen grinding was utilized to extract the RNA from the human pancreatic tissues. The RNA was subsequently subjected to the process of reverse transcription to generate cDNA utilizing an Evo M-MLV RT Kit with gDNA Clean (Accurate Biotechnology). The expression level of 7 ECMGs was measured by RT-PCR utilizing SYBR® Green Premix Pro Taq HS qPCR Kit (Accurate Biotechnology, Shanghai, China), with GAPDH serving as the internal reference. The ΔΔCt method was implemented to analyze and quantify the RNA level expression. The primer sequences employed were as follows, which were obtained from Sangon Biotech (Shanghai) Co., Ltd.: for human COL11A1, 5’- GTGAAGTCATTCAGCCTTTACC-3’(Forward), 5’- ATTTCTTCCATTCCATCCGAGT-3’ (Reverse); for human LAMC2, 5’- GAGGATCAAACAAAAAGCGGAT-3’(Forward), 5’- AGATTCTTCTGTGTACGCTTGA-3’(Reverse); for human LAMA3, 5’- CTGCAGTTTAAACAAACCACCT-3’(Forward), 5’- CAGCTGGTTGATACGAAAAGTC-3’ (Reverse); for human LAMB3, 5’- GAGCCTGTGACTGTGATTTCC-3’(Forward), 5’- GGTAGCGATTACAGTAGCCTC-3’(Reverse); for human ITGA2, 5’- GGGCGACGAAGTGCTACGAAAG-3’(Forward), 5’- ACCCAAGAACTGCTATGCCAAACC-3’ (Reverse); for human ITGB4, 5’- AATGGGGGCATCTGTAATGG-3’(Forward), 5’- GAGTAGTTGATCTCGCAGATGG-3’(Reverse); for human GAPDH, 5’- GGTCTCCTCTGACTTCAACA-3’ (Forward), 5’- GTGAGGGTCTCTCTCTTCCT-3’ (Reverse); for human ITGB6, 5’-ACCTGTGAAGACTGCCTGCTTATTG-3’ (Forward), 5’- ACACCTTTCGCCAACTCCAGATG-3’ (Reverse).

Immunohistochemistry, immunofluorescence, and single-cell maps

The protein expression levels of the 7 hub ECMGs were verified between cancer and normal samples from The Human Protein Atlas (HPA: https://www.proteinatlas.org/). However, we did not find the protein expression information of COL11A1 in the HPA database, therefore we displayed the protein expression levels of the other 6 hub ECMGs. In addition, the antibody staining in different types of cancer observed in current human tissue was categorized in the HPA dataset as not observed, low, medium, or high. This score was determined by taking into account both the intensity of staining and the percentage of total cells that were stained. Similarly, the HPA database was also employed to demonstrate the cellular localization (except the COL11A1) through immunofluorescence. Finally, we also analyzed the single-cell types atlas of the 7 hub ECMGs in PAAD tissues with the help of the HPA platform.

ECM scores and ECMG expression-based cluster analysis

Because of the significant differences in gene expression levels observed in the datasets that had been acquired, we created an ECM-related score classifier premised on the mRNA expression of ECMGs to demonstrate the differential levels of expression present across samples. Specifically, ssGSEA was initially undertaken to assess each patient’s ECM pathway activity. The differential analysis was carried out utilizing “gplots” package in R, and the “pheatmap” package was employed to create the heat map based on the cluster analysis outcomes. The mRNA expression levels in cancerous tissues were categorized by comparisons with the mRNA expression levels of the genes in normal samples: ECM active [cluster 1 (C1)], ECM inactive [cluster 2 (C2)], and normal ECM [cluster 3 (C3)].

We utilized the violin plot that was generated utilizing the ”ggpubr” package to represent the enrichment scores of the clusters to better demonstrate the links that exist between the gene expression levels of these three clusters. The “survival” and “survminer” packages in R were adopted to investigate the discrepancy in prognosis among these three clusters. The “pRRophetic” package in R was utilized to ascertain the half-maximal inhibitory concentration (IC50) of the drugs in the 3 clusters based on the cell expression levels identified from the GDSC(Genomics of Drug Sensitivity in Cancer) database. Of note, the value of the IC50 should be as low as possible for optimal drug sensitivity.

Effect of differentially expressed tumor suppressor genes and oncogenes in the ECM-receptor interaction

The ECM-receptor interaction performs an instrumental function in modulating the proliferation, differentiation, as well as apoptosis of tumor cells. Aberrant activation of the ECM-receptor interaction was linked to the onset and progression of distinct malignancies. Chromatin modifications, which include ubiquitination, acetylation, and histone methylation, are among the most vital epigenetic processes that modulate gene transcription. Certain classical oncogenes and histone modification-related genes (i.e. sirtuins (SIRTs) and histone deacetylases (HDACs)) in the ECM-receptor interaction might affect the modulation of the ECM-receptor interaction. In our research, the expression levels of several oncogenes were evaluated and displayed using heat maps. These levels were compared across the three clusters of the ECM-receptor interaction.

Correlation between the ECM scores and tumor immune microenvironment

Quantification of the 29 immune-related gene sets retrieved from TCGA was accomplished with the use of a ssGSEA, and the final number of genes found was 707, reflecting distinct types of immune cells, functions, and pathways (17). After that, a heat map depicting the connection between the ECMGs and infiltration level of immune cells was created with the use of R Studio’s “ggplot2” and “dplyr” packages, utilizing Spearman’s correlation coefficient as an analytical tool for analyzing statistical data. Kruskal-Wallis test was also applied to make a comparison of the discrepancy in the expression of immune checkpoints genes (ICGs) among these three clusters. In a single experiment, the gene signals expression from various immune cell populations may be analyzed utilizing ssGSEA. Depending on the findings of ssGSEA, we utilized the “ggstatsplot”, “data.table”, “dplyr”, “tidyr”, and “ggplot2” packages in RStudio to evaluate and visualize the association between the ECM score and immune substances. In the plotted figure, the degree of association is represented by the area of each sphere, and the p-value is represented by the color. Lastly, we utilized the “ggscatterstats” package to construct a scatter plot for displaying the associations between the 4 classical immune cell populations (T helper (Th) cells, regulatory T cells (Treg), neutrophils, and macrophages) and the ECM score.

Establishment and verification of a novel ECM-associated prognostic panel for PAAD patients

To clearly estimated the survival probability of PAAD patients, a novel prognostic panel was constructed and validated premised on the expression of ECM-associated mRNAs and long non-coding RNAs (lncRNAs). After retrieving the human gene transfer format (gtf) file from the Ensembl database (http://www.ensembl.org/), it was used to differentiate between protein-coding genes and lncRNAs. The ‘cor.test’ built-in function in R was employed to determine the Pearson correlation coefficients between ECM-associated mRNAs and all lncRNAs. After that, the lncRNAs that had |correlation coefficients| >0.4 and p values <0.001 were taken into consideration as ECM-associated candidates for subsequent analyses. Afterward, the expression levels of ECM-associated lncRNAs and mRNAs were integrated with clinical data from the TCGA dataset and the ICGC dataset, correspondingly. To determine whether ECM-associated mRNAs and lncRNAs have prognostic significance, a univariate Cox regression analysis was executed (filter criteria: p<0.05).

The 177 samples included in the TCGA dataset were categorized at random into two groups: the train cohort, which consisted of 89 individuals, and the test1 cohort, which consisted of 88 individuals. Furthermore, all of the samples from the TCGA dataset were allotted to the test2 cohort (n=177), and all of the samples from the ICGC cohort were allotted to the test3 cohort (n= 82).

To prevent over-fitting and identify suitable variables among the ECM-associated mRNAs and lncRNAs with prognostic significance, a least absolute shrinkage and selection operator (LASSO) regression analysis was conducted on the train cohort. After that, a multivariate Cox proportional hazards regression analysis was employed to establish an ECM-APP, and the “predict” function in R was utilized to calculate the risk score. Following the completion of the calculation of each sample’s risk score in the train cohort, 89 samples were separated into low- and high-risk populations premised on the median risk score. The PAAD patients who enrolled in the test1, test2, and test3 cohorts were subsequently categorized into low- and high-risk populations premised on the median risk score that was acquired in the train cohort.

The following investigations were carried out on the train, test1, test2, and test3 cohorts with the aim of conducting both internal and external validation of the ECM-APP (1): The ‘pheatmap’ R package was used to generate a heatmap that depicts the varying levels of gene expression that are associated with ECM-APP (2). The Kaplan-Meier method was utilized to carry out a survival analysis to determine whether or not the ECM-APP can accurately predict survival (3); The area under the curve (AUC) served as the basis for our receiver-operating characteristic (ROC) curves, which we utilized to determine the diagnostic significance of the ECM-APP (4); The ECM-APP was evaluated utilizing univariate and multivariate Cox regression analyses, correspondingly, to identify whether or not it had independent prognostic value.

Tumor mutation burden analysis

The aggregate number of somatic gene insertion errors, deletion errors, gene coding errors, and base substitutions identified per million bases were described as the TMB. The “perl” programming language was utilized to count the number of non-synonymous mutations. Then, the differences in TMB across the high- and low-risk populations were computed with P <0.05 signifying the criteria for statistical significance. The PAAD driver genes were then identified utilizing the R package “maftools,” and the state of the topmost 20 genes exhibiting the greatest mutation frequency in the low- and high-risk populations was investigated further. We also explored the discrepancy in the clinical outcomes between different TMB scores and ECM-APP risk scores through K-M log-rank test.

Analysis of the tumor immune microenvironment in low - and high-risk population

To get a deeper comprehension of the possible processes behind the different prognoses across high- and low-risk subgroups, we intensively studied the discrepancies in immunocyte infiltration, immune function response, and immune checkpoint expression based on a variety of bioinformatics algorithms.

The TIMER2.0 database (http://timer.cistrome.org/) offers a complete immunological signature of tumor-infiltrating cells in a wide variety of tumor samples taken from the TCGA database. This signature is premised on the algorithms of QUANTISEQ, MCPCOUNTER, EPIC, XCELL, CIBERSORT, and TIMER. The “pheatmap” package in R was used to illustrate the infiltration levels of a variety of immune cells within each sample belonging to the train, test1, and test2 cohorts. After that, the “limma” package in R was adopted to determine the statistical differences in infiltration levels of immune cells between low- and high-risk subgroups. The heatmap revealed only immune cells that had significant variation (p < 0.05) In addition, the “gsva” package in R was utilized in conducting ssGSEA to discover the infiltration levels of immunocytes and immune-associated functions that were different between the high-risk and low-risk subgroups. Ultimately, only the statistically significant findings were displayed after investigating the differential expression of common ICGs in high- and low-risk subgroups (filter criteria: p < 0.05).

Results

Pan-cancer landscape of ECMGs

To begin, we created a corresponding flowchart (Figure 1) to depict the workflow of this study project more vividly. To examine the genetic abnormalities of ECMGs in cancer, the proportion of CNV was analyzed, and the findings revealed that in general CNV occurred at high frequencies (almost ranging from 10% to 40%) in most cancer types (Figure 2A). We discovered that UCEC, SKCM, STAD, COAD, and LUSC, as well as LUAD, all had a greater frequency of SNVs. On the other hand, the frequency of SNVs found in UCS, OV, SARC, PAAD, and KIRC was low (Figure 2B).

Figure 1 | The current study’s flowchart.

Figure 2 | The pan-cancer landscape of ECM receptor interaction-related genes. (A) CNV gain and loss frequency data across cancer types. The length of the line represents the variation frequency of the ECM-related genes in pan-cancers. (B) SNV data across cancer types. The mutant frequencies of ECM-related genes in pan-cancers (red color to green color represents low to high). (C) Gene expressions of the ECM-associated genes between tumor and neighboring normal tissues in pan-cancers (P<0.05). (D) Survival landscape of the ECM-related genes among all the types of cancers. The white color represents genes with P > 0.05, and the blue and yellow colors represent risk and protective genes, respectively. (E) The DNA methylation of ECMGs in different tumors (red color to blue color signifies high to low). (F) The relationship between cancer signaling and ECMGs (red color to blue color signifies high to low).

To examine the mRNA expression status of ECMGs between tumor and neighboring normal samples in various cancers, differential expression analysis was performed (Figure 2C). The levels of COL5A2, IBSP, COL4A1, AGRN, SPP1, COL11A1, and HMMR in nearly all tumor tissues were significantly elevated compared with those in paired normal tissues; on the contrary, the levels of RELN, ITGA8, TNXB, LAMA2, SV2B, and COL6A6 in nearly all tumor tissues were significantly decreased compared with those in paired normal tissues. Subsequently, we then intensively investigated the prognostic performances of these ECMGs in pan-cancers by analyzing gene expression and patients’ survival time. As illustrated in Figure 2D, we executed a univariate cox regression analysis to discover the risky ECMGs (HR>1 and p<0.05) and protective ECMGs (HR<1 and p<0.05). Interestingly, we found that most ECMGs are present as risk factors in most tumors including PAAD; however, part of ECMGs function as protective factors in KIRC and SKCM.

In addition to CNV, methylation of promoters may influence gene expression, and abnormal DNA methylation of promoters is linked to the onset of tumors. In each of the 20 different types of cancer, we found that ECMGs had complicated methylation patterns. LAMA1, CHAD, ITGA8, and COL11A1 consistently showed hypermethylation in more than half of cancer types; on the contrary, TNN, SPP1, COL6A6, and TNR consistently showed hypomethylation in more than half of types of cancer (Figure 2E). Additionally, the links between cancer hallmarks and ECMGs were evaluated, and the findings revealed that more than half of the hallmarks were frequently remarkably linked to ECMGs (Figure 2F).

Identification and validation of hub ECMGs linked to the onset and progression of PAAD

A total of 13 differentially expressed ECMGs between PAAD samples and adjacent/normal pancreas tissues were identified after the intersection of three cohorts’ results (i.e. GSE28735, GSE62452, and TCGA-GTEx) (Figure 3A). Consequently, the prognostic values of 13 differentially expressed ECMGs in PAAD were explored by combining Kaplan–Meier survival analysis and univariate Cox regression analysis. As depicted in Figure 3B, the findings of univariate Cox regression analysis illustrated that 10 genes (i.e. COL5A2, ITGA3, LAMA3, ITGB6, FN1, ITGB4, ITGA2, LAMC2, COL11A1, and LAMB3) of 13 differentially expressed ECMGs were detected to be significantly associated with PAAD prognosis. Meanwhile, Kaplan–Meier survival analysis demonstrated that 9 genes (i.e. ITGB6, CD36, ITGA3, LAMC2, ITGA2, LAMA3, ITGB4, COL11A1, and LAMB3) of 13 differentially expressed ECMGs were discovered to be remarkably linked to PAAD prognosis (Figures 3C–O). Based on the intersection of results of univariate and Kaplan–Meier survival analyses, we obtained 8 ECMGs truly associated with PAAD prognosis (i.e. ITGB6, ITGA3, LAMC2, ITGA2, LAMA3, ITGB4, COL11A1, and LAMB3). Furthermore, we evaluated the possible link between the ECMGs and the tumor stage of PAAD, and 8 ECMGs (i.e. FN1, LAMA3, ITGB6, ITGB4, ITGA2, LAMC2, COL11A1, LAMB3) were detected to be significantly associated with tumor stage (Figures 3P–Y). Overall, we identified 7 hub ECMGs (i.e., LAMA3, ITGB6, ITGB4, ITGA2, LAMC2, COL11A1, LAMB3), which were not only differentially expressed in PAAD and adjoining/normal pancreas tissues but also remarkably linked to the prognosis and stage of PAAD.

Figure 3 | Determination of differentially expressed ECM-related genes, their prognostic significance, and their relationship with the tumor stage of PAAD. (A) A Venn diagram revealed 13 ECM-related genes that were differently expressed between PAAD and normal pancreatic tissues. (B) The analysis illustrated the prognostic significance of 13 differentially expressed ECM-related genes in PAAD. (C–O) Survival analysis of 13 ECM-related genes. (P–Y) The relationship between 10 ECM-related genes and the tumor stage of PAAD.

Following these, the expression profiles of 7 hub ECMGs were validated by RT-PCR and IHC in cell lines and clinical samples of PAAD (Figures 4A–C). We found the mRNA levels of LAMA3, ITGB6, ITGB4, ITGA2, LAMC2, COL11A1, and LAMB3 were higher in at least one of the pancreatic cell lines than that in normal cell lines cells (Figure 4A). Meanwhile, we also explored the expression levels of the 7 hub ECMGs in our clinical samples, and the results showed that the 7 hub ECMGs were expressed at high levels in tumors in contrast with the adjoining non-tumor tissues (Figure 4B). To analyze the protein expression of 7 hubs ECMGs, we intensively studied the immunohistochemistry findings of tumor and normal pancreas tissues using the HPA database (Figure 4C). We did not find the protein expression information of COL11A1 in the HPA database, therefore, the other 6 hub ECMGs proteins available were performed, which is nearly consistent with the results of the bioinformatic analysis and qRT-PCR. These protein expression levels became markedly up-modulated in PAAD as opposed to normal pancreas tissues. Similarly, we explored the cellular localization of the 6 hub ECMGs (except the COL11A1), the expression product of LAMC2, LAMA3, LAMB3, ITGA2, ITGB4, and ITGB6 were mainly located on the endoplasmic reticulum and Golgi apparatus, endoplasmic reticulum, vesicles, nucleoplasm, plasma membrane, and cell Junctions, and cell Junctions, respectively (Figure 4C). By the HPA database, we analyze the single-cell types atlas for each of the 7 hub ECMGs (Supplementary Figure S1). We found that COL11A1 is mainly distributed in pancreatic smooth muscle cells, while other genes are mainly distributed in pancreatic endocrine cells.

Figure 4 | Validation of the expression traits of seven hub ECMGs. (A) RT-PCR of cell lines (H6C7: cell lines of human pancreatic duct epithelial; BxPC-3/CF-PAC1/Pan-1: cell lines of pancreatic cancer). (B) RT-PCR of clinical samples (tumor tissues vs normal adjacent tissue). (C) Immunohistochemistry and immunofluorescence of clinical samples (tumor tissues vs normal adjacent tissue). (* indicates p <0.05; ** indicates p < 0.01; *** indicates p < 0.001; **** indicates p < 0.0001; ns, non-significant

Cluster analysis premised on the ECM scores

To delve more into the link between ECMGs and PAAD, we classified 259 samples of PAAD into three distinct groups depending on the levels of mRNA expression of ECMGs (Figure 5A). C1 contained tumor patients with active ECM, C2 contained those with inactive ECM, and C3 contained those with normal ECM. The enrichment scores for the 3 clusters were orderly arranged as C1 > C3 > C2 according to the violin plot (Figure 5B). Then, we assessed whether or not the clustering was plausible by generating the survival curves for each of the 3 clusters. Patients diagnosed with PAAD who were allocated to the C2 cohort exhibited remarkably higher OS rates as opposed to those allocated to the C1 and C3 clusters (Figure 5C), suggesting that the ECM score was an indicator that should be considered risky.

Figure 5 | ECM score-based clustering analysis. (A) Clustering of gene data illustrates 3 clusters displayed by the heat map: ECM active [cluster 1 (C1)], ECM inactive [cluster 2 (C2)], and normal ECM [cluster 3 (C3)]. The proportion of patients with upmodulated genes is shown on the right side of the figure. In the color bar on the right side, red denotes mRNA up-modulation, blue signifies mRNA downmodulation, and white denotes mRNA no-regulation. The ECM score is displayed in four different colors, with negative values shown in blue and positive ones shown in red. (B) The “ggpubr” package’s violin plot indicates that the enrichment scores of the three clusters, from high to low, are C2, C3, and C1. The “kruskal.test” was employed in this study, and the p-values are presented above the clusters. (C) A survival curve depicting the three different clusters. Cluster 2 has a better survival rate compared to either cluster 1 or cluster 3. Cluster 1 is denoted by the red line, cluster 2 is denoted by the green line, and cluster 3 is denoted by the black line. The number of years is signified by the abscissa, while the rate of survival is denoted by the ordinate.

Link between drug sensitivity and ECM clusters

Targeted molecular medicines are now the primary treatment option for individuals with advanced PAAD who have lost the chance to undergo surgical resection. The molecular biology of cancer serves as the foundation for molecular targeted treatment, which treats cancer by setting tumor-associated molecules as therapeutic targets and using medications and agents that are highly specific to those molecules. As described previously, the ECM is a significant contributor to the onset and progression of malignancies. Our findings also suggested that the ECMGs might have a risky function in PAAD. Is there any connection between the ECMGs and the currently available targeted medications that can efficiently treat PAAD? To obtain greater insights into this topic, we conducted further analysis on the theoretical grounds and instructions provided for PAAD to investigate the connection between ECMGs and the IC50 values of 12 targeted treatments and traditional drugs that are often used in tumor research [35,36]. The 12 drugs we selected included the use of tyrosine kinase inhibitor(e.g., saracatinib, sunitinib), mTOR inhibitor, poly ADP-ribose polymerase inhibitor(e.g., rucaparib, olaparib), plant anticancer drugs(e.g., camptothecin, docetaxel, cisplatin, vinblastine, vinorelbine), the first-line chemotherapeutic drugs, pyrimidine antineoplastic drugs(e.g., gemcitabine), and Bcl-2 inhibitor(e.g., obatoclax. Mesylate). With the help of the “pRRophetic” package, we predicted the IC50 values of above 12 commonly targeted drugs among these three molecular subtypes. A lower value for IC50 is an indication of increased sensitivity to the medication. Following is a list of the various medication sensitivities that were recorded among the ECM clusters based on the research results: AZD0530(saracatinib): C1 > C2 > C3; sunitinib: C2 > C3; AZD8055(mTOR inhibitor): C2 > C1 > C3; AG.014699(rucaparib): C1 > C2 > C3; AZD.2281(olaparib): C2 > C3; camptothecin: C2 > C3; docetaxel: C1 > C2 > C3; cisplatin: C2 > C3; vinblastine: C1 > C2 > C3; vinorelbine: C2 > C3; gemcitabine: C2 > C1 > C3; obatoclax.Mesylate: C2 > C3 (Figures 6A–L).

Figure 6 | The link between ECM clusters and drug sensitivity. The estimated IC50 box plots for 12 kinds of commonly used chemotherapeutic drugs are conducted in (A-L) for cluster 1 (yellow), cluster 2 (blue), and cluster 3 (red). The 12 types of chemotherapeutic agents are AZD0530 (saracatinib), sunitinib, AZD8055 (mTOR inhibitor), AG.014699 (rucaparib), AZD.2281(olaparib), camptothecin, docetaxel, cisplatin, vinblastine, vinorelbine, gemcitabine, obatoclax.Mesylate. The P-values are depicted above the clusters and the P < 0.05 is deemed as having statistical significance.

Correlation of ECM scores with potentially targetable classical genes, HDAC family genes, and sirtuin family genes

Heat maps relating to oncogenes in the three ECM clusters were produced to subsequently examine the differential expression profiles of the oncogenes in each of the clusters (Figure 7A). Interestingly, we discovered that the levels of expression of the oncogenes MTOR, KRAS, MYC, CCND1, and PIK3CA were much lower in C2 in comparison to C1 and C3. The inhibition of oncogenes probably accounts for the better prognosis of C2 subgroups. In addition, the expression levels of commom tumor suppressor genes including TP53 and PTEN were lowered in C2 but elevated in C1 and C3, similar to the expression levels of the oncogenes.

Figure 7 | Correlation of ECM scores with Classical Oncogenes, Tumor Suppressor Genes, and Immune Cell Infiltration. (A) Different levels of expression of distinct tumor suppressor genes and oncogenes in 3 ECM clusters. (B) The different levels of HDAC Family Genes expression among three ECM clusters. (C) The different levels of Sirtuin Family Genes expression across the 3 ECM clusters. (D) The heat map depicts the relationship between ECM-associated genes and infiltration levels of immune cells. A positive association is denoted by red, while a negative association is denoted by blue. (E) The different expression levels of immune checkpoints among three ECM clusters. (F) The plot depicts the link between the ECM score and infiltration levels of immune cells. The size of the sphere on the right side of the figure denotes the degree of abs (correlation), while the color denotes the p-value. (G–J) The scatter plot depicts the relationship of ECM score with 4 immune-infiltration-related substances. The ECM score was shown to have a positive link to the infiltration levels of neutrophils, macrophages, CCR, Treg cells, and so on. (* indicates p <0.05; ** indicates p < 0.01; *** indicates p < 0.001; **** indicates p < 0.0001).

Gene transcription mediation is greatly influenced by the activity of enzymes known as histone deacetylases. These enzymes are responsible for catalyzing the elimination of acetyl groups from histone and non-histone lysine residues and are intimately linked to tumor onset and metastasis (18). Recently, suppression of HDAC has emerged as a method that has been verified in clinical practice for treating cancer (19). The levels of expression of HDAC1, HDAC7, and HDAC9 were found to be considerably lowered in C2 contrasted with the levels observed in C1 (Figure 7B). In addition, the levels of HDAC5 and HDAC10 expression were remarkably elevated in C2 (Figure 7B). These findings revealed that various members of the HDAC family had varied functions in the PAAD and may provide novel avenues for the future targeted therapy of malignancies.

Histone deacetylation enzymes (HDACs) of classes I and II are not the only types of HDACs and there is another particular class of HDACs (class III HDAC, Sirtuin) (20). Recently, a vast growing body of research indicates that SIRTs engaged in a wide variety of biological activities that are relevant to the onset and advancement of tumors, including variations in both the tumor-associated metabolic pathways and TME and uncontrolled proliferation of cells (21, 22). SIRTs are considered to perform a complicated function as oncogenes or tumor inhibitors in a variety of cancers under a variety of experimental settings. In this research, SIRT3 and SIRT6 were highly expressed in the C2 subgroup; however, SIRT1 and SIRT5 had low expression in the C2 subgroup (Figure 7C). Accumulating evidence has suggested that SIRT3 and SIRT6 could be served as the tumor suppressors, thus, the abnormal sirtuin status might be also responsible for the different clinical outcomes among three ECM-based clusters (22).

Correlations between the ECM scores and the immune cell infiltration

The TME is a dynamic environment that involves two-way interaction between stromal and tumor cells and is characterized by the presence of complex networks (23, 24). During the process of tumor cell development, immune evasion takes place, which later develops into metastases, and the ECM offers the molecular and cellular milieu that is essential for this dynamic mechanism. Immune cells, which are an essential component of the TME, are intimately linked to the clinical manifestation of neoplasms and hence have the potential to serve as effective therapeutic targets for cancer (25). Furthermore, the ECM is very crucial in the process of modulating the immunological microenvironment of tumors (26, 27). In this research, we further examined the link between ECMGs and infiltration levels of immune cells and immune-associated roles (Figure 7D). As per the findings, the vast majority of genes have a positive association with the vast majority of immune-infiltrating agents, including ITGA4. In contrast, some genes, such as LAMA5, ITGB4, GP6, AGRN, ITGA3, and SDC1, are inversely linked to the infiltration levels of immune cells. It has been well-established that ICGs have an instrumental function in modulating the functionality of effector T cells and, as a consequence, anti-tumor immunity. Our findings reveal that ECM-inactive subgroups are accompanied by a lower expression level of ICGs, indicating ECM-inactive subgroups had a stronger anti-tumor immune response and a better prognosis (Figure 7E). Following these, we additionally examined whether or not there was a connection between the ECM score and infiltration levels of immune cells (Figures 7F–J). Consequently, it was shown to have a favorable link to the infiltration levels of most immune cells, including Th cells, Treg cells, neutrophils, and macrophages.

ECM-associated prognostic panel enables the auxiliary prediction of clinical outcomes of patients with PAAD

In addition to well-recognized ECM-related mRNAs, multiple lncRNAs also had the potential to react with ECM and then influence proliferation, apoptosis, metastasis, and invasion of various tumor cells. To construct a comprehensive and robust ECM-APP, we first identify 278 ECM-related lncRNAs with the help of the Pearson correlation test (Supplementary Figure S2A). Subsequently, we integrated the expression profiles of 83 ECM-related mRNAs and 278 ECM-related lncRNAs and rated them as candidate molecules used for the panel development. As per the findings of a univariate Cox regression analysis, 149 out of 361 candidate molecules served as possible prognostic markers (Supplementary Figure S2B). After this, a LASSO regression analysis was carried out to get rid of any collinearity that existed across the 149 genes and to avoid the overfitting of the prognostic model (Supplementary Figure S2C). 13 of 149 candidate molecules were preserved for additional multivariate Cox regression analysis (Supplementary Figure S2D). Lastly, multivariate Cox proportional hazards regression analysis integrating nine genes (i.e., PTOV1−AS2, RPARP−AS1, CASC8, UNC5B−AS1, ITGA7, AC136475.3, AC027288.3, LINC01116, UCA1) was performed to establish a comprehensive and robust ECM-APP (Supplementary Figure S2E).

Subsequently, in the train cohort, we separated 89 PAAD patients into low- and high-risk subgroups premised on the median risk score of 1.092 (Figure 8A). Predicated on the risk score distributions and the survival status, the high-risk subgroup of PAAD patients had a significantly higher death rate (Figure 8B). The heatmap depicted the expression distributions of 9 ECMGs involved in the prognostic panel (Figure 8C). Then, the findings of the Kaplan-Meier survival analysis highlighted that PAAD patients had a worse OS in the high-risk subgroup in contrast with those in the low-risk subgroup, implying that the ECM-APP could accurately distinguish PAAD patients with poor or good prognosis (Figure 8D). To confirm the effectiveness and accuracy of ECM-APP, the diagnostic performance of the risk score was evaluated utilizing the ROC analysis. The AUCs of the ROC curves had values of 0.834, 0.848, and 0.860, correspondingly, for 1-, 3-, and 5- year survival (Figure 8E). Considering that the c-index for the train set was 0.847, it is reasonable to infer that the ECM-APP had an outstanding prognostic ability. Finally, using the clinical follow-up data acquired from TCGA, we conducted univariate and multivariate Cox regression analyses. The findings illustrated that the risk score independently functioned as a prognostic index (HR = 1.068, 95% CI =1.040−1.096, p < 0.001 in univariate analysis; HR =1.064, 95%CI = 1.036−1.094, p < 0.001 in multivariate analysis) (Figures 8F, G). It is worth noting that stage, grade, gender, and age, were not the independent prognostic markers for PAAD patients, which illustrated the integrity and accuracy of our model.

Figure 8 | Construction of a novel ECM-APP in the train cohort. (A) Sectionalization premised on the median risk score. (B) Survival status and risk score distributions (C) Heatmap depicted the expression levels of 9 genes integrated into the prognostic signature. (D) Survival curve of OS time in low-risk and high-risk subgroups. (E) ROC curve of 1-, 2-, and 3-years of train cohort, area under the curve (AUC) of the curve are 0.834, 0.848, and 0.860, correspondingly. (F) Univariate cox regression analysis illustrated the independence of risk score, gender, grade, stage, and age. (G) Multivariate cox regression analysis showed the independence of risk score, stage, grade, gender, and age.

For validation of the reliability of the ECM-APP, PAAD samples in the test1 cohort, test2 cohort, and test3 cohort were also distinguished into low- and high-risk populations with a similar method (Figures 9A, 10A, 11A). Notably, when differentiating across the samples, the median risk score of 1.092 in the train cohort functioned as a unified benchmark. The risk score distributions, as well as survival status in the three cohorts, revealed similar tendencies to those reported in the train cohort in both the internal validation cohorts (test1 and test2 cohorts) and the external validation cohort (test3 cohort) (Figures 9B, 10B, 11B). Additionally, the heatmaps from the test1, test2, and test3 cohorts exhibited a similar expression pattern of the 9 ECMGs to that in the train cohort (Figures 9C, 10C, 11C). More importantly, Both the internal and the external validation results showed that patients diagnosed with PAAD who had high risk scores exhibited dismal OS rates (all p < 0.05) (Figures 9D, 10D, 11D). We discovered that in the test1 cohort, the AUC values of the ROC curves were 0.702, 0.688, and 0.705 while in the test2 cohort, they were 0.771, 0.775, and 0.818 for 1-, 3-, and 5-year survival, correspondingly. Moreover, the AUC values in the external validation were 0.730, 0.707, and 0.701 in the test3 dataset (Figures 9E, 10E, 11E). In the test1, test2, and test3 datasets, the c-index was computed to be 0.698, 0.788, and 0.713, correspondingly. Lastly, due to the deficiency of clinical information in ICGC, our novel ECM-APP was also proved to be an independent prognostic indicator in test1and test2 cohorts (Figures 9F, G, 10F, G).

Figure 9 | Internal validation of the robust ECM-APP in the test1 cohort. (A) Sectionalization premised on the median risk score. (B) Survival status and risk score distributions (C) Heatmap depicted the expression profiles of 9 genes integrated into the prognostic signature. (D) Survival curve of OS time in low-risk and high-risk subgroups. (E) ROC curve of 1-, 2-, and 3-years of test1 cohort, area under the curve (AUC) of the curve are 0.702, 0.688, and 0.750, correspondingly. (F) Univariate cox regression analysis illustrated the independence of risk score, stage, grade, gender, and age. (G) Multivariate cox regression analysis showed the independence of risk score, stage, grade, gender, and age.

Figure 10 | Internal validation of the robust ECM-APP in the test2 cohort. (A) Sectionalization premised on the median risk score. (B) Survival status and risk score distributions. (C) Heatmap illustrated the expression profiles of 9 genes integrated into the prognostic signature. (D) Survival curve of OS time in low-risk and high-risk subgroups. (E) ROC curve of 1-, 2-, and 3-years of in test2 cohort, area under the curve (AUC) of the curve are 0.771, 0.775, and 0.818, correspondingly. (F) Univariate cox regression analysis showed the independence of risk score, stage, grade, gender, and age. (G) Multivariate cox regression analysis showed the independence of risk score, stage, grade, gender, and age.

Figure 11 | External validation of the robust ECM-APP in the test3 cohort. (A) Sectionalization premised on the median risk score. (B) Survival status and risk score distributions. (C) Heatmap demonstrated the expression profiles of 9 genes integrated into the prognostic signature. (D) Survival curve of OS time in low-risk and high-risk subgroups. (E) ROC curve of 1-, 2-, and 3-years of in test3 cohort; areas under the curve (AUC) of the curve are 0.73, 0.707, and 0.701, correspondingly.

Characterization of TMB between low- and high-risk populations

Numerous studies have shown that TMB is linked to elevated CD8+ T cell infiltration levels. These lymphocytes can identify cancer cells and subsequently initiate anticancer responses (28–30). For that, the association between TMB and risk score was explored using the mutation data of PAAD patients in the train, test1, and test2 cohorts. To begin, the TMB levels were discovered in both the low- and the high-risk subgroups. There was a substantial difference in TMB levels between the two subgroups (P < 0.05) in the train, test1, and test2 cohorts (Figures 12A–C), and the TMB scores were statistically elevated in the high-risk subgroup. Additionally, we researched and created a visual representation of, the distribution of gene mutations in the low- and high-risk score subgroups. The research evaluated the mutation status and clinical and pathological characteristics of the topmost 20 driver genes that had the most frequent alteration. The mutation counts of genes were visibly elevated in the high-risk subgroup in contrast with those in the low-risk subgroup in the train, test1, and test2 cohorts, principally including KRAS, tumor protein p53 (TP53), and CDKN2A (Figures 12D–F).

Figure 12 | ECM-APP-based analysis of tumor mutation profiles and their clinical correlation. (A–C) The discrepancy in the TMB scores between low-risk and high-risk subgroups in the train, test1, and test2 cohorts. (D–F) The waterfall plot showed the frequently mutated genes in low-risk and high-risk groupings from the train, test1, and test2 cohorts. High risk is indicated by the red line, while low risk is denoted by the blue line. (G–I) Survival analysis of TMB scores in low- and high-risk subgroups from the train, test1, and test2 cohorts. (J–L) Survival analysis stratified by the risk scores and TMB scores from the train, test1, and test2 cohorts.

Patients diagnosed with PAAD who had elevated TMB scores showed a lower OS time as opposed to those who had low TMB values across all three cohorts (Figures 12G–I). To gain a deeper comprehension of the effectiveness of the consistent prognostic value of TMB and risk score, we verified the synergistic impact of the two markers in the prognosis prediction of PAAD. Interference of TMB status with the prognosis prediction accuracy of risk scores was observed, as shown by the stratified survival curve. We found patients with high TMB scores and risk scores had a worse survival time than the others in the train, test1, and test2 cohorts (Figures 12J–L). In summary, these data revealed that risk score might serve independently as a predictor and could evaluate the clinical outcome of immunological therapy for anti-tumor treatment.

Characterization of tumor immune microenvironment between low-risk and high-risk populations

The variation of immunocyte infiltration between low- and high-risk was calculated by EPIC, XCELL, MCPCOUNTER, QUANTISEQ, CIBERSORT-ABS, CIBERSORT, and TIMER algorithms. The heat map about immune cell infiltration showed most immune cells had fewer infiltrations in the high-risk subgroup in the train cohort (Figure 13A), which was validated by the findings of the infiltration status of immune cells in test1 and test2 cohorts (Figures 13B, C). To get a deeper comprehension of the connection between the risk score and the immune state, we performed ssGSEA to quantify the enrichment scores of a variety of immune cell subpopulations and the functions that are associated with them. The components that make up the antigen presentation process, notably the score of B cells, T cells, DCs, mast cells, neutrophils, NK cells, pDCs, Type II IFN response, T cell co−stimulation, Th1 cells, T cell co−inhibition, TIL, cytolytic activity, check-point, CCR, T helper cells, were significantly different across the high- and low-risk subgroups in the train cohort (all adjusted P< 0.05, Figures 13D, G). Interestingly, we found that the score of all the immune cells, as well as immune-associated functions, were lower in the high-risk subgroup, which could be confirmed in the test1(Figures 13E, H) and test2 cohorts(Figures 13F, I). Considering the importance of immune checkpoints for immune cells to exert their anti-tumor function, the gene expressions of immune checkpoint-related molecules between high- and low-risk subgroups were depicted in Figure 13J. As we could see, all the immune checkpoint-related molecules had significantly statistical differences in the train cohort. The results of immune checkpoints in test1 and test2 cohorts were consistent with this (Figures 13K, L). And in the low-risk subgroups, the gene expressions of immune checkpoint-related molecules were higher than in high-risk subgroups.

Figure 13 | ECM-APP-based analysis of tumor immune microenvironment. (A–C) The heat maps showed discrepancies in infiltration levels of immune cells across low- and high-risk subgroups premised on the train, test1, and test2 cohorts. The box plots showed discrepancies in ssGSEA scores between two risk subgroups in the (D, G) train cohort, (E, H) test1, and (F, I) test2cohorts. (J–L) The box plots showed discrepancies in immune checkpoint expression in low- and high-risk subgroups premised on the train, test1, and test2 cohorts. (* indicates p <0.05; ** indicates p < 0.01; *** indicates p < 0.001. ns, non-significant).

Discussion

PAAD is characterized by an extraordinarily dense fibrotic stroma, which hinders both the perfusion of the tumor as well as the delivery of anticancer medications (31). Because the ECM comprises the vast majority of the stroma, it is the primary factor in the stroma’s elevated interstitial tissue pressure as well as its stiff mechanical characteristics (32). Besides its mechanical effect, the ECM also provides critical physical and biochemical signals that stimulate the survival, proliferation, and metastases of cancer cells (33). Furthermore, PAAD cells can endure the nutrient-deficient environment due to the ECM, which acts as a source of nutrients for the cells (33). Even though treatment approaches utilizing stroma-depleting medications have shown unsatisfactory results, a growing body of research suggests that the ECM might provide several viable therapeutic targets (32). Thus, ECM-based risk stratification of PAAD is a promising strategy for prognosis assessment and individual management.

First, a comprehensive investigation of the pan-cancer-related ECMGs was carried out with the assistance of a wide variety of bioinformatics-related technologies. For the first time, the expression traits, prognostic values, methylation levels, mutation profiles, and signaling pathway correlation of ECMGs in pan-cancer have been presented using heatmaps, which opens up a wide variety of possible study avenues for the investigation of ECMGs in the future. Because PAAD was the primary focus of our work, we devoted more time and effort so as we can thoroughly analyze it. We found that the majority of ECMGs were present as risky genes in the patients who had PAAD. This finding was in line with the findings from the earlier studies, which showed that upregulation of ECMGs may contribute to the progression of cancer (4). Of note, our findings also revealed that ITGA7, CD36, SDC3, COL4A6, and TNXB functioned as protective genes in PAAD, which was an inconsistent phenomenon that ITGA7 and CD36 had been reported to serve as risky genes in the onset, progression, and chemoresistance of PAAD (34, 35). It’s conceivable that these contradicting findings are because there are still pathways that haven’t been found, and they might be competing with each other for dominance or indicate heterogeneous tumors.

It has been well-established that genes with both differential expression traits and clinical significance could be served as the hub genes of the occurrence and progression of diseases. Our findings illustrated considerable variations in the expression of COL11A1, ITGA3, ITGB4, ITGB6, LAMA3, LAMB3, and LAMC2 between PAAD and normal pancreatic tissues, which was verified by qRT-PCR and IHC. Furthermore, above 7 ECMGs were also detected to be significantly associated with prognosis and stage of PAAD, indicating their crucial roles in the pathophysiologic mechanisms of PAAD.

Subsequently, we further stratified patients with PAAD into 3 clusters depending on their ECMG expression levels and ECM scores. The OS rates of patients who belonged to the ECM-inactive cluster were reported to be substantially greater in contrast with those of patients who belonged to the ECM-active cluster, demonstrating that genes implicated in ECM were mostly risky.

The predominant therapies for PAAD are those that target the ECM pathway, including poly ADP-ribose polymerase inhibitors, tyrosine kinase inhibitors, and plant anticancer drugs (36, 37). Therefore, in the treatment of PAAD, we investigated the potential involvement of some of the most widely administered medicines that target ECMGs. We discovered that the three patient clusters had distinct responses to the medications that were investigated, which suggests that patients may receive a treatment plan that is better tailored to their specific needs if ECMG expression profiles were used to make the decision. For example, patients whose ECM pathway is inactive may benefit more from treatment with gefitinib and AZD.8055 (mTOR Inhibitor), while patients with highly active ECM pathways may gain more benefit from treatment with AZD.0530 (saracatinib) and docetaxel.

It is now a well-established and consensually-acknowledged fact that the aberrant expression of oncogenes and tumor suppressor genes might result in the onset and progression of PAAD. ECM might disrupt the balance between tumor suppressor genes and oncogenes, thus aggravating the condition and worsening the prognosis of PAAD. Surabhi and his colleagues reported that the collagen milieu promotes resistance to chemotherapy in PAAD via elevated histone H3K9 and H3K27 acetylation levels, as well as elevated expression levels of GCN5, PCAF, and p300 histone acetyltransferases, which indicated that ECM exerts profound epigenetic effects on PAAD cells (38). Similarly, our findings revealed that the expression levels of several classical oncogenes and histone modification-related genes (i.e. sirtuins (SIRTs) and histone deacetylases (HDACs)) across the three ECM-based clusters were significantly different. For instance, the decreased levels of oncogenes expression (MTOR, KRAS, MYC, CCND1, PIK3CA, MUC16, HDAC1, HDAC7, and HDAC9) and increased expression levels of tumor suppressors (SIRT3 and SIRT6) in the C2 populations might account for their poor prognoses.

The infiltrating immune cells are a significant part of human malignancies. It progressively became apparent that most neoplasms were not considered foreign to the host and that specific infiltration of immune cells exhibited the potential to promote tumor progression and metastasis, and the immune cells are intimately linked to the clinical outcomes of neoplasms, which could be considered as the effective targets for anticancer treatment (25, 39, 40). Therefore, we assessed the connection between the factors related to the infiltration levels of immune cells and the ECMGs in our study. We found that ECM performs a fundamental function in immune suppression, and has the potential to induce primary as well as secondary resistance to immune checkpoint inhibitors. C2 subtype (ECM-inactive) was accompanied by a low expression of immune checkpoints, suggesting its enhanced anti-tumor immune response. This illustrates that it has better clinical outcomes. Treg cells and mast cells may aid in the progression of a tumor by inhibiting the host’s antitumor immune responses (41, 42). Additionally, the T helper cell might promote tumor metastasis, leading to a dismal prognosis in pancreatic cancer patients (43). As a result, the findings of the association between the ECM score and the mast cells, Treg cells, and T helper cells were in line with our hypothesis. The abundance of the above-mentioned cancer-promoting immune cells positively correlated to ECM score, which might account for the poor prognosis in the C1 subtype.

After that, we developed a risk panel by employing LASSO-Cox regression analysis to predict the patients’ chances of surviving PAAD. K-M analysis and ROC curves were employed in the train cohorts, the internal validation cohort, and the external validation cohort to confirm the outstanding prognostic ability of our ECM-APP. Our ECM-APP was shown to independently serve as a prognostic marker in PAAD utilizing, as evidenced by univariate and multivariate Cox regression analyses.

TMB is a new biological marker for predicting the clinical outcomes of PAAD patients as well as their responsiveness to immunotherapeutic treatment (44). The waterfall plot revealed that the mutation counts of genes were visibly elevated in the high-risk subgroup in contrast with those in the low-risk subgroup, especially for KRAS and TP53. Following that, a stratified survival curve was generated, which revealed that the risk score had prognostic predictive potential, which was independent of TMB, indicating that risk score and TMB characterize diverse aspects of immunology. In addition, the risk score in conjunction with the mutation data showed a substantial variation in the frequency of gene variation between the high- and the low-risk subgroups when viewed from the perspective of the transcriptome.

Because immune infiltration served as a significant driving element in PAAD, we continued our investigation into the fundamental roles played by the tumor immune microenvironment in our ECM-APP. Our data showed that low-risk populations were accompanied by a multitude of anti-tumor immune cell infiltration (e.g. CD4+ T cells, B cells, CD8+ T cells, and NK cells), whereas high-risk populations were accompanied by a multitude of M0 macrophage infiltration. These findings suggested that the high-risk subgroup was an immunosuppressive phenotype, whereas the low-risk subgroup was characterized by an exuberant anti-tumor immune response. Additionally, the expression of immune checkpoints showed a significant difference between low-risk and low-risk populations, which was also one of the possible causes for different clinical outcomes.

Conclusion

Through integrating a series of bioinformatics methods, seven hub ECMGs (i.e. LAMB3, LAMA3, ITGB6, ITGB4, ITGA2, LAMC2, and COL11A1) that has the potential to act as a new indicator of both the advancement and prognosis of PAAD were identified via our research. The pathogenesis of PAAD was associated with an abnormally activated ECM-receptor interaction. Patients with PAAD could be classified into three clusters (i.e. ECM-inactive, ECM-normal, and ECM-active) with different prognosis, immune characteristics, and drug sensitivity based on ECM scores. A novel ECM-APP with excellent prediction capacity was created and verified, which may offer an attractive strategy to predict PAAD patients’ prognoses.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Ethics statement

The studies involving human participants were reviewed and approved by Ethics Committee of the First Affiliated Hospital of Dalian Medical University (ID: PJ-KS-KY-2022-60). The patients/participants provided their written informed consent to participate in this study.

Author contributions

XC, QY, and JL are the co-first authors. XC, QY, and JL were responsible for the design of the research methods, the execution of the experiments, the analysis of the data, as well as the drafting and revision of the manuscript. DS, SL, and ZW conceived the research methodologies, and revised the manuscript. SX, XS, and YS assisted with the gathering of data and conducting the tests. XC also collected the clinical samples. All authors contributed to the article and approved the submitted version.

Funding

The research was conducted with support from the Leading Talent Team of Support Program for High Level Talent’s Innovation of Dalian in 2019 (2019RD11), the Key Projects of Overseas Training Foundation of the Higher Education Institutions of Liaoning Province, China (2020GJWZD004), and the National Natural Science Foundation of China (No. 82000075).

Acknowledgments

We thank Bullet Edits Limited for the linguistic editing of the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2022.985911/full#supplementary-material

Supplementary Figure 1 | Analysis of the single-cell types atlas of seven hub ECMGs (A): COL11A1; (B): LAMC2; (C): LAMA3; (D): LAMB3; (E): ITGA2; (F): ITGB4; (G): ITGB6) related to the prognosis and stage of PAAD by HPA database.

Supplementary Figure 2 | Determination of differentially expressed ECM-associated mRNAs and lncRNAs with prognostic significance. (A) Identification of 278 ECM-related lncRNAs. The correlation analysis between 83 ECM-related mRNAs and 278 lncRNAs was performed by Pearson correlation analysis (|R| >0.4 and P<0.01). (B) Univariate Cox regression analysis showed the prognostic values of 149 differentially expressed ECM-related lncRNAs and mRNAs in PAAD. (C) The LASSO coefficient profiles of ECM-related lncRNAs and mRNAs in PAAD. (D) The cross-validation results of model construction. A total of nine genes were filtered by LASSO Cox regression analysis. (E) Multivariate Cox regression analysis of nine ECM-related mRNAs and lncRNAs.

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REVIEW
published: 28 October 2022
doi: 10.3389/fimmu.2022.1049812

Research progress of bile biomarkers and their immunoregulatory role in biliary tract cancers

Yun-cheng Li †, Kang-shuai Li †, Zeng-li Liu, Yong-chang Tang, Xiao-Qiang Hu, Xing-yong Li, An-da Shi, Li-ming Zhao, Li-Zhuang Shu, Shuo Lian, Zhang-di Yan, Shao-hui Huang, Guo-li Sheng, Yan Song, Yun-jia Liu, Fan Huan, Ming-hui Zhang and Zong-li Zhang *

Department of General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center, Harvard Medical School, United States

Reviewed by:
Chuanzhao Zhang, Guangdong Provincial People’s Hospital, China
Xiu-Ping Zhang, People’s Liberation Army General Hospital, China

*Correspondence:
Zongli Zhang
zzlzzl1900@163.com

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 21 September 2022

Accepted: 12 October 2022

Published: 28 October 2022

Citation:
Li Y-c, Li K-s, Liu Z-l, Tang Y-c, Hu X-Q, Li X-y, Shi A-d, Zhao L-m, Shu L-Z, Lian S, Yan Z-d, Huang S-h, Sheng G-l, Song Y, Liu Y-j, Huan F, Zhang M-h and Zhang Z-l (2022) Research progress of bile biomarkers and their immunoregulatory role in biliary tract cancers. Front. Immunol. 13:1049812. doi: 10.3389/fimmu.2022.1049812

Biliary tract cancers (BTCs), including cholangiocarcinoma and gallbladder carcinoma, originate from the biliary epithelium and have a poor prognosis. Surgery is the only choice for cure in the early stage of disease. However, most patients are diagnosed in the advanced stage and lose the chance for surgery. Early diagnosis could significantly improve the prognosis of patients. Bile has complex components and is in direct contact with biliary tract tumors. Bile components are closely related to the occurrence and development of biliary tract tumors and may be applied as biomarkers for BTCs. Meanwhile, arising evidence has confirmed the immunoregulatory role of bile components. In this review, we aim to summarize and discuss the relationship between bile components and biliary tract cancers and their ability as biomarkers for BTCs, highlighting the role of bile components in regulating immune response, and their promising application prospects.

Keywords: biliary tract cancers, bile components, biomarkers, immune effect, liquid biopsy

Introduction

Biliary tract cancers (BTCs) constitute a diverse group of malignancies emerging from the biliary epithelium, including cholangiocarcinoma and gallbladder carcinoma (1). According to its anatomical site, cholangiocarcinoma can be divided into intrahepatic cholangiocarcinoma(iCCA), perihilar cholangiocarcinoma(pCCA) and distal cholangiocarcinoma(dCCA) (2–4). iCCA, pCCA, and dCCA have distinct clinicopathologic characteristics in terms of epidemiology, molecular characteristics, clinical features, management, and outcomes (5). BTCs account for about 3% of all gastrointestinal tumors, and their incidence has increased in recent years (6). BTCs have an abysmal prognosis, with a five-year survival rate of about 10% (4). Radical resection is the only cure chance for BTCs. However, since patients are usually asymptomatic or only exhibit non-specific symptoms in the early stage, most patients are diagnosed at the advanced stage, missing the opportunity for radical surgery (7). Therefore, early diagnosis of BTCs is vital to improve patient prognosis.

The diagnosis of BTCs is based on a combination of imaging techniques, including ultrasound, CT, MRI, and tumor biopsy (5, 8).CT scanning is convenient and economical and is usually the first choice for diagnosing BTCs. MRI can provide high-resolution anatomical information about the biliary system, while MRCP is audio-visual (1). ERCP can directly display the biliary system and improve the accuracy of diagnosis. ERCP can also be used to confirm the diagnosis of cholangiocarcinoma by brushing cytology or biopsy (9). However, widely accepted biomarkers for diagnosing and dynamically monitoring the disease are still lacking. Currently, widely applied tumor markers, such as serum CA199 and CEA, have limited diagnostic value for BTCs (10). Tumor markers can be used for tumor screening, early detection, differential diagnosis and staging, prognosis judgment, efficacy monitoring, recurrence and metastasis monitoring, and guiding individualized treatment. The ideal tumor markers should have high sensitivity and specificity and reflect the tumor’s dynamic changes. The detection of tumor markers should be simple, fast, and accurate. Tumor markers have great application prospects in cancer. Thus, many researchers are working on searching for biomarkers used in the diagnosis, treatment response, and prognosis of BTCs (11–13). Tumor immunotherapy is a new and hot field. In addition, understanding the interaction between molecules and immune system is the premise of implementing immunotherapy.

Bile is produced by hepatocytes and secreted into the intestine through the biliary system. The compositions of bile are complex. Bile is composed mainly of bile acids, phospholipids, cholesterol, bilirubin, proteins, inorganic salts, etc. Proteins account for about 7% of the total bile compositions (14). Under the condition of disease, the ingredients of bile can be changed, especially in terms of BTCs, which is directly in touch with bile. Therefore, compared with blood or other body fluids, bile is an essential source for searching for tumor markers in the biliary system. Current advances in omics techniques have enabled researchers to discover new and valuable biomarkers in biological fluids (5). This review focuses on changes in bile composition and their interaction with the immune system. Changes in small molecules, proteins, ctDNA, miRNA, and extracellular vesicles have also been discussed.

Small molecule biomarkers in bile

When tumors of the biliary system occur, small molecules in the bile are altered. Existing studies have focused on differences in bile acid molecules and lipids in bile. The production of bile acids in the liver is the body’s primary way of cholesterol metabolism. The primary bile acids, such as cholic and chenodeoxycholic acid, are synthesized from cholesterol in the liver, then secreted into the intestine to produce secondary bile acids through the action of intestinal bacteria. Primary and secondary bile acids are unconjugated and can be combined with glycine or taurine to form conjugated bile acids (15). The intestine can reabsorb more than 95% of all bile acids; the rest is excreted with the stool (16).

Bile acids

Bile acids can be divided into hydrophobic and hydrophilic bile acids according to their affinity for water. Hydrophobic and hydrophilic bile acids play opposite roles in tumor carcinogenesis. Hydrophobic bile acids, such as deoxycholic acids (DCA) and lithocholic acid (LCA), can promote tumorigenesis, whereas hydrophilic bile acids, such as ursodeoxycholic acid (UDCA), can prevent tumorigenesis (17–19). However, the relationship between the composition of bile acid in bile and the malignant transformation of biliary tract tumors has not been fully elucidated. Compared with patients with biliary stones or controls (liver transplant donors without biliary disease), patients with biliary system tumors had lower concentrations of total bile acids and lower ratios of DCA and LCA. For patients with bilirubin ≤ 2.0mg/dL, biliary tract tumor patients also showed lower total bile acid concentration and a lower percentage of DCA and LCA compared with the control group, suggesting its value in the early diagnosis of biliary tract tumors (20). A later study further confirmed the reduction in total bile acid concentrations. They also found an elevated ratio of primary bile acids and conjugated bile acids in CCA patients compared with BBD or PC, which is consistent with previous results (21, 22), indicating that conjugated bile acids can promote the growth of bile tract cancers. They further identified GCA as a positive biomarker and TUCDA as a negative biomarker for cholangiocarcinoma (23). Researchers have also found that compared with benign stenosis, the conjugated bile acids(C)/unconjugated bile acids(U) ratio of malignant stenosis patients is relatively low. As the C/U ratio decreases, the diagnosis rate of inflammation and tumors increases (24). The synthesis of bile acids is dynamically regulated and bile acid concentration can be a real-time and reliable indicator of liver function (25).However, the regulatory mechanisms of bile acid synthesis is complex and have not yet been fully elucidated. The results observed in those studies may reflect a partial imbalance in the process of bile acid during tumorigenesis.

In addition to digestive function, bile acids also participate in the regulation of immune function. Primary and secondary bile acids can inhibit the expression of pro-inflammatory cytokines from monocytes, macrophages, dendritic cells, and Kupffer cells (26). Bile acid metabolites affect the host immune response by directly regulating the balance of TH₁₇ and Tregs (27). One LCA derivative, 3-oxolCa, can inhibit TH₁₇ cell differentiation. Another LCA derivative, IsoalloLCA, can enhance Treg differentiation. Biliary primary and secondary bile acids can regulate the expression of RORγ⁺ regulatory T cells via bile acid nuclear receptor (28). Primary bile acids increase the expression of CXCL6, thereby causing aggregation of hepatic CXCR6⁺ NKT cells and producing anti-tumor effects through IFN-γ, while secondary bile acids have opposite effects (29). SIRT5 is a metabolic regulator involved in the occurrence and development of a variety of tumors and plays context-specific roles. A recent study found that the decreased expression of SITR5 in hepatocellular carcinoma patients leads to increased bile acid synthesis. Bile acids act as signaling molecules to stimulate their nuclear receptors and promote the polarization of M2-type macrophages, thereby creating an immunosuppressive tumor microenvironment and promoting tumor growth (30). It is reasonable to infer that bile acids play a role in the occurrence and development of biliary tract tumors by interacting with the immune system. In conclusion, bile acids play a complex role in immune regulation, and understanding the role of bile acids in immunity can promote the implementation of immunotherapy.

Lipids

Lipids include phospholipids, glycolipids, and cholesterol and their esters. Oxidized phospholipids(oxPLs) are essential in tumor cell apoptosis (31). Ten oxPLs were detected in bile from patients with diseases including CCA, PSC, PC, and BBD. Among them, ON-PC(1-palmitoyl-2(9-oxononanoyl)-sn-glycero-3-phosphatidylcholine) and S-PC(1-palmitoyl-2-succinoyl-sn-glycero-3-phosphatidylcholine) were significantly increased in CCA compared with other biliary stenosis patients. The sensitivity and specificity of ON-PC for distinguishing CCA from other biliary strictures were 85.7% and 80.3%, respectively. The sensitivity and specificity were increased to 100% and 83.3% by combining ON-PC and S-PC (32). The lipid peroxidation product 4- hydroxynonenal (4-HNE) was raised in gallbladder cancer patients compared with stones or controls. These findings indicate that lipids and their metabolites are also important bile markers for BTCs (33).

Other small metabolites

Metabolomics was applied to analyze low molecular metabolites in the body (10). The techniques used in metabolomics studies typically include Fourier transform infrared spectroscopy, gas chromatography/mass spectrometry(GC/MS), or lipid chromatography/mass spectrometry(LC/MS) (34). A new nuclear magnetic resonance spectra (NMR)-based metabolomics approach was also established to compare differences in the bile of patients with biliary tract cancer and benign biliary tract disease. A specific NMR spectroscopy can distinguish the two groups with 86% and 81% sensitivity and specificity, respectively (35). Lysophosphatidylcholine, phenylalanine, 2-octenoylcarnitine, and tryptophan levels in biliary tract cancer were decreased in patients of BTCs compared with benign diseases (36, 37).¹H-MRS method was also applied to analyze the composition of bile. The levels of phosphatidylcholine and Taurine in bile were decreased in CCA patients compared to benign groups. The four regions in the final classifier (which represents phosphatidylcholine, bile acids, lipid, and cholesterol) can distinguish the two groups with a sensitivity and specificity of 88.9% and 87.1%, respectively (38). Another study examined volatile organic compounds in bile headspaces (gas above the sample) and found that several compounds (ethanol, acrylonitrile, acetonitrile, acetaldehyde, benzene, carbon disulfide, dimethyl sulfide, 2-propanolol) were decreased in patients with CCA compared with PSCs. A model was established based on the bile concentration of acrylonitrile, 3-methyl hexane, and benzene to distinguish patients with or without CCA. The sensitivity and specificity of the model are 90.5% and 72.7%, respectively (39).

Studies have focused on changes in other small molecules in the bile of patients with biliary tract cancer. The levels of glutathione (GSH), peroxide, ferrous iron (Fe²⁺), glutathione peroxidase (GPX), and farnesyl transferase/geranylgeranyltransferase type-1 subunit alpha (FNTA) in bile were investigated to illustrate the ferroptosis level of eCCA. The ability of these substances to distinguish between eCCA and common bile duct stones was further investigated. Compared with patients with common bile duct stones, the levels of these substances in bile are significantly reduced. Bile can be used as a biomarker source for diagnosing eCCA, especially for differentiating eCCA from benign bile duct stenosis (40). The lactate level in bile differs between the malignant disease group and non-malignant diseases or healthy people and may serve as a biomarker to distinguish benign people and malignancies (41). The content of ions and heavy metals in bile also changes in the diseased state. Compared with gallstones, patients with gallbladder cancer have decreased zinc levels and elevated copper and copper/zinc ratio (42). They tend to have higher concentrations of heavy metals such as chromium and cadmium (43).

Protein biomarkers in the bile of CCA patients

Bile is believed to be the most relevant body fluid, which can closely indicate the pathological conditions of the biliary tract. Proteins are involved in tumor development (44–47). More and more data on protein biomarkers in bile is emerging. Serum CA199 and CEA are widely used markers for diagnosing biliary duct cancer, although the sensitivity and specificity are relatively low (48). Consequently, researchers have also studied the possibility of CA199 and CEA as bile biomarkers of CCA. The results indicate that bile CA199 and CEA are not valuable in distinguishing benign and malignant obstructive biliary tract disease (49). Thus, more potential bile markers with high sensitivity and specificity are urgently needed. Proteins in bile that may serve as tumor markers are summarized in Table 1.

Table 1 | Bile proteins with potential diagnostic value for BTCs.

Mucin family

The mucin family is a group of glycosylated macromolecules. Some mucins are abnormally expressed in cancer cells and are involved in tumorigenesis and progression (70). Several mucin family members also exist in the bile and can serve as bile markers. MUC2 and MUC16 were the first identified mucin family members which were differentially expressed in bile between CCA and controls using proteomics methods (71), although the sensitivity and specificity of bile MUC2 and MUC16 in diagnosing CCA still need further validation. Previous studies have shown that intratumoral MUC4 and MUC5AC are associated with the diagnosis and prognosis of biliary tract tumors (72, 73). Therefore, whether bile MUC4 and MUC5AC are indicators of biliary tract tumors has attracted attention. Bile MUC4 was identified as a particular marker of BTCs (74). However, whether bile MUC5AC expression could distinguish benign and malignant biliary tract diseases is still controversial. A study reported that bile MUC5AC couldn’t distinguish benign from malignant biliary tract diseases (74). However, another study showed that bile MUC5AC could distinguish benign and malignant patients with higher diagnostic accuracy than serum MUC5AC (AUC=0.85 in bile vs. AUC=0.82 in serum). Evaluating MUC5AC in bile and serum simultaneously and based on the serum-to-bile ratio can achieve better diagnostic performance (AUC=0.97) (50).

As glycosylated macromolecules, studies have also focused on the performance of glyco-alteration of glycoproteins as a diagnostic marker of CCA. In a study, researchers have established a new sandwich ELISA system to detect the glyco-alteration of MUC1. The results revealed that bile Wisteria floribunda Agglutinin (WFA)-Positive Mucin 1 is a promising CCA biomarker, which can effectively distinguish CCA from benign biliary tract diseases with sensitivity and specificity of 90.0% and 76.3%, respectively and the AUC was 0.86 (51).

NGAL

Neutrophil gelatinase-associated lipocalin (NGAL), also called lipocalin-2 (LCN2), is a 25-kDa multifunctional protein released by activated neutrophils. NGAL plays a role in the onset and development of tumors (75). Zabron et al. compared the bile protein profiles of benign and malignant diseases. Biliary NGAL was the most valuable biomarker in distinguishing between benign and malignant groups. The area under the receiver-operating characteristic curve was 0.76 in differentiating malignant from benign pancreaticobiliary disease with sensitivity and specificity of 94% and 55%, respectively (52). Its diagnostic effect was validated in an independent cohort, and this capability is independent of CA199. The combination of bile NGAL and serum CA199 can improve the diagnostic accuracy for malignancy. Another study showed that bile NGAL levels could distinguish pancreaticobiliary cancer and benign biliary diseases with sensitivity and specificity of 73.3% and 72.2%, respectively. The area under the curve was 0.74 (53). In addition, Chiang et al. reported that biliary NGAL could differentiate CCA patients from gallstone patients with sensitivity and specificity of 87% and 75%, respectively. The AUC was 0.81. CCA patients with higher NGAL expression were also found to have poor overall survival (OS) of patients with CCA (54).

CEACAM6

Bile samples from patients having biliary stenosis caused by CCA or other malignant tumors were analyzed by the proteomics method (76). A total of 127 differentially expressed proteins were identified. Several of them have been reported to be associated with pancreatic cancer. Immunoblotting assays were performed to validate these markers’ expression, and significant CEACAM6 elevation in CCA has been identified. A subsequent study explored the role of bile CEACAM6 in diagnosing CCA. Bile CEACAM6 could discriminate cholangiocarcinoma from benign disease (AUC=0.738) (55). CEACAM6 was further screened and validated as a biomarker to distinguish malignant and benign bile duct stenosis with sensitivity and specificity of 93% and 83%, respectively. The AUC was 0.92. The combination of bile CEACAM6 and serum CA199 can improve the diagnostic accuracy for malignancy (AUC 0.96) (56).

Cytokines

CCA cells can secrete several cytokines and promote tumor progression by paracrine. Some of the cytokines secreted by CCA can also enter bile and may serve as biomarkers. Among them, IGF-1 and VEGF were the most studied. IGF-1 is a bioactive protein polypeptide, and it has been previously reported that serum IGF-1 levels are associated with prostate, breast, pancreatic, lung, and colorectal cancer (77, 78). VEGF promotes tumor angiogenesis, and the relationship between serum VEGF levels and various tumors has also been reported (79, 80). Expression levels of IGF-1 and VEGF in the bile and serum of extrahepatic cholangiocarcinoma, pancreatic cancer, and benign biliary abnormalities were measured to evaluate their role as a tumor marker (57). Biliary IGF-1 perfectly differentiated extrahepatic cholangiocarcinoma from benign biliary abnormalities or pancreatic cancer. The AUC was 1. Bile VEGF levels have no differentiating effect on biliary obstruction. Meanwhile, another study shows the level of bile VEGF could distinguish pancreatic patients from other etiologies of biliary stricture (58). Biliary VEGF-1 may help rule out distal common bile duct cancer (81).

Some cytokines related to chronic inflammation, such as IL-6 and TNF-a, play a role in the development of tumors by interacting with the immune system. Chronic inflammation-driven cytokines released from cholangiocytes, fibroblasts, or immune cells can promote the development of CCA (82).

Enzymes

BTCs are always complicated with an anomalous arrangement of pancreaticobiliary ducts and affect bile digestive enzymes, especially for pCCA and dCCA. Abnormal digestive enzymes and their relative product expression in bile may serve as biomarkers. One study used the quantitative proteomics method to compare bile proteins in six CCA patients with three different gross-appearance tumor types to identify possible biomarkers. They selected α-1- antitrypsin (AAT) for further validation. Fecal AAT, which can indirectly reflect bile AAT, can differentiate CCA from controls with a sensitivity and specificity of 80% and 75%, respectively. The AUC was 0.83 (83). Elevated amylase activity in bile is also associated with pancreaticobiliary reflux, a significant risk factor for biliary system tumors (84, 85). Therefore, high biliary amylase activity is associated with BTCs tumorigenesis (86). However, pancreaticobiliary reflux is affected by the location and extent of biliary tract obstruction. The amylase level in bile is not specific for diagnosing biliary tract cancers (87, 88). Compared with patients with gallbladder stones, the pancreatic elastase (PE) level in CCA patients is elevated. Biliary PE can distinguish patients with CCA from that with gallstones. The combined measurement of PE and amylase and the PE to amylase ratio can improve the accuracy of CCA diagnosis (AUC=0.0877) (59).

Dhar et al. (89) discussed the role of pyruvate kinase M2(PKM2) in the development of cholangiocarcinoma. They proposed that PKM2 can be used as a diagnostic marker of BTCs and is associated with prognosis. A subsequent study (60) showed that biliary PKM2 had low sensitivity (52.9%), but the specificity was high (94.1%) for the diagnosis of malignant and benign bile duct stenosis. The AUC was 0.77.

Mac2BP

In the first published bile proteomics, Mac-2-binding protein (Mac2BP) was identified as the potential biomarker for CCA. The value of Mac2BP as a diagnostic marker for cholangiocarcinoma was further validated in the following study (61). Bile Mac2BP can distinguish between malignant and benign biliary diseases with sensitivity and specificity of 69% and 67%, and the AUC was 0.70. The diagnostic effect is similar to biliary CA199 (AUC 0.69), and the combination of bile Mac2BP and CA19-9 can improve the accuracy of diagnosis (AUC 0.75).

Other bile proteins

Our group (62) applied ELISA to detect survivin and CA199 in bile. Biliary survivin could differentiate CCA and benign biliary obstruction with sensitivity and specificity of 67.27% and 80.85%, and the AUC was 0.78. Combining biliary survivin and CA199 could improve diagnostic accuracy (AUC=0.838).

Wang et al. (90) analyzed the proteins of gallbladder bile samples from gallbladder cancer, gallbladder adenoma, and chronic calculous cholecystitis patients using 2D LC-MS/MS. They proposed S100A8 as a potential biomarker for GBC and used immunohistochemical analysis to confirm it. Another study showed that S100P levels in bile were significantly elevated in patients with CCA, and bile S100P could distinguish between patients with CCA and choledocholithiasis with sensitivity and specificity of 92.9% and 70.0%, respectively (63).

Some other bile proteins including total fibronectin(tFN) and cellular fibronectin(cFN) (64), sB7-HB3 (65), sCD97 (66), LR11 (67), minichromosome maintenance protein 5(Mcm5) (68), HSP27, and HSP70 (69) was also reported to be bile biomarkers to distinguish benign and malignant biliary tract diseases with different sensitivity and specificity. However, these biomarkers still need further validation.

ctDNA

The term liquid biopsy was first used to describe how the same diagnostic information can be obtained from blood samples as tissue samples. In oncology, the term broadly refers to the sampling and analysis of various biological fluids (91). Analytes include cell-free DNA(cfDNA), cell-free RNA(cfRNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs) (91). The interaction between liquid biopsy components and anti-cancer immunity is a new area of research (92). CfDNA refers to fragmented DNA found in non-cellular components of blood, which are usually double-stranded fragments of about 150-200 base pairs in length (93). In cancer patients, the cfDNA released from tumor cells is called circulating tumor DNA (ctDNA) and constitutes part of the total cfDNA (94, 95).

Assessing the ctDNA in bile is a promising liquid biopsy method, especially for patients with insufficient tumor tissue to biopsy. However, whether the mutation profiles detected by ctDNA reliably reproduce the mutation profiles obtained by tumor biopsy remains a question (96). Bile contact with tumor cells directly, Therefore, detecting genetic alterations in bile may be more valuable than in plasma for biliary tract cancers. Several studies (97, 98) have demonstrated the feasibility of detecting genetic mutations of ctDNA in tumors of the biliary system, with a high compatibility rate with tissue biopsies. A recent study shows genetic mutations of ctDNA in bile are similar to those in tissues and are more effective than in plasma (97). Similar conclusions were reached in another study (98). cfDNA in bile samples is mainly long DNA fragments, significantly different from plasma samples. Targeted deep sequencing can reliably detect mutant variants in bile cfDNA. Tumor stage and location affect the sensitivity of mutation detection in bile cfDNA samples. In patients with GBC, the positive rate of mutation detected in bile ctDNA was 58.3%, which is higher than cytology. The mutation compliance rate between tissue DNA and bile ctDNA in GBC patients was 85.7%. KRAS and TP53 are frequent mutation genes in tumors of the biliary system. Several previous studies have evaluated the diagnostic effects of KRAS and P53 mutations in bile on tumors of the biliary system (99–102). Overall, the frequency of KRAS and P53 mutations in bile is too low to rely on these molecular markers to establish a reliable diagnosis.

It was initially thought that higher cfDNA might indicate tumor growth. However, many other diseases contribute to a similar increase. Thus, recent research has focused on epigenetic features of cfDNA, such as methylation (103). One study showed that hypermethylation of UCHL1 and RUNX3 in pancreaticobiliary fluid might be useful for diagnosing biliary duct cancers (104). INK4a/ARF’s promoter methylation status in bile might be useful for diagnosing benign and malignant biliary tract diseases (105). Shen et al. (106) established and validated a methylation panel in the bile, which can improve the sensitivity of detecting BTCs.

ctDNA can be used to diagnose biliary duct cancers at an early stage. Recently, a prospective study using ddPCR to detect the methylation status of four genes (CDO1, CNRIP1, SEPT9, VIM) in bile showed that detecting DNA methylation markers in bile allowed for more accurate and early detection of CCA patients in PSCs (107). Another prospective study suggests that next-generation sequencing (NGS) mutational analysis of bile cell-free DNA (cfDNA) in the initial stages of biliary stenosis can significantly improve the detection of malignant tumors. In addition, it can also test for the presence of mutations suitable for targeted therapy (108).

miRNA and extracellular vesicles

miRNAs are small non-coding RNA molecules of 18-25 nucleotides in length, which can regulate gene expression by binding to corresponding mRNA sites (109). Extracellular miRNAs are stable and detectable in other body fluids (110). There are significant differences in miRNA concentration and composition in different diseases. Therefore, miRNA can be used as a biomarker to evaluate the body’s condition (111). Extracellular miRNAs are not homogenous groups and exist in various body fluids in different forms. Its origin and function are not fully understood. It is currently believed that some extracellular miRNAs are packaged in apoptotic bodies, shedding microvesicles, exosomes, or high-density lipoprotein (HDL) particles. Most extracellular miRNAs (90%-95%) bind to AGO proteins (110).Therefore, attention should be paid to the origin of the miRNAs when evaluating their roles as biomarkers.

Extracellular vesicles are all kinds of vesicles with membrane structures released by cells, and their contents include proteins, lipids, nucleic acids, and so on. According to the diameter and occurrence mode, it can be divided into exosomes, microvesicles, and apoptotic bodies (112, 113). Tumor-derived exosomes are likely to serve as potential cancer markers.

Shigehara et al. (114) compared the difference of miRNA in the whole bile between patients with malignant and benign biliary tract diseases for the first time and pointed out that miR-145 and miR-9 could be used as diagnostic markers of BTCs (Table 2), especially miR-9, which has higher diagnostic accuracy. A subsequent study showed that RNA from free-floating cells degrades rapidly. The bile processing process can produce unpredictable deviations. Therefore, they focused on extracellular vesicles in bile instead of the whole bile and established a miRNA panel based on extracellular vesicles in bile to diagnose CCA with a sensitivity and specificity of 67% and 96%, respectively (115). However, miRNA in extracellular vesicles only accounts for a small part of extracellular miRNA. Thus, one study suggested that extracellular vesicles were not a reliable miRNA source for CCA diagnosis. They recommend cell-free bile rather than whole bile or extracellular vesicles in bile to find potential miRNA markers (118). miRNA profiles in the bile of PSC, PSC with CCA, and CCA patients were different, among which miR-142, miR-640, miR-1537, and miR-3187 were significantly different in PSC and CCA patients (116) (Table 2). In another study, extracellular miR-30d-5p and miR-92a-3p were increased dramatically in the bile of CCA patients compared with benign biliary disease (BBD) patients (117) (Table 2). miR-30d-5p showed the best performance in distinguishing CCA and BBD patients. In addition, it has been demonstrated that high methylation of miR-1247, -200a, and -200b in bile may help determine benign and malignant biliary tract diseases (119).

Table 2 | Bile miRNAs with potential diagnostic value for BTCs.

The extracellular vesicles can also be used to distinguish between patients with benign and malignant diseases. As aforementioned, an established miRNA panel (Table 3) in the extracellular vesicles can be used to diagnose CCA (115). Ge X et al. (120)compared the expression of lncRNAs in bile exosomes from CCA patients and biliary obstruction patients. They identified that two lncRNAs were significantly elevated in CCA, and the combined use of two lncRNAs showed better diagnostic value (Table 3). Besides, both of them were associated with CCA prognosis. Bile exosome circCCAC1 can distinguish CCA from benign hepatobiliary diseases (AUC=0.857) while tissue circCCAC1 is associated with the prognosis of patients (122). In addition to non-coding RNA, proteins in extracellular vesicles can also be used as markers for patients with CCA. Ikeda, C et al. (121)used EDEG to separate human bile EVs and analyzed proteins in extracellular vesicles for the first time. They found that Claudin-3 in bile EVs can distinguish between CCA and patients with bile duct stones with a sensitivity and specificity of 87.5% and 87.5%, respectively (Table 3). The concentration of EVs in bile may be relevant to diagnosing malignant biliary tract disease. One study showed that the concentration of EVs in bile can distinguish malignant from non-malignant CBD stenosis with 100% accuracy (123).

Table 3 | Bile EVs with potential diagnostic value for BTCs.

In addition to being a marker of disease, the potential therapeutic role of EVs in disease is gradually being discovered (124). EVs can target receptor cells, increase drug concentration in target tissues, and reduce drug toxicity and side effects. The outer membrane of the vesicle increases drug stability and prevents drug degradation. The study of vesicle-related functions has become a research hotspot and is expected to play a role in the early diagnosis and treatment of many diseases.

Discussion

BTCs are a group of highly heterogeneous diseases with abysmal prognosis. Finding a method for early diagnosis of BTCs in a non-invasive or minimally invasive way and evaluating the treatment effect to improve the prognosis of patients with BTCs is promising. The search for tumor markers is a feasible approach. Many previous studies have focused on finding a substance in the blood, evaluating its relationship with BTCs, and determining whether it can be used as a tumor marker. Blood samples are readily available, but blood often reflects the body’s overall conditions and cannot be accurately localized to an organ or tissue. The components in the blood may interfere with each other and mask the characteristic information of the disease, especially in the early stages (125). Therefore, many studies have focused on finding tumor markers in other body fluids. Bile is directly exposed to BTCs, and tumors will release certain substances into bile. Thus, bile may contain tumor-specific information, an essential source for searching for tumor markers. The acquisition of bile is invasive, and it is difficult to obtain information about a healthy person’s bile (14). While in the disease state, some treatments such as ERCP, PTCD, and surgery become essential sources of bile without adding additional trauma to patients. Therefore, bile biomarkers research is more valuable. This study focused on bile and summarized the research progress of the relationship between bile components and BTCs.

Metabolomics is used to analyze low molecular metabolites in bile to determine the pathophysiology of the human. Bile acids and lipids are two kinds of small molecules that are most concerned at present. However, the results of different studies are inconsistent or even opposite. Many factors are involved, including pretreatment before bile testing, tumor anatomical site and methods of bile collection, individual differences in disease severity, and various other factors affecting the results. For the early diagnosis of BTCs, more attention was paid to whether bile composition changes in the absence of biliary obstruction (20). Bile acids and lipids are involved in the occurrence and development of BTCs, and the mechanism needs to be further studied.

Proteomics studies the composition and variation of proteins in cells, tissues, or living organisms. Clinical detection of disease is mainly based on protein analysis. Bile protein analysis is challenging to carry out because of the interference of many other substances (14). Thus, pretreatment is required before proteomic analysis. The proteome is a fluctuating description of the host’s response to disease. Many minor but significant changes may occur in the early stage of the disease (126).Therefore, combining multiple markers is more effective than single markers (127). Protein changes arise during the malignant transformation of healthy cells (128–130). Researchers currently focus on finding overexpressed proteins that flow into body fluids due to disease as disease biomarkers (131–133). Since bile is directly in contact with BTCs, tumor markers of BTC are more likely to be identified in bile. The use of mass spectrometry to analyze proteomic patterns in tissues and body fluids, combined with bioinformatics tools to distinguish patterns in normal, benign, or malignant disease states, namely, proteomic pattern diagnosis (134). Thus, artificial intelligence (AI) is required for processing high-throughput omics data.

Liquid biopsy is a hot field now, which can be used to detect cfDNA, cfRNA, CTCs, EVs, etc. (91). Compared with the traditional methods, it can comprehensively reflect the overall state of the tumor in a minimally invasive and fast way, and it applies to a wide range of people. Liquid biopsies are good supplements for patients who have difficulty obtaining biopsy tissues or have insufficient tissues for genetic testing. Liquid biopsies can be used to diagnose and guide the treatment of patients (135). The low ctDNA content of the samples used for liquid biopsies, especially in the early stages, poses a great challenge to the detection of ctDNA (135). ctDNA is currently the most widely used in clinical practice. ctDNA can be used as a tumor marker of BTCs by detecting the related mutation status of ctDNA (125). ctDNA in bile provides a comprehensive view of the tumor genome, reflecting DNA released from multiple tumor regions. It is dynamic. Thus, ctDNA in bile can monitor treatment response, drug-resistant, tumor heterogeneity, and detect residual disease after surgery. In conclusion, detecting ctDNA in bile is an auspicious method.

Multiple miRNAs have been found in the blood as markers of BTCs. However, miRNAs are rarely reported in bile. Extracellular miRNAs are heterogeneous and may play an essential role in the occurrence and development of BTCs (110). Extracellular miRNAs may act as tumor markers for BTCs and are associated with prognosis. EVs are a recently emerging class of molecules that play an essential role in cell-to-cell communication, with exosomes being the most mature (113). There are also growing studies of EVs in bile. EVs contain various substances such as proteins, nucleic acids, etc. These substances within EVs or the concentration of EVs in bile may play an essential role in the occurrence and development of BTCs and may act as biomarkers for BTCs. EVs can also be used in the treatment of diseases. Bile-specific exosomes may lead to better therapeutic outcomes. Bile enters the intestine through the biliary tract and may be helpful in the treatment of intestinal diseases (124).

In addition to traditional chemotherapy and targeted therapy, tumor immunotherapy is a revolutionary breakthrough. Instead of acting on tumor cells, it activates the body’s immune system to produce an anti-tumor effect. Understanding the mechanism of interaction between molecules and the immune system is immunotherapy’s premise. Anti-tumor immunotherapy is promising theoretically and ideologically. However, it still seems a long way to go.

Future perspectives

Bile biomarkers are promising and meaningful in diagnosis and prognosis judgement of CCA. However, bile collection is invasive, and it is difficult to obtain normal bile from a healthy donor (14). Setting the control group to patients with benign diseases may lead to unpredictable biases. Instead, finding a non-invasive way, such as blood, feces, etc., to get an accurate diagnosis is preferred. However, this requires building a relationship between bile and the clinically readily available samples such as blood, urine, stool, etc. Besides, single bile biomarker is hard to reach a high specificity and a high sensitivity simultaneously. An accurate BTCs diagnosis involves combining data from multiple omics, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. The high-throughput multi-dimensional information, combined with AI technology, makes it possible to diagnose BTCs more accurately and improve the judgment of patient prognosis.

Author contributions

Y-CL, K-SL, Z-LL, Y-CT, and X-QH collected the references. Y-CL and K-SL analyzed the references and wrote the paper. X-YL, A-DS, L-MZ, L-ZS, SL, Z-DY, S-HH, G-LS, YS, Y-JL, FH, and M-HZ read the paper and provided revising advice. Z-LZ contributed to study supervision and revised the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 81900728, 82072676,82172791,82203766), Shandong Province Natural Science Foundation (Grant No. ZR2019MH008, ZR2020MH238, ZR2021QH079), Shandong Province Key R&D Program(Major Scientific Innovation Projects,2021CXGC011105), Shandong Medical and Health Technology Development Project (Grant No. 2018WSB20002), Clinical Research Foundation of Shandong University (Grant No. 2020SDUCRCA018), Key Research and Development Program of Shandong Province (Grant No. 2019GSF108254). The funders had no role in study design, data collection, analysis, interpretation, and manuscript writing.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Glossary

ORIGINAL RESEARCH
published: 01 December 2022
doi: 10.3389/fimmu.2022.1060051

Prognostic model of immune checkpoint inhibitors combined with anti-angiogenic agents in unresectable hepatocellular carcinoma

Xiaomi Li †, Wei Sun †, Xiaoyan Ding, Wei Li * and Jinglong Chen *

Department of Cancer Center, Beijing Ditan Hospital, Capital Medical University, Beijing, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Antonio Giovanni Solimando, University of Bari Aldo Moro, Italy
Chunxia Su, Shanghai Pulmonary Hospital, China
Hong Zhu, Sichuan University, China

*Correspondence:
Jinglong Chen
cjl6412@ccmu.edu.cn
Wei Li
vision988@126.com

†These authors have contributed equally to this work and share first authorship

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 02 October 2022

Accepted: 21 November 2022

Published: 01 December 2022

Citation:
Li X, Sun W, Ding X, Li W and Chen J (2022) Prognostic model of immune checkpoint inhibitors combined with anti-angiogenic agents in unresectable hepatocellular carcinoma. Front. Immunol. 13:1060051. doi: 10.3389/fimmu.2022.1060051

Background

The combination of immune checkpoint inhibitors (ICIs) and anti-angiogenic agents has shown promising efficacy in unresectable hepatocellular carcinoma (HCC), but until now no clinical prognostic models or predictive biomarkers have been established.

Methods

From 2016 to 2021, a total of 258 HCCs treated with ICIs and tyrosine kinase inhibitors (TKIs) were retrospectively enrolled, as the study cohort. Patients’ baseline data was extracted by least absolute and shrinkage selection operator (LASSO) and Cox regression. Finally, a prognostic model in the form of nomogram was developed. Model performance was assessed in terms of discrimination, calibration, and clinical utility. A 5-fold cross-validation was used to evaluate the internal repeatability of the model. In addition, the patient cohort was divided into three subgroups according to nomogram scores. Their survivals were estimated by Kaplan-Meier methods and the differences were analyzed using log-rank tests.

Results

Seven clinical parameters were selected: Eastern Cooperative Oncology Group performance status (ECOG PS), combination of transarterial chemoembolization (TACE), extrahepatic metastasis (EHM), platelet to lymphocyte ratio (PLR), alanine aminotransferase (ALT), alpha-fetoprotein (AFP), and Child-Pugh score. The model had an area under the curve (AUC) of 0.777 at 1 year and 0.772 at 2 years. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) showed that the discrimination, consistency and applicability of the model were good. In addition, cross-validation validated the discrimination of the model, and the C index value of the model is 0.7405. The median overall survival (OS) of the high-, medium- and low-risk subgroups was 7.58, 17.50 and 53.17 months, respectively, with a significant difference between the groups (P < 0.0001).

Conclusion

We developed a comprehensive and simple prognostic model for the combination of ICIs plus TKIs. And it may predict the efficacy of the combination regimen for unresectable HCC.

Keywords: hepatocellular carcinoma, immune checkpoint inhibitors, anti-angiogenic agents, predictive model, platelet to lymphocyte ratio, alpha-fetoprotein

Introduction

Primary liver cancer is ranked the sixth of cancer morbidity and the third of cancer mortality worldwide (1). And hepatocellular carcinoma (HCC) is the main pathological type of liver cancer (2). HCC has a poor prognosis, possibly due to untimely diagnosis and complex tumor microenvironment (3, 4). Sorafenib was the first approved front-line tyrosine kinase inhibitor (TKI) for unresectable HCC (5, 6). In recent years, four other TKIs (lenvatinib, regorafenib, cabozantinib, and ramucirumab) have been used for first-line or second-line treatment of HCC (7–10). While immune checkpoint inhibitors (ICIs) have shown clinical prospects in other cancers, these programmed cell death protein 1 (PD-1) monoclonal antibodies including nivolumab, pembrolizumab, camrelizumab and sintilimab, are approved in advanced HCC based on clinical trials (11–14). In the light of the synergic efficacy for the combination of anti-angiogenic TKIs or monoclonal antibody plus ICIs, the combination regimens have been validated and approved (15, 16). More notably, IMbrave150 and ORIENT-32 showed that the combination of ICIs and anti-angiogenic agents resulted in longer overall survival (OS) of 19.2 months and progression-free survival (PFS) of 6.9 months compared with mono-sorafenib (17, 18). Though with the increasing evidence for ICIs plus TKIs in advanced HCC, the prognosis of advanced HCC varies a lot and clinical predictive biomarkers are limited (15). Considering that some patients could not benefit at all, they would be screened by the prognostic model to further guide clinical choice.

As for the prognostic biomarkers, unlike other malignancy, tumor mutation burden (TMB) and programmed cell death protein ligand-1 (PD-L1) expression seems less predictive in HCC (15, 19, 20). The diversity of response to treatment and the potential severe adverse events makes the discovery of biomarkers of great clinical importance. Radiomics nomograms have achieved good predictive performance, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.880, but the acquisition and analysis of images often require professional radiologists and professional equipment (21, 22). Two radiomics models involved a sample size of less than 60 and uncertain treatment regimens, either mono-ICIs or the combination of anti-angiogenic agents plus ICI. Genomics utilizes tumor tissue for testing and it is invasive (23–25). Although these clinical predictive models are convenient to use, none of the current models involve clinical inflammatory factors (15). Indeed, the tumor microenvironment is closely related to the prognosis of HCC, and neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), prognostic nutritional index (PNI), and systemic immune inflammatory (SII) index are representative systemic inflammatory markers (26–29). The comprehensive model including the above factors could be developed, thus, the patients with poor prognosis will be promptly identified and invalid treatment might be avoided.

Therefore, this study retrospectively analyzed unresectable HCC treated with ICIs combined with TKIs. The predictive factors affecting patient survival were explored and a prognostic model for advanced HCC in Chinese population was developed, in whom HBV-infection was the main etiology.

Methods

Patients and data collection

Patients with unresectable HCC were retrospectively enrolled, who received PD-1 inhibitors and anti-angiogenic agents at Beijing Ditan Hospital, Capital Medical University from 2016 to 2021. The study conformed to the 1975 Declaration of Helsinki and has been approved by the Ethics Review Committee of Beijing Ditan Hospital, Capital Medical University. All patients provided written informed consent.

Inclusion criteria for this study included: (1) aged ≥ 18 years; (2) HCC diagnosed by imaging or pathological examination; (3) Child-Pugh class A or B; (4) Eastern Cooperative Oncology Group performance status (ECOG PS) < 2; (5) BCLC stage C or B unable to tolerate radical treatment. Meanwhile, the following patients were excluded: (1) combined with a history of other malignancy outside HCC or metastatic liver tumor; (2) pregnant or lactating women; (3) severe organ or blood dysfunction; (4) abdominal computed tomography (CT) and/or magnetic resonance imaging (MRI) were not performed. (5) loss of follow-up.

Patients’ clinical data were queried by electronic medical records, mainly including age, gender, ECOG PS, etiology (“HBV”or”HCV”or”other”), previous and present treatment (surgery, ablation, transarterial chemoembolization (TACE) and treatment line), Child-Pugh class and BCLC stage. In addition, imaging data (tumor number, tumor size, portal vein tumor thrombus (PVTT), extrahepatic metastasis (EHM)) and laboratory parameters (peripheral blood count, liver function, and tumor marker AFP) were collected before the start of treatment, and NLR (absolute neutrophil count/absolute lymphocyte count), PLR (absolute platelet count/absolute lymphocyte count), LMR (absolute lymphocyte count/absolute monocyte count), PNI (serum albumin + 0.5 * absolute lymphocyte count), and SII (absolute neutrophil count * platelet count/absolute lymphocyte count) were calculated.

Treatment and follow-up

All patients were treated with ICIs, and the selected PD-1 inhibitors mainly included camrelizumab (Hengrui Medicine, China) and sintilimab (Innovent Biologics, China), 200 mg intravenously every three weeks. In addition, the above patients could be included in this study whether they were TKI ‐ naïve or TKI ‐ experienced. The TKIs involved were sorafenib (Bayer Pharma AG, Germany) 400-800 mg/day orally; lenvatinib (Eisai Co., Ltd., Japan) 8 mg/day for body weight < 60 kg and 12 mg/day orally for body weight ≥ 60 kg; and regorafenib (Bayer Pharma AG, Germany) 160 mg/day orally every 4 weeks 1-3. Patient management was to modify dose and interrupt treatment according to local drug regulations.

Response to treatment and questioning survival status were assessed every 6-12 weeks after the start of treatment, and medication modifications were documented.

Model development and validation

Histograms were plotted to view the data distribution, and logarithmic transformation was performed for continuous variables with a clearly skewed distribution. Alternative predictors were screened by least absolute shrinkage and selection operator (LASSO) regression, and then independent predictors were selected by multivariate Cox regression (30). Based on the above results, a nomogram was constructed as the final model. Model performance was then assessed by ROC curve, calibration curve, and decision curve analysis (DCA). ROC curve measures the discriminatory power of the model, and AUC value quantitatively describes discrimination, with 0.5 indicating no discriminatory power and 1 indicating the best discriminatory power (31). The consistency between the model predicted risk and the actual risk is represented by the calibration curve, and the closer to the slope line, the better the calibration (32). DCA curves assess the clinical utility of the model, with greater area under the curve having better applicability (33). To test the reproducibility of the model, we intend to use a 5-fold cross-validation. We randomly selected 80% of patients as the training set, and the rest as the validation set, calculate AUC value, repeat 1000 times, and record the results in box plot and scatter plot. A total score was calculated for each patient based on the nomogram assigned ratio and divided into three risk groups: low, medium, and high using X-tile software.

Statistical analysis

All statistical analyses were implemented by R software (version 4.1.1, Chicago, IL). Quantitative data were presented as median (range), while qualitative data were presented as number (percentage). OS was calculated from the start of treatment until death or last follow-up, survival curves were plotted by Kaplan-Meier method, and median survival (mOS) and 95% confidence interval (95% CI) were reported. Log-rank test was used when comparing different risk groups and P < 0.05 was considered statistically significant. The software packages involved in this study were “ggplot2”, “survminer”, “survival”, “glmnet”, “rms”, “riskRegression”, “dcurves”, “caret”, “nomogramFormula” and “dplyr”.

Results

Patient characteristics

Two hundred and fifty-eight patients treated with ICIs and TKIs were recruited retrospectively in this study (Figure 1). Patients’ baseline characteristics are shown in Table 1, with a median age of 59 (range, 51-65) years, 39 (15.1%) women and 219 (84.9%) men. And the majority of HCC was caused by HBV infection (n = 230, 89.1%). Ninety-seven (37.6%) patients had ECOG PS of 1, 157 (60.9%) had intrahepatic tumors > 3, 157 (60.9%) had maximum tumor diameter > 5 cm, and 76 (29.5%) had Child-Pugh class B. Overall, 195 (75.6%) patients were in BCLC stage C and had indications for PD-1 inhibitors and anti-angiogenic agents. In addition, although one-quarter of patients were in the middle stage of HCC, progression after TACE required the combination of systemic therapy. Other treatment history before ICI varied, with 40 (15.5%) and 109 (42.2%) cases receiving surgery and ablation, respectively, and 225 (87.2%) cases had combined TACE, 109 (42.2%) patients were first-line and the rest were later-line. In addition, 137 (53.1%) patients had PVTT and 101 (39.1%) had EHM. Until the last follow-up, a total of 97 (37.6%) patients had died, with a mOS of 30.2 (95% CI, 25.1 – 40.8) months, and their survival curves are shown in Figure 2.

Figure 1 | Flowchart of the study.

Table 1 | Patient baseline characteristics.

Figure 2 | Kaplan-Meier curves of overall survival (A) in whole population.

Model development and validation

Histograms were used to view the data distribution of continuous variables. NLR, PLR, SII, alanine aminotransferase (ALT), aspartate aminotransferase (AST) and AFP showed a significantly skewed distribution, and logarithms were taken for the above factors. While other variables (age, LMR, PNI and albumin/globulin ratio (A/G)) were not converted. Eleven variables were selected by lambda values corresponding to partial Likelihood Deviance in Lasso regression: ECOG PS, the combination of TACE, tumor number, PVTT, EHM, A/G, log (PLR), log (ALT), log (AFP), ALBI class and Child-Pugh score (Figures 3A, B). A stepwise backward regression was then used to refit the model and the Akaike Information Criterion (AIC) minimum was selected as the final model (Supplementary 1). We screened independent predictors of ICI including ECOG PS, combination of TACE, EHM, log (PLR), log (ALT), log (AFP), Child-Pugh score (Supplementary 2), and established nomogram to predict the 1-year and 2-years survival probability of patients (Figure 3C). In addition, to facilitate the application of this model, we construct an online tool: https://xiaomili.shinyapps.io/DynNomapp/.

Figure 3 | Result of Lasso regression (A, B) and nomogram for combination regimen for unresectable hepatocellular carcinoma (C).

In all study cohorts, we plotted ROC curve for the model, which had higher AUC value (0.777, 95% CI 0.704 – 0.850) at 1 year and (0.772, 95% CI 0.690 – 0.855) at 2 years, indicating better predictive performance of the nomogram (Figures 4A, B). The calibration curve of this model showed good agreement between prediction and observation in survival probability (Figures 4C, D). According to the DCA curve, the model predicted a clear net benefit of patients compared to “none” or “full”, and the clinical utility was better (Figures 4E, F). In addition, cross-validation showed that the model predicted a median AUC value of 0.7613 (95% CI, 0.7044–0.8185) for 1-year survival and 0.7520 (95% CI, 0.6948–0.8141) for 2-year survival, again demonstrating its discriminant ability (Figures 4G, H). Finally, we calculated the C-index for this predictive model, which has a value of 0.7405.

Figure 4 | Receiver operating curve (A, B), calibration curve (C, D), decision curve analysis (E, F) and cross-validation of AUC (G, H) for model predicting survival at 1 year and 2 years.

Risk stratification

The nomogram integrated seven variables and assigned a score to each predictor, and the total score was calculated according to the actual situation of the patient. And the corresponding survival probability scale was the survival probability of HCC after combination regimen. To visually demonstrate the power of the constructed model, we stratified the total patient score using X-tile software with cutoff values of 182.7 and 240.3. Thus, the study cohort was divided into three risk groups, the high-risk group consisted of 36 patients and 23 deaths, with a mOS of 7.58 (95% CI, 4.9 – 15.1) months; the medium-risk group consisted of 82 patients and 37 deaths, with a mOS of 17.50 (95% CI, 14.1 – 29.0) months; and finally, the low-risk group included 140 patients and 37 deaths, with a mOS of 53.17 (95% CI, 40.0 – not reached (NR)) months. Kaplan-Meier among the three groups was shown in Figure 5, with significant differences between groups (P < 0.0001).

Figure 5 | Kaplan-Meier curves for overall survival between the high-, medium- and low-risk groups.

Discussion

The advent of ICIs has greatly improved the prognosis of a variety of solid tumors, including HCC, and has shown synergy in combination with anti-angiogenic agents (16). All patients in our study received a combination of ICIs and anti-angiogenic drugs, and the mOS was as long as 30.2 months, far better than mono-PD-1 inhibitor or mono-TKI in previous studies. Considering the etiological differences between HCC patients worldwide and China, the response to the combination regimen of TKIs plus PD-1 inhibitors will also be different. Therefore, we need to construct a prognostic model of unresectable HCC in Chinese populations, in whom the main etiology was HBV infection, which is different from HCV-, alcohol- or NAFLD- related HCC. However, there is an urgent need in the era of precision medicine to explore reliable predictors and identify subgroups of patients who may benefit from the combination therapy. In this study, we included seven variables and constructed a prognostic model in advanced HCC, with good discrimination, calibration, and applicability. The patient population was divided into three subgroups, with mOS of 53.17, 17.50, and 7.58 months in the low-, medium-, and high- risk groups, respectively, and internal validation again demonstrated its accuracy.

Because there are differences in treatment response to the above combination regimen, the identification and integration of molecular markers is currently a problem to be addressed. Nivolumab tends to improve survival and response in HCC with PD-L1 expression ≥ 1%, but heterogeneity of PD-L1 expression and diversity of detection methods limit its utility (19, 34). High TMB status is a predictive biomarker for second-line treatment with pembrolizumab in advanced solid tumors, but genomic instability makes detection difficult (20, 35). Also, neither PD-L1 expression or TMB were determined to predict the efficacy of mono-PD-1 inhibitors. In the era of comprehensive treatment, it is important to select HCC that may benefit from ICIs combined with TKIs by identifying biomarkers and translate into clinical applications.

As one of the common forms of prediction models, nomograms are widely used in tumor prognosis assessment (36). The mechanism of advanced HCC is complex. Its survival is not only associated with patient characteristics, but also with tumor features including tumor immune microenvironment and genic alterations following treatment. We used Lasso regression and multivariate Cox regression to screen variables, and the potential predictors included ECOG PS, combination of TACE, EHM, PLR, ALT, AFP, and Child-Pugh score.

BCLC stage is a commonly used stage in clinical practice, and ECOG PS, EHM and Child-Pugh score included in our model are important components (37). ECOG PS is an important independent prognostic factor for anti-PD-1 therapy in advanced HCC, which is consistent with the results presented in our study (38). Patients with extrahepatic metastasis were defined as BCLC stage C and the difference in organ tumor microenvironment made their different response. As the most common site of extrahepatic metastasis, lung metastasis has been shown to respond best to treatment in two studies (39, 40). Unfortunately, we did not record specific metastatic sites and further exploration of specific responses in different organs is warranted. Most clinical trials have included only Child-Pugh class A patients to minimize the impact of liver dysfunction on outcomes, whereas we have also investigated Child-Pugh calss B patients. Unlike BCLC stage, we divided according to Child-Pugh score, adding more detail information, thereby improving the accuracy of the model.

Although BCLC stage is widely used in clinical practice, there is a lack of treatment history and inflammatory markers. Other treatment modalities in addition to systemic therapy also affect the survival of HCC, and a small sample study found that TACE tends to delay HCC progression, although no randomized intervention was performed (38). The sequence and time interval between TACE and ICIs are currently inconclusive. The heterogeneity of the tumor microenvironment makes treatment responses different, systemic inflammatory responses play an important role in immune monitoring and treatment assessment (41). NLR and PLR, as the most commonly used inflammatory markers, have been shown to be independent predictors of HCC (42–44). However, no prognostic effect of NLR was found in our study, whereas PLR was included in the model as a continuous variable. High-affinity lymphocytes have antitumor activity and are associated with cancer immune escape (45). Although advanced HCC is often accompanied by thrombocytopenia, platelets can promote the formation of tumor metastasis (46). Therefore, platelet binding to lymphocytes is predictive of HCC with combination regimen, and tends to predict a poor prognosis. In addition, we report the association of other nutritional inflammatory markers (LMR, PNI, and SII) with prognosis, but no significant correlation was found.

Immunotherapy related hepatotoxicity is one of the immune-related AEs and often manifests as an increase in ALT or AST (47). We have found that elevated ALT levels before ICI also predict shorter survival, therefore, management of liver function is challenging. AFP is one of the markers of HCC, and elevated AFP is associated with progression and survival (48). Ramucirumab is used in HCC previously treated with sorafenib and is currently the only agent to select patient populations based on AFP (10). We found that AFP levels were negatively correlated with HCC prognosis treated with ICIs and TKIs and could be used as potential markers to predict patient survival.

The above indicators provide clinicians with direct information on whether to use combination therapy and maximize drug efficacy. Our model contains not only important components of BCLC stage, but also liver function, tumor and inflammatory markers, as well as treatment history. Multiple rather than single prognostic factors were included in the study, which is more comprehensive and optimizes its predictive performance. In addition, the above indicators are routine clinical examinations, non-invasive and easy to obtain.

Limitations of this study are: first, the ICIs and TKIs received by patients are heterogeneous, and no studies have yet demonstrated differences in efficacy among different PD-1 inhibitors; second, there is an inevitable selection bias in the retrospective design, we did not perform stratified analysis according to genes or proteins, and lack predictive analysis of tumor molecular mechanisms for immune prognostic models; finally, the patient population comes from a single center and lacks external validation in other regions, and etiological differences between regions are also non-negligible factors. Additional potential predictors need to be explored prospectively in large multicenter studies in the future, and appropriate subgroups of patients have been selected.

Conclusion

We constructed a prognostic model for predicting ICIs combined with anti-angiogenic agents for unresectable HCC. The included indicators are comprehensive and simple, and the model has good performance, realizing individualized prediction of patients. The model may predict the efficacy of the combination regimen for advanced HCCs.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving human participants were reviewed and approved by the ethics review committee of Beijing Ditan Hospital, Capital Medical University. The patients/participants provided their written informed consent to participate in this study.

Author contributions

Study conception and design, JC and WL. Conception, data collection, assembly of data, project administration, and manuscript preparation, XL and WS. Data collection, data analysis, and manuscript review, XD. All authors contributed to the article and approved the submitted version.

Acknowledgements

We thank all participants for their endeavor and contribution to this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2022.1060051/full#supplementary-material

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ORIGINAL RESEARCH
published: 05 December 2022
doi: 10.3389/fimmu.2022.974241

Overexpression of SMS in the tumor microenvironment is associated with immunosuppression in hepatocellular carcinoma

Lin Xiang ¹†, Longhuan Piao²†, Dong Wang ³ and Li-Feng-Rong Qi ⁴*

¹ Department of Translational Medicine Research Institute, Jiangsu Yifengrong Biotechnology Co., Ltd., Nanjing, Jiangsu, China, ² Department of Orthopedic Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China, ³ Department of Histology and Embryology, Binzhou Medical University, Yantai, Shandong, China, ⁴ State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, Jiangsu, China

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School, United States

Reviewed by:
Leire Pedrosa, Institut de Recerca Biomèdica August Pi i Sunyer (IDIBAPS), Spain
Matthew McMillin, University of Texas at Austin, United States

*Correspondence:
Li-Feng-Rong Qi
qilifengrong@163.com

†These authors have contributed equally to this work and share first authorship

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 21 June 2022

Accepted: 02 November 2022

Published: 05 December 2022

Citation:
Xiang L, Piao L, Wang D and Qi L-F-R (2022) Overexpression of SMS in the tumor microenvironment is associated with immunosuppression in hepatocellular carcinoma. Front. Immunol. 13:974241. doi: 10.3389/fimmu.2022.974241

Disorders of polyamine metabolism may contribute to the development of hepatocellular carcinoma (HCC), but the precise mechanism remains unknown. This study reports that spermine synthase (SMS), an enzyme involved in polyamine biosynthesis, is overexpressed in HCC and not associated with hepatitis virus infection in HCC patients. The results of analyzing the clinical data of HCC patients showed that SMS level as a categorical dependent variable was related to clinicopathological features of poor prognosis. Furthermore, the Kaplan-Meier survival analysis and ROC curve indicated that increased SMS level is associated with poor survival rate in HCC and may be a potential biomarker to discriminate HCC tissues. However, SMS overexpression limited the therapeutic effect of immune checkpoint blockade (ICB), which seemed to be related to the immunosuppressive effect of the HCC immune microenvironment formed by higher mRNA transcript levels of immune checkpoints and higher infiltration levels of immunosuppressive cells. In samples with high and low SMS expression, functional enrichment analysis of the differentially expressed genes (DEGs) showed that SMS may be linked to the occurrence and development of HCC by affecting a variety of immune-related pathways, such as Intestinal immune network for IgA production, Fc gamma R-mediated phagocytosis, Antigen processing and presentation, Th1 and Th2 cell differentiation. Subsequently, analysis of the co-expression network of SMS in the liver hepatocellular carcinoma (LIHC) cohort revealed that SMS has a broad impact on multiple important immune- and metabolic-related processes in HCC. In summary, SMS is a promising biomarker to differentiate the prognosis, immune characteristics, and holds promise as a potential target for ICB therapy to improve HCC.

Keywords: hepatocellular carcinoma, spermine synthase, immune checkpoint blockade (ICB), polyamine metabolism, tumor immune microenvironment (TIME)

Introduction

Liver cancer is a great health problem around the world and is estimated to affect more than 1 million individuals annually (1). Hepatocellular carcinoma (HCC), the most prevalent primary liver cancer, occurs easily in the context of chronic liver disease due to hepatitis virus infection, heavy alcohol intake or metabolic syndrome (2). Because of the limitations of early diagnosis technology, many HCC patients are confirmed as an advanced period at the initial diagnosis, losing the opportunity for healing by surgery or ablation (3). Currently, immune checkpoint blockade (ICB) has been admitted as an encouraging treatment modality for advanced HCC patients. Although ICB targeting the PD-1 and CTLA-4 have been approved for second-line therapy of HCC (4–6), response rates to ICB therapy only ranged from 15% to 30% (7–9). Because an immunosuppressive tumor microenvironment is promoted by the tumor cells, the infiltrating stromal and immune cells. Moreover, the capability of hepatoma cells to escape immune surveillance and potentially resist ICB treatment is likely due to the further enhancement of immunosuppression by tolerogenic liver environment (10). At present, the lack of clinically available biomarkers to assess response to ICB limits the efficacy and narrows the range of patients who could get help from ICB. Therefore, there is a pressing demand to develop effective biological targets to enhance the efficacy of ICB in HCC.

Liver has a central position in the metabolism of amino acids that contributes to biochemical processes indispensable for cell proliferation (11). There is a significant change of amino acids concentrations in specific bio-fluids from patients with liver diseases (12, 13). Recent studies have also shown the alterations of particular amino acids in diagnosed HCC patients (14–17).

In this study, we identified an amino acid metabolism-related hub gene, Spermine Synthase (SMS), an important regulator of polyamine metabolism, which was related to the prognosis of HCC patients from TCGA. Moreover, we investigated the effect of SMS expression on response to immunotherapy and analyzed its relationship with the degree of immune infiltration and gene transcription at immune checkpoints. Furthermore, we assessed SMS expression in the immune microenvironment in publicly available single-cell transcriptome sequencing data sets for HCC. Finally, we analyzed the differentially expressed genes (DEGs) in the different SMS expression samples and co-expression gene of SMS to explore the precise mechanisms of SMS in HCC. Our data implied that SMS is an encouraging biomarker to distinguish the prognosis, immune characteristics, and might act as a potential target for ICB therapy improvement in HCC.

Materials and methods

Identification of amino acid-metabolism related hub genes in TCGA-LIHC

In the GEPIA2 (18) (http://gepia2.cancer-pku.cn/) database, ANOVA and LIMMA methods were adopted to study the tumor and paired normal samples in the TCGA-LIHC dataset, respectively, to obtain a list of DEGs (with the |Log2FC| > 1 and q-value < 0.01). Intersection DEGs of the two lists above were screened out by Venn analysis. The comprehensive list of amino acid and derivative metabolic process was obtained from the Molecular Signatures Database v7.5.1 (MSigDB) (19–21) (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). Genes were annotated by the GO term GO:0006519. Intersection AAMRHGs of two gene lists above were screened out by Venn analysis. All hub genes made out by multivariate Cox regression analysis were conducted in TIMER2.0 (22–24) (http://timer.cistrome.org/).

RNA sequencing data collection and analysis

RNA-sequencing expression (level 3) profiles and relevant clinical data for LIHC were obtained from the TCGA dataset (https://portal.gdc.com). The current-release (V8) GTEx datasets were downloaded from the GTEx data portal website (https://www.gtexportal.org/home/datasets). HCCDB database (25) (http://lifeome.net/database/hccdb) was also used in SMS expression analysis in HCC. The mRNA and Protein level of AAMRHBs in HCC and normal tissues were tested at “TCGA analysis” and “CPTAC analysis (26)” module of UALCAN (27) (http://ualcan.path.uab.edu/analysis-prot.html), respectively. In addition, cell line mRNA expression matrix of tumors was from the Cancer Cell Line Encyclopedia (CCLE) dataset (28) (https://portals.broadinstitute.org/ccle), which was used to validate AAMRHGs expression in cancer cell lines.

Evaluation of the AAMRHGs prognostic signature

The HCC patients in this study were separated to two groups with high and low gene expression. The basis for the demarcation is the median value of gene. The clinical data analized by single-gene binary logistic regression was performed with AAMRHGs as the independent variable, low expression as the reference, and clinical characteristics as the dependent variable. The prognostic data for HCC patients were downloaded from the TCGA dataset.

The Kaplan-Meier (KM) survival curve analysis of overall survival (OS), disease-specific survival (DSS) and progression free survival (PFS) were executed by R package Survival and Survminer. timeROC (29) was adopted to analyze time-dependent receiver operating characteristic (ROC).

Univariate and multivariate cox regression analysis (29) were applied to determine the proper terms to establish the nomogram. Statistics of each variable were processed by “forestplot” R package to form forest map.

A nomogram was developed based on the results of multivariate cox proportional hazards analysis to predict the X-year overall recurrence. The nomogram provided a graphical representation of the factors which can be used to calculate the risk of recurrence for an individual patient by the points associated with each risk factor through ‘rms’ R package (30).

ROC curve analysis was executed by GraphPad Prism Version 9 to assess the discriminatory accuracy of SMS expression in predicting evaluated the extent to which they could separate each tumor entity from the normal tissue.

Prediction of the therapeutic effect of ICB and analysis of immune function

We used TIDE (http://tide.Dfci.harvard.edu/) to evaluate the potential clinical efficacy of immunotherapy in different SMS overexpression groups (31, 32). We selected SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3 and PDCD1LG2 (33–36) as immune-checkpoint-relevant transcripts and extracted expression values of these genes from two groups. Immune infiltration level results between two groups were evaluated by immunedeconv (37) which integrates 6 latest algorithms, including TIMER (24), CIBERSORT (38), quanTIseq (39), xCell (40), MCP-counter (41) and EPIC (42). The results were obtained by TIMER2.0 (5). Results (p<0.001) were visualized by the Pheatmap package. Moreover, we used “gsva” package of ssGSEA to perform quantitative analysis of the immune cells and pathways between the high- and low-expression groups. 28 immune cell subpopulations were collected from the literature (43).

Immune microenvironment analysis based on scRNA-seq expression

Single-cell RNA sequencing (scRNA-seq) was adopted to reveal the distribution and expression of AAMRHGs in the immune microenvironment of HCC. The data (GSE98638, GSE125449, GSE140228) was obtained from scTIME Portal (44) (http://sctime.sklehabc.com/), a database and a portal for single cell transcriptomes of tumor immune microenvironment. The portal implemented tumor immune microenvironment specific analysis modules to allow the data exploration. GSE98638 contains data obtained by deep single-cell RNA sequencing on single T cells isolated from peripheral blood, tumor and adjacent normal tissues from hepatocellular carcinoma patients. GSE125449 contains data obtained by single-cell transcriptome profiling of liver cancer biospecimens from nine hepatocellular carcinoma and ten intrahepatic cholangiocarcinoma patients. GSE140228 contains data obtained by single-cell transcriptome profiling of CD45⁺ immune cells for HCC patients from five immune-relevant sites (tumor, adjacent liver, hepatic lymph node (LN), blood, and ascites).

Functional enrichment analysis

The limma package in the R software was adopted to study differentially expressed mRNA, threshold for which was set with reference to adjusted p < 0.05 and |Log2FC| > 1, between high- and low- expression groups. “ClusterProfiler (45)” package was adopted to carry out Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of co-expression genes. The GSEA was carried out using WebGestalt (WEB-based Gene Set Analysis Toolkit is a functional enrichment analysis web tool) (46). Then GSVA (47) was performed on Hallmark annotation libraries in MSigDB respectively to obtain the matrix of samples and pathways. Limma package was used for difference analysis between the high- and low- expression group. 19 important signaling pathways were collected from the literature (48), and the ssGSEA algorithm was adopted to predict the score of the corresponding pathway of each sample through the expression matrix, and then grouped according to the level of gene expression.

Co-expression networks analysis

To identify gene co-expressed with the AAMRHGs, the LinkedOmics tool (49) (http://www.linkedomics.org) was adopted to calculate the Pearson’s correlation coefficient between AAMRHGs and others. Top 20 positively and negatively correlated genes were adopted to analyze their effect on OS of HCC. Top 50 positively and negatively correlated genes were used for GO and KEGG enrichment analyses.

Statistical analysis

Statistical analyses were carried out using R version 4.0.3 and GraphPad Prism 9.0. Statistical differences of two groups and three groups were compared through the Wilcox test and Kruskal-Wallis test, respectively. Wilcoxon rank-sum test was adopted to analyze SMS expression in non-paired samples. Wilcoxon rank signed test was adopted to analyze SMS expression in paired samples. The log-rank test was adopted to analyze the differences between survival curves. p < 0.05 was considered statistically significant.

Results

Identification of amino acid and derivative metabolic process related genes from TCGA-LIHC and GEO databases

This research was carried out in accordance with the flow chart exhibited by Figure 1. 2207 and 3195 DEGs between the tumor and normal groups were identified in the TCGA-LIHC gene expression profiles obtained by ANOVA and LIMMA algorithms, respectively. Furthermore, 607 genes that overlap in two algorithm groups were shown by a venn diagram. And then, intersection of 607 DEGs and AAMGs (GO:0006519) yielded 3 amino acid and derivative metabolic process related differentially expressed genes (AAMRHGs), included in the subsequent functional classification analysis (Figure 1). Single gene or a gene group suitable for research was determined by multivariate Cox regression analysis in Tumor Immune Estimation Resource 2.0 (TIMER2.0) Database. Finally, SMS was selected (Extended Data Figures S1A, B).

Figure 1 | Flow chart of identifying target genes for study. GEPIA2 picture is from http://gepia2.cancer-pku.cn/.

Upregulation of SMS in HCC

We compared SMS expression in 371 HCC tumor tissues and 276 normal tissues (50 in TCGA-LIHC dataset and 226 in GTEx dataset) by Wilcoxon rank sum test. The expression levels of SMS were markedly higher in tumor tissues compared to unpaired (p < 0.001; Figure 2A) and paired (p < 0.001; Figure 2B) normal tissues, respectively. To clarify the transcriptional and translational levels of SMS in HCC tissues, we carried out CPTAC analysis of UALCAN and analysis of 11 HCC study cohorts in the HCCDB database, respectively. We found that both the protein (Figure 2C) and mRNA (Figure 2D) expressions of SMS were pronouncedly elevated in HCC tissues relative to normal tissues. There were also consistent trends in the mRNA and protein levels of SMS in pan-cancer tissues by analysis of the pan-cancer study cohorts of UALCAN (Extended Data Figures S2A, B). As revealed by Cancer Cell Line Encyclopedia (CCLE) analysis, SMS expression in Liver cancer cell lines were higher than the average level of pan-cancer cell line (Extended Data Figures S2C, D). These results illustrate a strong association between SMS upregulation and HCC tumorigenesis.

Figure 2 | Upregulation of SMS in HCC. (A) SMS mRNA expression in 371 HCC samples (371 in TCGA-LIHC) and 276 normal samples (50 in TCGA-LIHC dataset and 226 in GTEx dataset). (B) SMS mRNA expression in 50 HCC and paired normal samples. (C) SMS protein expression based on CPTAC. (D) Comparison of the transcriptional level of SMS between HCC tissues and paired normal tissues in HCCDB. ****p < 0.0001 vs. indicated control.

Evaluation of the prognostic relevance of SMS in HCC

To make clear the role and prominence of SMS expression, the clinicopathological features of HCC patients with different SMS expression were investigated (Table 1). Contrast to the low expression of SMS group, patients in the high expression of SMS group had a significantly higher ratio of more severe primary tumor (T) stage, more severe lymph nodes (N) stage, more severe metastasis (M) stage, worse Pathologic stage, Tumor status, and higher Alpha-fetoprotein (AFP), all which with significant difference.

Table 1 | Clinical characteristics of HCC patients in TCGA database.

The univariate analysis with Logistic regression demonstrated that SMS expression was related to poor prognostic clinicopathological features (Table 2). Elevated SMS expression in HCC is positively associated with T stage, Pathologic stage, Histologic grade, Adjacent hepatic tissue inflammation, AFP and Fibrosis ishak score, meanwhile negatively associated with Race, Weight and Height significantly (all p < 0.05).

Table 2 | Single-gene binary logistic regression.

Next, Kaplan-Meier survival analysis illustrated that patients in the high expression of SMS group had remarkably shorter overall survival [OS, n=370, HR=2.090(1.477-2.960), log-rank P<0.001], disease-specific survival [DSS, n=362, HR=1.846(1.180-2.887), log-rank P=0.0056] and progression free survival [PFS, n=370, HR=1.436(1.070-1.928), log-rank P=0.0145] contrast to the low expression of SMS group in HCC (Figure 3A). To examine the ability of SMS to predict HCC development, through time-dependent ROC analysis, we found that SMS could predict the OS, DSS and PFS of HCC patients at 1, 3, and 5 years to a certain extent, whose AUC were mostly between 0.6-0.8 (Figure 3B). Univariate and multivariate Cox regression analysis were used to assess the independent prognostic value of 6 clinicopathological variables, including SMS, Age, Gender, pT stage, pTNM stage and grade, in terms of OS of HCC. SMS and pT stage were independent prognostic markers for OS (Figure 3C). Then we adopted a nomogram model for estimating these clinicopathological variables. The concordance index for this nomogram was 0.703 (0.657-0.75) (Figure 3D). Furthermore, SMS expression indicated discriminative effect with an AUC value of 0.956 (CI = 0.944-0.973) to identify tumors from normal tissue (Figure 3E). The result showed that SMS could be a heartening biomarker to discriminate HCC tissues. Those results imply that SMS expression foresees detrimental outcomes and is related to disease development in HCC patients.

Figure 3 | Differential SMS expression as a prospective biomarker of poor prognosis and to discriminate HCC samples. (A) OS, DSS and PFS of SMS mRNA in HCC cohort. (B) Time-dependent ROC analysis of SMS expression in HCC. (C) Forrest plot of univariate and multivariate Cox regression analysis in HCC. (D) Nomogram of SMS and other prognostic factors in HCC. (E) ROC curve assessing the performance of SMS for HCC diagnosis.

Correlation of SMS expression with ICB response and immune infiltration in HCC

Immunotherapy has revolutionized treatment of cancers. Although a subset of patients with HCC benefit from ICB therapy, many patients do not achieve significant benefit. We next examined whether SMS expression could predict ICB clinical response by Tumor Immune Dysfunction and Exclusion (TIDE) analysis. The result displayed a higher median TIDE score in SMS overexpression group, indicating a weakened ability to respond to ICB (Figure 4A). In addition, TIDE prediction score is also negatively correlated to anti-PD1 and anti-CTLA4 treatment effects. This prompted us to explore the relationship among SMS expression, immune checkpoint, and immune cell infiltration level. The mRNA transcription levels of immune checkpoints were generally increased in HCC with the higher SMS expression, suggesting that more intense immunosuppression was prevalent in HCC with higher SMS expression (Figure 4B).

Figure 4 | ICB immunotherapy response predict and Immune infiltration analysis. (A) Differences in TIDE prediction score between the indicated groups. (B) Expression of immune checkpoints between the indicated groups. (C) Heatmap of immune responses between the indicated groups. (D) ssGSEA immune cells scores between the indicated groups in boxplots. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001 vs. indicated control, ns, no significance.

The number and proportion of infiltrating immune cells play a principal role in cancer development and immunotherapy response, and thus correlate with patient prognosis. The association between SMS expression and immune infiltration in HCC was shown in the heatmap, which was analyzed by the R package immunodeconv (p<0.001, Figure 4C). Comparative analysis of immune cells affirmed that there were differences in various immune cells between two groups, such as Immature B cell, Immature dendritic cell, Macrophage, etc. (Figure 4D).

The results insinuate that strong immunosuppressive effect in the tumor microenvironment is a prerequisite for poor prognosis and tumor development in SMS-overexpressing HCC patients. In summary, SMS overexpression might affect immune cell infiltration in HCC patients, resulting in poor ICB response and efficacy against HCC.

SMS expression in the tumor immune microenvironment of HCC

To understand the impact of SMS gene expression on the tumor immune microenvironment, we acquired 3 HCC single-cell transcriptome datasets in the scTIME Portal (http://sctime.sklehabc.com/) and analyzed them in each dataset. Cells expressing SMS gene were red, while cells lacking SMS gene were gray (Figure 5A). In dataset GSE98638, compared with immune microenvironments derived from other tissues, T cell exhaustion and inflammatory senescence-related cells in tumor tissue had a higher positive rate of SMS expression (Figure 5B). In dataset GSE125449, malignant tumor cells and TAM tumor-associated macrophages were more positive than other cells in the immune microenvironment (Figure 5C). In dataset GSE140228, compared with immune microenvironments derived from other tissues, there were differences in SMS-expressing cells in the outer edge of tumor tissue, especially macrophages. In particularly, there were tumor-derived macrophages that express SMS in ascites fluid, which may be partly associated with cancer metastasis (Figure 5D). Overall, SMS expression is associated with a stronger tumor immunosuppressive effect in tumor immune microenvironment of HCC.

Figure 5 | SMS expression within the tumor immune microenvironment of HCC. (A) UMAP annotated with cell types (Up panel) and colored by SMS positive cells (red) and none (gray) (Down panel). (B–D) Heatmaps of SMS gene expression positivity rates for each immune cell subset in GSE98638 (B), GSE125449 (C), and GSE140228 (D).

Differential gene expression profiles comparing SMS-low and SMS-high in HCC

To exclude the effect of hepatitis infection on SMS expression, we examined the SMS expressions of tumors among the HCC patients with or without viral hepatitis in the TCGA-LIHC data set. There was no correlation between SMS expression and hepatitis B virus or hepatitis C virus infection (Figure 6A).

Figure 6 | Functional analysis of DEGs in HCC patients with distinct SMS levels. (A) SMS expression in HCC patients with or without Hepatitis virus infection and in normal liver tissues. (Left, HBV; Right, HCV) (B) The heatmap of differential gene expression between the indicated groups. (C) Volcano plots of the gene expression profile data. (D) Up- and down-regulated genes were analyzed for GO terms and KEGG pathway. (E) GSEA of the high and low SMS expression clusters. (F) ssGSEA scores of 19 functional pathways related to cancer between the indicated groups. *p<0.05, **p<0.01, ***p<0.001, ****p < 0.0001 vs. indicated control, ns, no significance.

To find out the underlying mechanisms of SMS that facilitate tumor development, we analyzed DEGs in the high- and low-SMS expression samples. Microarray data was standardized by limma package. The clustering analysis and expression of DEGs were shown in a heatmap (Figure 6B) and a Volcano Plot (Figure 6C), respectively. According to the results of Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we found that the DEGs were mostly enriched in chromosome segregation, mitotic nuclear division, organic and carboxylic acid biosynthetic process, etc. (Figure 6D).

Further, through GSEA by the WebGestalt, we assessed the downstream pathways for SMS. We found immune-related pathway enrichment results through KEGG analysis under the Functional Database option, such as Intestinal immune network for IgA production, Fc gamma R-mediated phagocytosis, Antigen processing and presentation, Th1 and Th2 cell differentiation. (Figure 6E). The results suggest that the abnormal expression of SMS may participate in the formation of immunosuppressive function in the immune microenvironment of HCC through these immune-related pathways.

In order to determine the relationship between SMS and cancer-related functional pathways, we calculated the functional pathway scores of 19 cancer-related functional pathways between the high and low expression groups of SMS by ssGSEA algorithm. As the results show, Tumor inflammation signature was similar between two groups. However, in SMS-high expression group, there were significant more active in tumor-related signaling pathways, such as Cellular response to hypoxia, Tumor proliferation signature, EMT markers, etc. (Figure 6F). These results suggest that SMS also may affect the occurrence and progression of HCC by regulating a variety of important tumor-related signaling pathways.

Co-expression networks of SMS in HCC

Analyzing the co-expression network of SMS in the LIHC cohort by Linkedomics, we expected to reveal gene interactions and thereby mine SMS gene function. There were 12,073 genes and 7,842 genes that had significant positive and negative correlations with SMS, respectively (false discovery rate, FDR < 0.01) (Figure 7A). Then, from the above two correlation groups, the top 50 genes were selected to make a heat map display (Figure 7B). SMS expression was extremely positively correlated with ACOT9 (positive rank #1, r = 0.568, p = 3.38E-32), EIF2S3 (r = 0.560, p = 3.65E-31) and TUBA1C (r = 0.512, p = 1.66E-25).

Figure 7 | Genes co-expressed with SMS in HCC. (A) Co-expressed genes with SMS in HCC. (B) Indicated co-expressed genes co-transcript with SMS in HCC. (C) Indicated co-expressed genes with SMS were analyzed for GO terms and KEGG pathway.

Next, we assessed high- or low-risk genes by hazard ratios (HR) in the HCC overall survival analysis. The results showed that the top 20 positively correlated co-expressed genes and the top 20 negatively correlated co-expressed genes may be high-risk genes and low-risk genes of HCC, respectively (p < 0.05, Table 3). Therefore, SMS is likely to promote HCC progression by regulating the risk factors of HCC.

Table 3 | Overall survival analysis of the top 20 genes positively and negatively correlated with SMS in HCC.

Finally, the top 50 positively correlated genes and the top 50 negative correlated genes were used for GO and KEGG enrichment analysis respectively. The result indicated that SMS co-expressed genes were chiefly related to neutrophil immune response pathway and organic matter catabolism process (Figure 7C). KEGG pathway analysis indicated that co-expressed genes were chiefly enriched in the Salmonella infection and Valine,leucine and isoleucine degradation (Figure 7C). These results suggest that SMS may be involved in immune-related processes in HCC.

Discussion

Liver is the principal organ of amino acid metabolism. Alterations in amino acid metabolism are characteristics of HCC (50). In the present study, we screened an amino acid metabolism-related SMS gene in TCGA-LIHC dataset. SMS catalyzes the final step in the production of spermine from arginine and plays a key role in maintaining normal spermine levels (51, 52). Disordered expression of SMS has been found in colorectal cancer and breast cancer (53, 54). Analysis of multiple cancer clinical research datasets showed that aberrant SMS expression is a common phenomenon in most cancers. In this research we focused on liver cancer. Based on in-depth mining of RNA-seq data combined with relevant clinical information of HCC, we observed that the elevated SMS expression was related to poor prognosis of HCC patients. Moreover, this effect of SMS was independent of other factors through Single-gene binary logistic regression analysis and COX regression analysis. According to the timeROC curve analysis, we believed that SMS expression could help to infer the survival rate of HCC patients. Meanwhile, SMS expression raised with HCC tumor stage development, implying that SMS is related to HCC development. Furthermore, when assessing the sensitivity and specificity of SMS expression in predicting HCC tissue from normal tissue by ROC curve, it was found that the predictive capability of variable SMS was accurate in predicting tumor and normal prognosis (AUC = 0.959, CI = 0.944-0.973). Taken together, we speculate that the high expression of SMS in HCC patients would cause spermine accumulation, which is confirmed in the urine and plasma of cancer patients (55). Therefore, SMS might be employed as prospective biomarkers for diagnosis and prognostication of clinical consequences for HCC.

Owing to the limitations of HCC diagnostic techniques, patients often lose the opportunity for thoroughgoing treatment such as surgery or liver transplantation (3). Therefore, ICB therapy gives hope of cure for patients with advanced HCC (10). To evaluate the effect of SMS expression on the efficacy of ICB, we used the TIDE algorithm and discovered that TIDE score increased in the high-expressing SMS group. TIDE score is positively correlated with the incidence of immune escape, which may account for the poor response to ICB therapy (32). Through public data mining, we found that mRNA transcription level of immune checkpoints was generally increased in HCC with the higher SMS expression. Moreover, SMS expression was shown to be associated with HCC immune infiltration. Evidence suggests that SMS can be a potential biomarker for predicting response to ICB therapy.

Various stromal cells, cytokines, chemokines, etc. in the tumor microenvironment can regulate tumor development and have become potential therapeutic targets. Using publicly available hepatocellular carcinoma single-cell transcriptomic data analysis, we assessed the distribution of SMS expression in the immune microenvironment of HCC. We discovered that immune cells with immunosuppressive effects in the immune microenvironment of HCC have a higher proportion of SMS-positive cells. Especially, liver promotes immune tolerance, preventing antigenic overload of certain substances absorbed from the gut (56). This study shows that a higher proportion of SMS-positive immune cells with immunosuppressive effects may promote the formation of stronger immunosuppressive environment in HCC, which is defined as “cold tumor” (57). Some SMS inhibitors could significantly enhance the therapeutic efficacy of polyamine depletion approaches, particularly in spermine-rich tumors (58–60). Tumor cells can consume a large amount of arginine in the tumor microenvironment, resulting in the lack of arginine in the tumor microenvironment, and the activation of anti-tumor immune cells will inevitably be inhibited (61). Therefore, inhibition of SMS to prevent arginine degradation in the tumor microenvironment is an attractive strategy to reactivate immune responses. Above findings indicate that inhibit the expression of SMS could result to the advancement of therapeutic strategies to weaken immunotherapy resistance in HCC and enhance checkpoint blockade therapy efficacy.

To get a full understanding about activity of SMS in HCC, we used TCGA-LIHC data to obtain DEGs by comparing the expression profiles of two groups (high and low expression of SMS). Interestingly, hepatitis virus infection, as a potential factor in the development of HCC, did not affect SMS expression. Furthermore, as indicated by GO and KEGG analysis, we discovered the SMS was correlated with chromosome segregation, mitotic nuclear division, organic and carboxylic acid biosynthetic process, etc.

Moreover, those important immune-related pathway, such as Intestinal immune network for IgA production, Fc gamma R-mediated phagocytosis, Antigen processing and presentation, Th1 and Th2 cell differentiation (Figure 6E), and cancer-related signaling pathways, such as MYC targets, G2 M checkpoint, activated E2F targets and PI3K-AKT-mTOR signaling (Figure 6F and Extended Data Figure S3), are closely related to the expression level of SMS in HCC when tested using GSEA, GSVA and ssGSEA. These results suggest that SMS overexpression is not only related to the immunosuppressive effect of tumor immune microenvironment, but also may be involved in the regulation of multiple tumor-related signaling pathways.

On the other hand, we analyzed genes significantly associated with SMS expression in HCC. We found some genes that were aberrantly expressed and associated with the overall survival of HCC, and these genes might constitute a network regulating the course of HCC with SMS. Pathway enrichment analysis of these genes showed that the regulatory network chiefly facilitated immune cell activity, which may further participate in the formation of immunosuppressive function in the immune microenvironment of HCC.

All data illustrates that SMS expression is widely drawn into the occurrence and development of cancer. In-depth study of the regulation mechanism of SMS expression and its regulatory network will contribute to clarify the pathogenic mechanism and the mechanism of immune escape in HCC.

Although this study expanded our knowledge of the connection between SMS and HCC, there were still some areas for improvement. Above all, the expression of SMS needs to be further verified in clinical samples. Next, the mechanistic details of the involvement of SMS in HCC development remain to be experimentally explored.

In conclusion, this study is the first to find that SMS expression is upregulated in HCC. We believe that SMS could be considered as a potential biomarker of poor prognosis and its expression level could provide a therapeutic reference for related HCC patients. Besides, the role of SMS in the tumor immune microenvironment makes it a potential target for immunotherapy in HCC.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Author contributions

LX and L-F-RQ conceived the project. LP, DW, LX, and L-FRQ provided bioinformatics analysis and wrote the paper with input from all authors. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by Medical and Health Science and Technology Development Project of Shandong Province (2017WS822).

Acknowledgments

We acknowledge Yuan Zhang and Min Wang for critical reading of our manuscript.

Conflict of interest

Author LX is employed by Jiangsu Yifengrong Biotechnology Co., Ltd.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2022.974241/full#supplementary-material

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Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2022 Xiang, Piao, Wang and Qi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 08 December 2022
doi: 10.3389/fonc.2022.1075685

Budd-Chiari syndrome and its associated hepatocellular carcinoma: Clinical risk factors and potential immunotherapeutic benefit analysis

Kang-Shuai Li †, Sen Guo †, Yu-Xin Chen * and Zong-Li Zhang *

Department of General Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School, United States

Reviewed by:
Erbao Chen, Shenzhen Hospital, Peking University¸China
Bingqing Shang, National Cancer Center; National Clinical Research Center for Cancer; Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, China

*Correspondence:
Yu-Xin Chen
chenyuxin_sdu@163.com
Zong-Li Zhang
zzlzzl1900@163.com

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Oncology

Received: 20 October 2022

Accepted: 23 November 2022

Published: 08 December 2022

Citation:
Li K-S, Guo S, Chen Y-X and Zhang Z-L (2022) Budd-Chiari syndrome and its associated hepatocellular carcinoma: Clinical risk factors and potential immunotherapeutic benefit analysis. Front. Oncol. 12:1075685. doi: 10.3389/fonc.2022.1075685

Background

Hepatocellular carcinoma (HCC) is a well-described complication of Budd-Chiari syndrome (BCS). However, the risk factors of BCS in developing HCC and clinical characteristics and imaging features of BCS-associated HCC is still to be determined.

Methods

Data from 113 consecutive patients with primary BCS in Qilu hospital were retrospectively studied. The clinical features of 12 HCC patients associated with BCS were also analyzed. Chi-square analysis was performed to analyze the differences in clinical characteristics. The treatment regime and CT imaging features of BCS-associated HCC were also illustrated.

Results

113 consecutive patients admitted to our hospital between January 2009 and June 2016 with a primary diagnosis of BCS were enrolled. 10.6% (12/113) was diagnosed with HCC. The BCS patients were mainly male gender with an average age of 49.2 years. Symptom duration longer than one year exhibited decreased serum ALT and AST and increased ascites ratio. BCS-associated HCC patients were presented with IVC block and stricture of the hepatic venous outflow tract. Patients with HCC were older and showed elevated serum AST and total bilirubin. Most nodules of HCC located in the right posterior lobe with heterogeneous enhancement during the arterial phase and washout during the delayed phase.

Conclusions

The results indicate that BCS patients with IVC block and stricture of hepatic venous outflow tract seem to be associated with HCC. BCS associated HCC nodules exhibited irregular and heterogeneous enhancement in the arterial phase and washout on the delayed phase.

Keywords: Hepatocellular carcinoma, Budd-Chiari syndrome, malignancy, immunotherapeutic effect, IVC block

Introduction

The Budd-Chiari syndrome (BCS) is a group of disorders characterized by hepatic venous outflow obstruction. (1–4) Obstruction of the hepatic venous outflow tract has been reported to be associated with hepatocellular carcinoma (HCC), the most common form of primary liver malignancy. (5–7) However, the incidence rate of HCC in BCS patients varies from country to country, and the predisposing factors associated with the development of HCC are still to be illustrated. (8) Studies have found that following chronic hepatic congestion, the cirrhotic process can develop HCC in patients with BCS, which still needs further evidence. (9)

Ultrasonography, CT, and MRI, in combination with the clinical manifestations, are often applied for the diagnosis of BCS, although angiography is still the gold diagnosis standard. (10) Besides imaging features, the serum α-fetoprotein (AFP) has also been applied to diagnose and screen HCC. There is an increasing acceptance of the diagnosis of HCC using imaging techniques without a biopsy. (11) Although some researchers reported the imaging features of HCC in patients with BCS, typical images on the obstruction site and HCC nodules are still in need. (12) Also, the invasiveness of nodules in BCS-associated HCC patients still needs to be evaluated.

This study retrospectively investigated a cohort of 113 consecutive BCS patients, including 12 BCS-associated HCC patients. This study aimed to analyze the clinical and imaging features of HCC and evaluate the malignancy of nodules in BCS-associated HCC patients. The potential immunotherapeutic benefits were also discussed.

Materials and methods

We retrospectively studied 113 consecutive patients with a primary diagnosis of BCS who was admitted to our hospital between January 2009 and June 2016. The basic data were acquired by reviewing the patient medical record. The diagnosis of BCS followed the criteria by the European Group for the Study of Vascular Disorders of the Liver. All patients underwent ultrasonography, computed tomography (CT), or angiography to examine the obstruction of hepatic veins, portal veins, or IVC. The diagnosis of BCS was based on imaging showing an obstructed venous outflow tract. The diagnosis of HCC followed the guidelines for diagnosing and treating primary liver cancer in China (2019 Edition) (13). The diagnosis of HCC was based on ultrasonography, CT imaging features, and serum AFP level. Liver nodules were evaluated according to the following imaging features: shape, number, size, location, enhancement on arterial phase, washout on portal venous phase, tumor thrombus of the portal vein, and invasion of the bile duct.

Inclusion criteria of Budd-Chiari syndrome: 1. Previously diagnosed patients; 2. Those admitted to the hospital for treatment with related symptoms of Budd-Chiari syndrome this time; 3. Patients were admitted for other reasons and found-Budd Chiari syndrome during the inspection. Inclusion criteria for Budd-Chiari syndrome with HCC: 1. Those previously diagnosed with Budd-Chiari syndrome, received or did not receive treatment, and were hospitalized for HCC this time; 2. There was no diagnosis of Budd-Chiari syndrome in the past. The patients were admitted for HCC this time and were confirmed to have Budd-Chiari syndrome by systematic examination; 3. The patient was admitted to the hospital for treatment due to symptoms of Budd-Chiari syndrome, and the presence of HCC was confirmed by systematic examination. Exclusion criteria: secondary Budd Chiari syndrome caused by HCC hepatic venous metastasis or inferior vena cava metastasis.

SPSS version 25.0 software was applied for statistical analysis. A Chi-square analysis was performed to analyze the four-fold table data. For cells with theoretical frequency less than 5, Fisher exact test was applied. P<0.05 was considered statistically significant.

Results

Clinical features of 113 patients with Budd-Chiari syndrome

The clinical data of patients with Budd-Chiari syndrome in Qilu Hospital of Shandong University was analyzed, and the results were summarized in Table 1. The average age of the patients was 49.2 ± 13.4 years. After further dividing the age by the interval of ten years, we found that Budd Chiari syndrome occurred in 0-90 years. Among them, the peak of the incidents occurred between 40 and 50 years old, accounting for about 30% of all patients (Figure not shown). Male patients accounted for 65% of the total number of patients. The proportion of new cases with a symptom duration of less than one year and cases with a symptom duration longer than one year is about 1:1. Among the 113 cases, 5 cases (4.4%) carried the hepatitis B virus, and 1 case (0.9%) had the hepatitis C virus. In terms of hepatic venous outflow tract obstruction, inferior vena cava obstruction and mixed obstruction of inferior vena cava, and hepatic vein obstruction were the main types of obstruction, accounting for 44% and 12%, respectively. There were 9 cases (7.9%) with hepatic encephalopathy and 50 cases (44%) with ascites (Table 1).

Table 1 | Clinical data of 113 cases diagnosed with Budd-Chiari syndrome.

To determine the effect of the duration time of hepatic venous outflow tract obstruction on liver function, we further compared the liver function of 113 patients with symptoms lasting less than one year and more than one year (Table 2). The results revealed no significant differences in age, gender, IVC obstruction, and hepatitis B virus infection between the two groups. The positive rates of ALT, AST, and ascites were significantly different between the two groups. The positive rates of ALT, AST, and ascites in patients with symptoms lasting less than one year were higher than those lasting longer than one year. There were no significant differences in other indicators such as bilirubin, albumin, international standard ratio of prothrombin time (PT-INR), platelet, leukocyte, hemoglobin, and incidence of hepatic encephalopathy. Our results indicate that there is apparent hepatocyte damage in the early stage of hepatic venous outflow tract obstruction, and the incidence of ascites is also high due to portal hypertension. For patients with Budd-Chiari syndrome for more than one year, hepatocyte damage and ascites occurrence are relatively low owing to the potential compensatory mechanism.

Table 2 | Comparison of primary clinical data between patients with BCS duration time less than one year and BCS duration time more than one year.

The clinical features of 12 HCC patients associated with BCS are shown in Table 3. Nine (75%) of the 12 patients were male, and 3 (25%) were female. The average age was 54.4 years (35-70 years). BCS was diagnosed with imaging features (ultrasonography or CT). HCC was diagnosed with imaging features (ultrasonography or CT) and serum AFP level in 10 patients and with histological examinations in 2 patients receiving surgical removal of the nodule. The histological findings of the two samples showed hepatocellular carcinoma and cirrhosis in adjacent parenchyma. The mean duration between diagnosis of BCS and HCC was 17 years (range, 3-40 years). Inferior vena cava block alone was found in 83.3% (10/12) of patients, and hepatic vein and IVC block were observed in 16.7% (2/12) of patients. The serum AFP level was normal in 3 patients with HCC (3.8 ng/ml, 7.9 ng/ml, and 10.3 ng/ml, respectively, normal range < 20 ng/ml), whereas the mean level was 3321 ng/mL (range, 30.1-20643 ng/ml) in the other ten patients. Only one patient was HBV positive, and no patient with HCV positive. Hepatic function reserve was evaluated according to Child-Pugh grades. Seven patients were in A level; 2 patients were in B level, while three were in C level, indicating a poor hepatic function reserve.

Table 3 | Clinical features of 12 HCC patients associated with BCS.

To find out the risk factors of liver cancer in patients with Budd-Chiari syndrome, we compared the basic clinical data of 101 patients with Budd-Chiari syndrome and 12 patients with Budd-Chiari syndrome and associated HCC (Table 4). The results showed significant differences in age, location of the obstruction, AST, and bilirubin. Still, there were no significant differences in gender, HBsAg, ALT, albumin, PT-INR, platelet, leukocyte, hemoglobin, hepatic encephalopathy, and ascites. The difference in age between the simple group of Budd-Chiari syndrome and the group of Budd-Chiari syndrome associated HCC indicates that liver cancer generally occurs within a few years after the occurrence of Budd-Chiari syndrome. Namely, liver cancer is related to the presence of Budd-Chiari syndrome. Consistent with the literature reports, our study also found that IVC obstruction is associated with the occurrence of liver cancer, but there is no reasonable explanation at present. There was a marginally significant difference in AST and bilirubin (P = 0.05), which might be affected by the nodules of HCC.

Table 4 | Comparison of clinical data between patients complicated with HCC and not complicated with HCC.

Imaging features of hepatic venous outflow tract obstruction

Different kinds of obstructions in BCS patients complicated with HCC are summarized in Figure 1. Segmental obstruction of IVC (Figure 1A), IVC thrombosis formation (Figure 1B), IVC vascular malformation (Figures 1C, D), as well as compensatory vascular varices with no signs and symptoms (Figure 1E) were observed in our series. Figures 1F, G were CT angiography and a coronal section showing the segmental obstruction of IVC and corrected by stenting, indicating a practical function of CT angiography in diagnosing BCS. IVC enlargement and narrowness of the hepatic part of IVC were also observed in our series. (Figure 1H)

Figure 1 | Different kinds of obstructions in BCS patients are complicated with HCC. (A) The narrowness of IVC in one patient. (B) Thrombosis in one patient. (C, D) IVC vascular malformation in one patient. (E) Compensatory vascular varicosity in one patient with no signs and symptoms. (F, G) CT angiography and a coronal section showing the segmental obstruction of IVC and corrected by stenting in one patient. (H) Ultrasonic imaging in one patient.

Imaging features of hepatocellular carcinoma

The imaging features of hepatocellular carcinoma are shown in Figure 2 and summarized in Table 5. Liver cirrhosis was observed in all patients. The tumor size was 0.9 cm, 7.5 cm, and 9.7 cm for the three patients with normal AFP levels, whereas the mean tumor size was 7.4 cm (range, 3.1-14 cm) in the other seven patients. 2 patients with tumor size over 10 cm were observed with intrahepatic metastasis. And one patient with the largest tumor size was observed with portal vein invasion, while no bile duct invasion was observed. The tumor nodules located in the left lobe of the liver of three patients and the right lobe of the liver of the remaining nine patients. And among these nine patients, five located in the right posterior lobe may indicate an association between the anatomical position and the development of HCC in BCS patients. All patients were single nodules, while 2 of 3 patients with normal AFP levels were multiple nodules.

Figure 2 | HCC tumors in different patients. (A, C, E, G) Arterial phase of HCC tumors in different patients, (B, D, F, H) the according delayed phase of HCC tumors in these patients.

Table 5 | Tumor features of 12 HCC patients associated with BCS.

The largest nodule was selected to evaluate the nodule’s size, location, and shape if the patient had more than one nodule. *: with intrahepatic metastasis. LC: Liver cirrhosis; PV: portal vein; BD: bile duct.

The arterial and delayed phases of tumor nodules are shown in Figure 2. All nodules are located near the edge of the liver. All nodules exhibited irregular and heterogeneous enhancement in the arterial phase. Washout on the delayed phase occurred in all nodules.

Treatment

The treatment of 12 patients with HCC is shown in Table 6. 2 patients underwent surgical portosystemic shunt, four underwent IVC stenting, one underwent surgical removal of membrane obstruction, and one underwent catheter-directed thrombolysis for hepatic vein and inferior vena cava block. There was hepatic resection for one patient, radiofrequency ablation for one patient, and TACE treatments for another five patients. The remaining patients refused related treatment and only received conservative treatment.

Table 6 | Treatment of HCC and BCS in 12 patients with HCC.

Discussion

HCC has been regarded as one of the significant complications of BCS. (5, 8, 11, 14–19) The exact pathogenesis of HCC in patients with BCS has not been elucidated. (20) It has been reported that the female gender was a risk factor. However, in our cases, the male gender is prominent. This may be due to the small sample quantity and sporadic cases. In accordance with previous reports, hepatitis viruses have also shown less association with the development of HCC in BCS patients. (15) Obstruction of the hepatic venous outflow tract leads to sinusoidal congestion, ischemic injury to liver cells, and portal vein hypertension, subsequently leading to hepatic congestion with necrosis, regeneration, fibrosis, and liver cirrhosis was believed to be one of the mechanisms. (14) Many other researchers reported and analyzed the imaging features of hepatic venous outflow tract obstruction. (21) However, the Chinese BCS population differed significantly from the western BCS population. (22) In our cases, all cases were with IVC obstruction, which is the same as the previous report. However, 4 of our cases developed HCC after curative IVC stenting treatment patients, and the obstruction forms in our series vary from case to case. This may indicate a completely different underlying mechanism. Hana Park et al. previously reported the association between hepatic vein pressure gradient and the development of HCC. (23) Thus, cases after IVC stenting may remain a high hepatic vein pressure gradient. And if this is the case, surveillance of the hepatic vein pressure gradient should be performed.

As for tumor characteristics, there is evidence of different mechanisms from the influence of hepatitis viruses, alcoholism, autoimmune diseases, or chemical intoxication. All the tumor nodules are located at the edge of the liver-75% of nodules located in the right lobe, especially the right posterior lobe. The pathology of those two patients after hepatic resection shows high differentiation, and in accordance, the invasiveness of the tumor seems only associated with the diameter of the nodules. Only in 2 patients with a diameter larger than 10 cm were the intrahepatic metastatic observed, and only the largest was with PV invasion.

The prognosis of HCC is still poor. Hepatic resection remains an effective and optimal choice for treating BCS-associated HCC in patients with A or B Child-Pugh grades. However, TACE and RFA have also been reported as effective treatment options. (11, 15, 18, 24, 25) Immune checkpoint inhibitor has been recently successfully applied in HCC patients. However, current knowledge about the application of immune checkpoint inhibitors was mainly obtained in HBV or HCV-associated HCC patients. Further studies should be performed to clarify whether BCS-associated HCC patients could benefit from immunotherapy. The treatment selection may differ in different cases, and the treatment strategy still needs further clarification.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.

Ethics statement

The studies involving human participants were reviewed and approved by the Ethics Committee of Shandong University Qilu Hospital. The patients/participants provided their written informed consent to participate in this study.

Author contributions

K-sL collected and analyzed the data. SG and K-sL analyzed the references and wrote the paper. Y-xC read the paper and provided revising advice. Z-lZ contributed to study supervision and revised the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (Grant No. 81900728, 82072676, 82172791,82203766), Shandong Province Natural Science Foundation (Grant No. ZR2019MH008, ZR2020MH238, ZR2021QH079), Shandong Province Key R&D Program (Major Scientific Innovation Projects, 2021CXGC011105), Shandong Medical and Health Technology Development Project (Grant No. 2018WSB20002), Clinical Research Foundation of Shandong University (Grant No. 2020SDUCRCA018), Key Research and Development Program of Shandong Province (Grant No. 2019GSF108254). Beijing Medical Award Fund (Grant No. 6010121060). The funders had no role in study design, data collection, analysis, interpretation, and manuscript writing.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

HCC, hepatocellular carcinoma; BCS, Budd-Chiari syndrome; ALT, alanine aminotransferase; AST, aspartate aminotransferase; IVC, inferior vena cava; CT, computed tomography; MRI, magnetic resonance imaging; AFP, α-fetoprotein; PT-INR, international standard ratio of prothrombin time; HbsAg, Hepatitis B surface antigen; PLT, platelet; WBC, white blood cell; Hb, hemoglobin; HBV, Hepatitis B virus; HCV, Hepatitis C virus; HV, hepatic vein; PV, portal vein; BD, bile duct; LC, liver cirrhosis; RFA, radiofrequency ablation; TACE, transcatheter arterial chemoembolization.

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Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2022 Li, Guo, Chen and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 09 December 2022
doi: 10.3389/fimmu.2022.1052937

Efficacy of immune checkpoint inhibitors plus molecular targeted agents after the progression of lenvatinib for advanced hepatocellular carcinoma

Fucun Xie ¹,²,³,⁴†, Bowen Chen ⁵†, Xu Yang ¹†, Huaiyuan Wang ¹†, Ge Zhang ¹, Yanyu Wang ¹, Yunchao Wang ¹, Nan Zhang ¹, Jingnan Xue ¹, Junyu Long ¹, Yiran Li ¹, Huishan Sun ¹, Ziyu Xun ¹, Kai Liu ¹, Xiangqi Chen ¹, Yang Song ¹,⁶, Xiaobo Yang ¹, Zhenhui Lu ⁷, Yilei Mao ¹, Xinting Sang ¹, Yinying Lu ⁵* and Haitao Zhao ¹*

¹ Department of Liver Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, ² Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, ³ State Key Laboratory of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, ⁴ Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China, ⁵ Peking University 302 Clinical Medical School, Beijing, China, ⁶ Department of Thoracic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, ⁷ Hepatobiliary and Pancreatic Surgery, Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Ze Xiang, Zhejiang University, China
Paolo Gallo, Policlinico Universitario Campus Bio-Medico, Italy

*Correspondence:
Haitao Zhao
zhaoht@pumch.cn
Yinying Lu
luyinying1973@163.com

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 24 September 2022

Accepted: 16 November 2022

Published: 09 December 2022

Citation:
Xie F, Chen B, Yang X, Wang H, Zhang G, Wang Y, Wang Y, Zhang N, Xue J, Long J, Li Y, Sun H, Xun Z, Liu K, Chen X, Song Y, Yang X, Lu Z, Mao Y, Sang X, Lu Y and Zhao H (2022) Efficacy of immune checkpoint inhibitors plus molecular targeted agents after the progression of lenvatinib for advanced hepatocellular carcinoma. Front. Immunol. 13:1052937. doi: 10.3389/fimmu.2022.1052937

Background

Lenvatinib is a standard first-line systemic therapy in advanced hepatocellular carcinoma (aHCC) and is widely used in all lines. However, the efficacy and safety of immune checkpoint inhibitors (ICIs) plus molecular targeted agents (MTAs) after the progression of lenvatinib treatment are unclear.

Objective

The aim of this study was to evaluate the anticancer effects of ICI plus MTA in patients with aHCC who progressed after lenvatinib.

Methods

We retrospectively included aHCC patients treated with ICI plus MTA after the progression of lenvatinib from two medical centers. Participants who continued lenvatinib treatment were classified into the “ICI+Lenva” group, while the “ICI+Others” group included patients receiving other MTAs. The efficacy endpoints were progression-free survival (PFS), post-progression survival (PPS), overall survival (OS), and tumor response following RECIST v1.1. Safety was evaluated according to Common Terminology Criteria for Adverse Events v5.0.

Results

In this study, 85 eligible aHCC patients were enrolled, including 58 in the ICI+Lenva group and 27 in the ICI+Others group. At a median follow-up time of 22.8 months, the median PPS and PFS were 14.0 (95% CI: 9.0-18.2) and 4.5 months (95% CI: 3.5-8.3), respectively. The objective response and disease control rates were 10.6% and 52.9%, respectively. No significant differences were observed in any of the efficacy endpoints between the two groups. Prolonged PPS was associated with Child–Pugh grade A, AFP < 400 IU/ml, and concomitant locoregional treatment. All patients experienced adverse events (AEs), but no fatal AEs were observed.

Conclusion

ICI plus MTA in aHCC patients after the progression of lenvatinib presented high antitumor activity and safety. Patients could continue lenvatinib treatment and receive ICIs as well as locoregional treatment to achieve better OS.

Keywords: hepatocellular carcinoma (HCC), immune checkpoint inhibitor (ICI), molecular targeted agent (MTA), lenvatinib, efficacy, safety

Introduction

Hepatocellular carcinoma (HCC) is a highly malignant carcinoma with a dismal prognosis, especially advanced HCC (aHCC) (1). Lenvatinib, a molecular targeted agent (MTA), showed promising results compared with sorafenib in the REFLECT clinical trial (2), which made it the second MTA approved by the Food and Drug Administration as first-line systemic therapy for aHCC following sorafenib. A real-world study confirmed that lenvatinib performed well as a post-line systemic therapy in aHCC patients (3).

Immunotherapy is currently making progress in improving the survival of aHCC. Immune checkpoint inhibitors (ICIs) contribute to overcoming immune evasions by targeting immune checkpoints, including programmed cell death protein 1 (PD-1), programmed cell death ligand 1 (PD-L1), and cytotoxic T lymphocyte-associated antigen-4 (CTLA-4) (4). Anti-PD-1 inhibitor monotherapy and MTA plus ICIs have exhibited specific antitumor activity in aHCC (5–10).

However, not all aHCC patients respond to lenvatinib in real-world clinical practice. Moreover, resistance and progression were observed in a large portion of aHCC patients treated with lenvatinib. Multiple systemic treatment regimens have been brought into clinical practice for HCC patients after the progression of first-line sorafenib treatment. Regorafenib was the first MTA approved for second-line treatment, followed by cabozantinib and ramucirumab (11–13). Pembrolizumab and camrelizumab also displayed satisfying prognoses as non-first-line treatments (5, 6, 9).

Recent studies have pointed out that antitumor treatments after progression with first-line lenvatinib were associated with prolonged survival in aHCC patients (14, 15). However, the optimal subsequent therapy after the progression of lenvatinib was inconclusive partially because it has been only four years since the approval of lenvatinib as a first-line agent for aHCC patients. A previous study reported that regorafenib plus PD-1 inhibitor was a promising treatment pattern after the progression of first-line sorafenib or lenvatinib (16), which indicated that the ICI plus MTA pattern (17–19) might be a promising strategy for aHCC patients with progression of lenvatinib treatment. In addition, many patients continued to use lenvatinib after progression due to Patient Assistance Programs, but the efficacy of rechallenging lenvatinib with lenvatinib plus ICI remains questionable. In this study, we aimed to explore the effectiveness and safety of MTAs plus ICIs and the necessity of MTA rotation in aHCC patients after the progression of lenvatinib treatment.

Materials and methods

Study design

This study was a multicenter observational retrospective real-world study that included two medical centers (Peking Union Medical College Hospital, PUMCH; The Fifth Medical Centre of PLA General Hospital, PLAGH). Informed consent was not obtained from the study participants, as this was a retrospective study. The requirement for informed consent was waived by the Ethics Committees of PUMCH and PLAGH, as specific patient details are not presented here (JS-1391 and KY-2022-8-68-1). All data were collected from the electronic medical records system (EMRS). This study conformed to the Declaration of Helsinki and was approved by the Ethics Committee mentioned above. The study is registered at ClinicalTrials.gov (NCT03892577).

Patients and groups

Eligible patients were ICI-therapy-naïve aHCC patients who received MTA plus ICI directly after radiology-confirmed progression of lenvatinib treatment under RECIST v1.1 in two medical centers from Jan 2018 to Dec 2021. Clinicians made therapy decisions for each participant after a comprehensive evaluation of stage, liver function, physical performance, the patient’s preference, and Chinese clinical guidelines for managing hepatocellular carcinoma (20).

The major inclusion criteria were as follows: [1] at least one measurable tumor region according to RECIST v1.1; [2] received at least one regimen of MTA plus ICI directly after the progression of lenvatinib treatment; and [3] at least one effective follow-up. The major exclusion criteria were as follows: [1] end-stage HCC (defined as BCLC stage D); [2] did not suffer from progression of lenvatinib treatment or discontinued lenvatinib treatment due to other reasons instead of disease progression; [3] received other systemic therapies after progression of lenvatinib treatment; [4] without any effective follow-up; and [5] had already received ICI therapy before the progression of lenvatinib treatment.

The patients underwent an examination every six to eight weeks. According to the different patterns of combination therapy, patients who rechallenged lenvatinib plus ICI were included in the “ICI+Lenva” group, while those who switched to another MTA plus ICI were included in the “ICI+Others” group.

Endpoints and follow-up

The primary endpoint was post-progression survival (PPS, defined as the time between the initiation of MTA plus ICI after the progression of lenvatinib and death, the last-time effective survival follow-up or the end of the study, whichever came first). In particular, we calculated overall survival (OS), which started from the initiation of lenvatinib treatment to the finishing point mentioned above. The secondary endpoints were progression-free survival (PFS, defined as the time between the initiation of MTA plus ICI after the progression of lenvatinib and radiology-confirmed disease progression, death, or the last radiologic evaluation, whichever came first), objective response rate (ORR), and disease control rate (DCR). Patients with complete response (CR), partial response (PR), or stable disease (SD) ≥ six months continuously were defined as achieving a clinical benefit response (CBR). All secondary efficacy endpoints were evaluated according to RECIST v1.1. Safety was evaluated with the Common Terminology Criteria for Adverse Events (CTCAE) v5.0.

Risk factors for prognosis and subgroup analysis

Cox proportional hazards analyses were employed to explore the risk factors for PPS and PFS. Furthermore, to rule out the effect of previous systemic therapy before lenvatinib treatment on efficacy, we extracted patients treated with first-line lenvatinib for subgroup analysis.

Statistical analysis

Baseline characteristics, ORR, DCR, and CBR are described as numbers (ratio, %) and were compared with Fisher’s exact test. Survival analyses were performed with the Kaplan–Meier method, and differences were compared with the log-rank test. A Cox proportional hazards model was used to analyze risk factors for prognosis. Variables with P < 0.05 in univariate analysis were included in multivariate analysis. The significance level was defined as a two-tailed P < 0.05. All statistical analyses were conducted with R v3.6.3.

Results

Baseline characteristics

In our study, 85 eligible patients were continually enrolled, 63 from PUMCH and 22 from PLAGH. Among these 85 participants, 58 were in the ICI+Lenva group, and the remaining 27 were in the ICI+Others group. In the ICI+others group, 14 patients switched to apatinib, six to regorafenib, four to bevacizumab, two to sorafenib, and one to donafenib. PD-1 inhibitors were applied in 82 patients (96.5%), while the other three patients (3.5%) used PD-L1 inhibitors. Previous systemic therapies were reported in 30 (35.3%) patients before lenvatinib treatment. In addition, 21 (24.7%) patients received regional therapies during the period of ICI plus MTA therapy. Detailed baseline characteristics are recorded in Table 1. No significant differences were observed between the ICI+Lenva group and the ICI+Others group.

Table 1 | Baseline characteristics of patients.

Overall efficacy

At the data cutoff (2022-05-15), progressive disease was observed in 61 patients treated with ICI plus MTA following lenvatinib progression. The major reason for combination therapy termination was disease progression or death (n = 54). The other seven cases were associated with adverse events (AEs) (Table S1). Only 19 patients switched to other ICI plus MTA treatment (n = 9) or MTA monotherapy (n = 10) after combination therapy progression, while the other 42 patients did not receive systemic therapy (Table S2). Patients active in post-line systemic therapy after progression of ICI plus MTA were likely to have prolonged OS (median OS 6.0 months vs. 20.3 months, P = 0.012, Figure S1).

The median follow-up was 22.8 (95% CI: 19.6-27.6) months. The median PPS and PFS were 14.0 (95% CI: 9.0-18.2) months and 4.5 (95% CI: 3.5-8.3) months, respectively. The overall median OS was 22.1 (95% CI: 19.9-25.9) months. The overall ORR, DCR and CBR were 10.6% (95% CI: 5.0%-19.2%), 52.9% (95% CI: 41.8%-63.9%) and 32.9% (95% CI: 23.1%-44.0%), respectively (Table 2 and Figure 1).

Table 2 | Tumor response and efficacy endpoints for all patients.

Figure 1 | Survival outcomes for patients treated with ICI plus MTA after lenvatinib progression. (A) Progression-free survival for all patients; (B) Progression-free survival for the ICI+Lenva group and the ICI+Others group; (C) Post-progression survival for all patients; (D) Post-progression survival for the ICI+Lenva group and the ICI+Others group; (E) Overall survival for all patients; (F) Overall survival for the ICI+Lenva group and the ICI+Others group.

Differences in the efficacy endpoints

Following the median follow-up times of 23.5 months and 15.1 months in the ICI+Lenva and ICI+Others groups, the median PPS and PFS were 14.0 (95% CI: 9.0-21.2) and 15.9 (95% CI: 5.9-NE) months (P = 0.78) and 5.7 (95% CI: 3.6-11.7) and 3.6 (3.1-NE) months (P = 0.3), respectively, with no significant differences. The ORR, DCR and CBR in the ICI+Lenva vs. ICI+Others groups were 13.8% (95% CI: 6.2%-25.4%) vs. 3.7% (95% CI: 0.1%-19.0%) (P = 0.304), 51.7% (95% CI: 38.2%-65.1%) vs. 55.5% (95% CI: 35.3%-74.5%) (P = 0.74), and 37.9% (95% CI: 25.5%-51.6%) vs. 22.2% (95% CI: 8.6%-42.2%) (P = 0.15), respectively. Additionally, the median OS was 22.1 (95% CI: 17.2-28.6) and 21.3 (95% CI: 19.9-NE) in the two respective groups (P = 0.14) (Table 2 and Figure 1).

Subgroup efficacy analysis

Subgroup analyses were performed in patients with first-line lenvatinib treatment (N = 55). The ORR, DCR, CBR, PFS, PPS, and OS in this subgroup were similar to those in the post-line lenvatinib group (Figure 2). The ORR, DCR and CBR in the ICI+Lenva group vs. the ICI+Others group were 18.0% (95% CI: 7.5-33.5) vs. 0 (95% CI: 0.0-20.6), 53.9% (95% CI: 37.2-69.9) vs. 37.5% (95% CI: 15.2-64.6), and 43.6% (95% CI: 27.8-60.4) vs. 18.8% (95% CI: 4.1-45.7), respectively. The median PFS, PPS and OS were 6.1 (4.1-14.1) vs. 3.4 (1.9 -NE), 14.5 (11.2-21.4) vs. 7.6 (5.4-NE), and 23.1 (19.9-28.6) vs. 23.1 (11.8-NE), respectively. Except for CBR, which showed a marginally significant difference, no significant differences were observed among the other efficacy endpoints in the subgroups (Table 3 and Figure 2).

Figure 2 | Survival outcomes for patients treated with ICI plus MTA after first-line lenvatinib progression. (A) Progression-free survival for patients after first-line or post-line lenvatinib progression; (B) Progression-free survival for the ICI+Lenva group and the ICI+Others group in first-line lenvatinib subgroup; (C) Post-progression survival for patients after first-line or post-line lenvatinib progression; (D) Post-progression survival for the ICI+Lenva group and the ICI+Others group in first-line lenvatinib subgroup; (E) Overall survival for patients after first-line or post-line lenvatinib progression; (F) Overall survival for the ICI+Lenva group and the ICI+Others group in first-line lenvatinib subgroup.

Table 3 | Tumor response and efficacy endpoints for first-line lenvatinib subgroup analysis.

Factors influencing the overall efficacy

The multivariate Cox proportional hazards model suggested that AFP ≥ 400 ng/mL [1.75 (1.03-2.97), P = 0.037] was a risk factor for PFS. For OS, Child–Pugh B [1.96 (1.05-3.65), P = 0.034] and AFP ≥ 400 ng/mL [2.37 (1.35-4.17), P = 0.003] were risk factors, and concomitant locoregional therapy [0.33 (0.15-0.72), P =0.005] was a protective factor (Table 4 and Figure 3).

Table 4 | Risk factors for progression-free survival and post-progression survival.

Figure 3 | Risk factors for survival. (A) Multivariate Cox proportional hazard model for progression-free survival; (B) Multivariate Cox proportional hazard model for post-progression survival. ECOG, Eastern Cooperative Oncology Group; AFP, alpha-fetoprotein.

Adverse events

All patients were assessable for adverse events. AEs were recorded in all patients, and severe AEs (SAEs) were recorded in 46 (54.1%) patients. No AE-related deaths occurred. The most common AEs (>25%) were fatigue (45.9%), hyperbilirubinemia (38.8%), hypoalbuminemia (37.7%), elevated aspartate aminotransferase (36.5%), hypertension (35.3%) and decreased appetite (34.1%). For SAEs, hypertension (11.8%), diarrhea (8.2%), elevated aspartate aminotransferase (7.1%), fatigue (5.9%), abdominal pain (5.9%), rash (5.9%), anemia (5.9%) and gastrointestinal bleeding (5.9%) were the most common. The overall incidence of AEs in the ICI+Lenva group was slightly lower than that in the ICI+Others group (46.6% vs. 70.4%), with a marginally significant difference (P = 0.04) (Table 5).

Table 5 | Adverse events.

Discussion

Our study evaluated the clinical outcomes of MTAs plus ICIs after the progression of lenvatinib. In this cohort of 85 patients from two medical centers in China, we observed an ORR of 10.6%, with a median PFS of 4.5 months, a median PPS of 14.0 months, and a median OS of 22.1 months. No AE-related deaths occurred. This study indicated that later-line MTA+ICI treatment is generally safe and effective after the progression of lenvatinib treatment.

Lenvatinib demonstrated promising antitumor activity in the first-line treatment of aHCC (2), and it performed well in second-line and later-line studies in the real world (21, 22). However, tumor resistance is unavoidable. In the REFLECT study, patients in the lenvatinib arm who progressed without subsequent therapy had a median OS of approximately 11.5 months, which was not significantly different from 9.1 months with first-line sorafenib without subsequent therapies (HR 0.90, 95% CI: 0.75-1.09) (2). Sequential immunotherapy after the progression of MTAs is a promising treatment option. At present, the efficacy and safety of sequential MTAs and immunotherapy after the progression of sorafenib have been reported in prospective studies (6, 9, 11). Since lenvatinib was approved for HCC treatment not long ago, data on sequential therapy after lenvatinib progression are limited. The efficacy and safety of regorafenib and/or regorafenib plus PD-1 inhibitors after the progression of first-line sorafenib or lenvatinib treatment in real-world situations have been reported recently (16). However, considering the complicated lenvatinib lines in the real world and various factors affecting subsequent treatment decisions, the optimal sequential MTA+ICI regimen after the progression of lenvatinib and whether it needs to be actively switched to MTAs remain unclear. Our real-world research answered this question for the first time.

Later-line systemic therapy is likely to bring prognostic benefits. A post hoc analysis of the REFLECT study by Alsina et al. showed that after the progression of first-line lenvatinib, 32.6% of patients received subsequent systemic therapy. Most subsequent treatments were targeted agents, while only 3.1% of patients were treated with immunotherapy. Second-line systemic therapy was associated with prolonged survival (median OS: 20.8 vs. 11.5 months) (15). In the RESORCE study, the median OS of Child–Pugh A patients receiving regorafenib as a second-line MTA was 10.6 months (11). The KEYNOTE-240 study finally reported a median OS of 13.9 months and a median PFS of 3.0 months with pembrolizumab monotherapy after sorafenib progression (6). Furthermore, systemic therapy following the progression of combination therapy was associated with a better prognosis in our study (Figure S1).

It seemed that the MTA plus ICI pattern in our study performed better than MTA or ICI monotherapy. The median PPS after the progression of lenvatinib treatment in our cohort reached 14.0 months, which was higher than that in RESORCE and KEYNOTE-240 (6, 11). Moreover, the median OS reached 22.2 months, exceeding the median OS of patients in the lenvatinib arm who received later-line therapy in the REFLECT study. The comparison preliminarily displayed the effectiveness of subsequent MTA plus ICI after the progression of lenvatinib treatment. Huang et al. explored the efficacy of regorafenib and regorafenib plus PD-1 inhibitors after the progression of first-line sorafenib or lenvatinib treatment. The regorafenib plus PD-1 inhibitor group had better OS (median 13.4 vs. 9.9 months; P = 0.023) (16). In this research, we further analyzed the cohort. After extracting the subgroup of patients with first-line lenvatinib treatment, we found that the efficacy in the subgroup (median PFS: 4.6 months, median PPS: 14.0 months, and median OS: 22.1 months) was similar to the efficacy in the overall cohort (median PFS: 4.5 months, median PPS: 14.0 months, and median OS: 22.1 months). Moreover, PPS in both the subgroup and overall cohort was similar or slightly better than this endpoint reported in Huang’s cohort. The comprehensive analysis of these two studies indicated that later-line MTA+ICI was associated with a better prognosis, regardless of the progression of lines of lenvatinib treatment.

Combination therapy can achieve the “1+1>2” effect; however, the mechanism of combined synergism is not yet fully understood but might be related to the tumor microenvironment. Recent studies have shown that protein kinase Cα is directly phosphorylated at S226, activating transcription, inducing macrophage recruitment and M2-like polarization, and driving immune escape and resistance to ICIs. Lenvatinib can restore sensitivity to ICIs by blocking the protein kinase Cα/zinc finger protein 64/macrophage colony-stimulating factor axis, thus remodeling the tumor microenvironment (23). In addition, VEGFR-2 inhibition was associated with upregulation of IFN-γ and PD-L1 expression, and simultaneous blockade of PD-1 and VEGFR-2 in HCC can promote vascular normalization and enhance antitumor immune responses (24). With the development of tumor immunology, it will be possible to further expand the patient population suitable for immunotherapy by combining different MTAs or local therapy.

In the real world, many patients continued lenvatinib treatment after the progression of front-line lenvatinib treatment with the help of Patient Assistance Programs, lacking enough support to select another MTA. Unfortunately, no clinical research has discussed the necessity of switching to another MTA combined with immunotherapy as a late-line treatment. In our study, the ICI+Lenva and ICI+Others groups had similar follow-up periods and comparable baseline characteristics, and no significant differences were observed between these two groups in the short-term and long-term efficacy endpoints. In addition, lenvatinib performed well in a cost-effectiveness analysis (25–29), so the rechallenge of lenvatinib plus ICI is likely to be a better choice. Future prospective clinical trials are needed to prove the above hypothesis.

Physical performance, liver function, and AFP levels were risk factors for short- and long-term outcomes in our study, which have been thoroughly discussed in previous articles. At the same time, concomitant interventional therapy was a protective factor for long-term efficacy. Previous studies also reported the synergistic effect of interventional therapy on immunotherapy (30, 31). Our study further confirmed that interventional therapy improved the prognosis of patients treated with ICI plus MTA following lenvatinib. The safety of this MTA plus ICI pattern was acceptable. The AE spectrum was similar to that in REFLECT, RESORCE, KEYNOTE-240, and IMbrave150 (2, 6, 11, 32), and no fatal AEs occurred.

Several limitations existed in our study. Unlike global multicenter randomized controlled trials, this study is a retrospective real-world observational study including only 85 patients from two medical centers, which could not fully represent Chinese HCC patients. In addition, multiple ICIs and MTAs were used in this study, and complex treatment regimens may impact survival outcomes. At present, many first-line and second-line treatments for HCC have been approved. In the future, more complete randomized controlled trials are needed to explore the treatment options following progression after lenvatinib.

Conclusion

In conclusion, this study provides valuable evidence that ICI plus MTA in HCC patients after the progression of lenvatinib presented potential antitumor activity and safety. Furthermore, concomitant locoregional treatment seemed to be associated with better OS.

Data availability statement

The research data are not publicly available on ethical grounds. However, inquiries regarding all data analyzed in this study can be directed to the corresponding author.

Ethics statement

The studies involving human participants were reviewed and approved by Ethics Committees of Peking Union Medical College Hospital Ethics Committees of The Fifth Medical Centre of PLA General Hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author contributions

Conceptualization, HZ and YLu. Methodology, FX, BC, XuY and HW. Software, GZ and YaW. Validation, YcW and NZ. Formal analysis, JX. Investigation, JL. Resources, YL, HS and ZX. Data curation, KL, XC, YS and XbY. Writing—original draft prep-aration, FX, BC, XuY and HW. Writing—review and editing, FX, BC, XuY and HW. Visualization, ZL. Supervision, ZL, YM, XS, YLu and HZ. Project administration, HZ. Funding acquisition, XBY, XS and HZ. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by National High Level Hospital Clinical Research Funding (2022-PUMCH-B-128 to HZ), CAMS Innovation Fund for Medical Sciences (CIFMS) (2021-I2M-1-061 and 2022-I2M-C&T-A-003 to HZ; 2021-1-I2M-003 to XS), CSCO-hengrui Cancer Research Fund (Y-HR2019-0239 to HZ; Y-HR2020MS-0415 to XS; Y-HR2020QN-041 to XbY), CSCO-MSD Cancer Research Fund (Y-MSDZD2021-0213 to HZ) and National Ten-thousand Talent Program (to HZ).

Acknowledgments

We thank all the patients and their families for their participation and thank Zhisong Liu for assistance with the statistical analyses.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2022.1052937/full#supplementary-material

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CASE REPORT
published: 09 January 2023
doi: 10.3389/fimmu.2022.1079342

Conversion therapy for advanced intrahepatic cholangiocarcinoma with lenvatinib and pembrolizumab combined with gemcitabine plus cisplatin: A case report and literature review

Wei Zhang †*, Chu Luo †, Zun-Yi Zhang, Bi-Xiang Zhang and Xiao-Ping Chen

Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Yawei Qian, Zhongnan Hospital, Wuhan University, China
Hui-Chuan Sun, Fudan University, China

*Correspondence:
Wei Zhang
weizhangtjh@hust.edu.cn

†These authors have contributed equally to this work and share first authorship

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 25 October 2022

Accepted: 20 December 2022

Published: 09 January 2023

Citation:
Zhang W, Luo C, Zhang Z-Y, Zhang B-X and Chen X-P (2023) Conversion therapy for advanced intrahepatic cholangiocarcinoma with lenvatinib and pembrolizumab combined with gemcitabine plus cisplatin: A case report and literature review. Front. Immunol. 13:1079342. doi: 10.3389/fimmu.2022.1079342

Background

Intrahepatic cholangiocarcinoma (ICC) is a highly malignant biliary tumor. Patients with unresectable and advanced ICC have a poor prognosis with current gemcitabine-based chemotherapy. Combination therapy strategies based on immunotherapy have achieved promising results in various tumor types.

Case presentation

We reported a patient with unresectable ICC who received lenvatinib and pembrolizumab in combination with gemcitabine plus cisplatin (GP) chemotherapy and subsequently underwent radical liver resection. A 46-year-old male with a history of chronic hepatitis B and hypertension was diagnosed with ICC. Multiple liver tumors with ring-like enhancement were detected on abdominal contrast-enhanced CT and MRI. Enlarged lymph nodes were found in the hilar and retroperitoneal areas. The tumor was clinically staged as T2N1M0 (stage IIIB). Lenvatinib and pembrolizumab in combination with GP chemotherapy were adopted as first-line treatments for the patient. After six cycles of scheduled treatment, the diameter of the largest liver lesion and the number of liver lesions were markedly reduced. The level of the tumor marker CA19-9 decreased to a normal range. A partial response according to the mRECIST criteria was achieved without severe toxicities. Non-anatomical liver resection (segment 4b, 5,6 + segment 7 + segment 8), cholecystectomy and hilar lymph node dissection were performed one month after stopping combination therapy. Pathological examination confirmed a diagnosis of moderate-to-poorly differentiated ICC with lymph node metastasis. The patient has survived 15 months following resection of the tumors, with no evidence of local recurrence or distant metastasis.

Conclusion

Lenvatinib and anti-PD1 antibody pembrolizumab in combination with GP chemotherapy provided promising antitumor efficacy with reasonable tolerability, which may be a potentially feasible and safe conversion therapy strategy for patients with initially unresectable and advanced ICC.

Keywords: intrahepatic cholangiocarcinoma, immunotherapy, antiangiogenic therapy, chemotherapy, conversion therapy

Introduction

Cholangiocarcinoma is a heterogeneous group of malignant epithelial tumors arising from the biliary epithelium (1). Cholangiocarcinoma can be divided into intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (including hilar and distal bile duct) (2). The incidence and mortality of ICC have increased during the last three decades (3). To date, the most effective method of treating ICC is early radical surgical removal. However, most patients are in the advanced stage when diagnosed with ICC and lose the opportunity for surgical treatment (4). The median survival time of patients with advanced cholangiocarcinoma is less than two years, and the five-year survival rate is only 10% (5). Systemic conventional chemotherapy with gemcitabine and cisplatin (GP) is the standard first-line treatment, although the prognosis remains poor with a median survival of less than one year (6). Existing treatment options have limited and variable efficacy. Thus, it is urgent to develop novel effective therapies for ICC.

Recently, due to the development of precision medicine, immunotherapy has played an important role in treating tumors by preventing escape from immune surveillance. Immune checkpoint inhibitors (ICIs) have been successfully used to treat malignancies such as hepatocellular carcinoma (HCC), leading to an increasing number of studies evaluating their use in biliary tract cancer (BTC) (7, 8). Although these new inhibitors have improved patient survival, the effectiveness of ICIs monotherapy remains relatively limited, achieving an objective response rate (ORR) of up to approximately 20% (9). Numerous trials are being conducted to investigate therapy strategies using combinations of ICIs with immunotherapy, chemotherapy, anti-angiogenics or local therapy, with preliminary results showing promising antitumor efficacy and tolerable safety (10–12). Lenvatinib, a novel oral multi-kinase inhibitor, exerts both antiangiogenic and direct antitumor effects by targeting multiple kinase receptors, including vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF), and platelet-derived growth factor (PDGF) receptors (13). Recently, the combination of anti-PD1 antibody with lenvatinib has shown remarkable results in several retrospective studies included patients with advanced BTC (14, 15).

Here, we report a patient with unresectable and advanced ICC who received successful radical surgical resection after combination therapy with lenvatinib and the anti-PD1 antibody pembrolizumab combined with gemcitabine plus cisplatin chemotherapy as first-line treatment.

Case presentation

A 46-year-old male with a history of chronic hepatitis B and hypertension was admitted to our hospital for the treatment of multiple liver tumors in December 2020. The liver tumors were detected at a local hospital after he presented with abdominal pain and abdominal distension for one month. The patient reported no past or present alcohol consumption or a history of diabetes. His family history and medical history were unremarkable. A physical examination of the patient showed no obvious positive signs. Laboratory investigations of liver and renal functions were all normal. The only abnormal laboratory results were carbohydrate antigen 19-9 (CA19-9), which was elevated to 710 U/ml. The levels of AFP, PIVKA-II and CEA were 2.77 ng/mL, 20 mAu/mL and 1.39 ng/mL, respectively. The indocyanine green retention rate at 15 min was 6.3%. The Child-Pugh Score was A with 5 points. Multiple liver tumors with ring-like enhancement were detected on abdominal contrast-enhanced CT and MRI. The largest lesion (measuring 11.5 cm in diameter) was located in the right anterior lobe and segment 6 (Figure 1). The hilar and retroperitoneal area revealed a shadow of multiple enlarged lymph nodes. No portal or inferior vena cava vein invasion or distant metastasis was observed. To rule out the possibility of liver metastases from gastrointestinal tumors, gastrointestinal endoscopy and colonoscopy were performed. No evidence of advanced malignant tumors was found. Considering that multiple intrahepatic tumors were unresectable, we performed an ultrasound-guided fine-needle aspiration biopsy to confirm the diagnosis. Pathohistological analysis of the tumor revealed adenocarcinoma arising from the biliary tract. The results of immunohistochemical analysis were as follows: PCK(+), EMA(+), CK7 (+), CK19 (+), AFP (-), Hepatocyte (-), VILLIN (+), Glypican-3(-), Arginase-1(-), CK20(-), CD34 (-), and Ki-67 labeling index: 30%. Based on these findings we made a diagnosis of ICC with lymph node metastasis and without macrovascular invasion. The TNM clinical stage was determined as T2N1M0 (stage IIIB) according to the American Joint Committee on Cancer (AJCC) Cancer Staging Handbook, 8th edition.

Figure 1 | Representative MR imaging revealed the changes of multiple lesions before and after combination treatment. (A, D, G, J): An axial T2-weighted MR image. (B, E, H, K): An axial diffusion weighted image (DWI). (C, F, I, L): An axial delayed phase images. (A, B, C: A dynamic contrast-enhanced MRI showed that multiple liver tumor located in the segment 7 and 8 before treatment. (D, E, F: MRI results after six cycles of treatment revealed that the tumor located in the segment 7 and 8 has shrunk in diameter and the number of tumors decreased. (G, H, I): MRI showed the largest tumor (measuring 11.5 cm in diameter) located in the right anterior lobe and segment 6 before treatment. (J, K, L): The diameter of the largest liver lesion located in the right anterior lobe and segment 6 significantly decreased from 11.5cm to 7.7cm after six cycles of treatment. There was no markedly enhancement within the tumor during the delay phase.

Treatment

According to the opinion of the ICC multidisciplinary team (MDT) in our hospital and NCCN clinical practice guidelines in oncology hepatobiliary cancers, the patient was not a candidate for radical resection. For unresectable advanced ICC, gemcitabine and cisplatin combination chemotherapy is the standard first-line treatment, but the overall survival results remain disappointing. There is growing evidence that molecular targeted agents and immunotherapy may improve overall survival in patients with advanced ICC (9). After ICC MDT consultations and the patient’s informed consent, anti-PD immunotherapy (pembrolizumab, at a dose of 200 mg every three weeks) and lenvatinib (8mg/qd) combined with gemcitabine and cisplatin was considered as treatment for the patient, which was initiated in December 2020. Gemcitabine and cisplatin were administered intravenously on a 21-day cycle. Gemcitabine (1000 mg/m²) and cisplatin (25 mg/m²) were administered on Days 1 and 8 of each cycle. The patient was treated and monitored regularly. Blood count, liver, renal, thyroid, adrenal, and cardiac functions, and tumor markers were monitored every four weeks before each round of treatment. Adverse events were assessed and graded using the National Cancer Institute Common Terminology Criteria for Adverse Events v4.0.

After three cycles of targeted therapy, immunotherapy and chemotherapy, the diameter of the largest liver lesion significantly decreased from 11.5 cm to 7.7 cm. The tumor marker CA19-9 dramatically decreased from 710 to 35 U/ml (Figure 2). The tumor response was evaluated and considered as partial response (PR) in February 2021 according to the mRECIST criteria. After six cycles of scheduled treatment, enhanced MR indicated that there was no significant change in the size of the largest tumor. However, the number of liver lesions decreased. The treatment effect is indicated by extensive central tumor necrosis (Figure 1). The level of the tumor marker CA19-9 demonstrated a constant downward trend with complete normalization (14.39 U/ml) by the end of his six cycles of treatment (Figure 2). A partial response was achieved without severe toxicities. The treatment-related adverse events were decreased appetite, neutrophil count decrease, platelet count decrease, white blood cell count decrease and diarrhea. All these immune-related adverse events were well controlled and alleviated after treatment. No intolerable grade 3–4 treatment-related adverse events occurred. Follow-up PET-CT revealed no metastasis and demonstrated the absence of hypermetabolic liver lesions and lymph nodes in the abdomen. One month after stopping the medication, we performed non-anatomical liver resection (segment 4b, 5,6 + segment 7 + segment 8), cholecystectomy and hilar lymph node dissection in July 2021.

Figure 2 | The change of the expression level of CA 19-9 (A) and the diameter of the largest liver lesion (B) during combination therapy for advanced ICC.

Operative findings

Exploratory laparotomy with a reverse L-shaped incision in the right upper quadrant was performed. A hard mass with a size of 7.5 x 5.3 cm was observed on segment 4b, 5, and 6 of the liver, which was closely adjacent to the gallbladder. Multiple satellite nodules surrounding the tumor were observed. Intraoperative ultrasound identified two other lesions in segments 7 and 8. The maximal diameters of these two lesions were 1.4 and 1 cm, respectively (Figure 3). To ensure a wide (>1 cm) resection margin, intraoperative ultrasonography was used to delineate the cutting line. Parenchymal transection was performed by using a harmonic scalpel. Divided small diameter vessels were bipolar electrocoagulated, while larger vessels and bile ducts were transected after clamping with Hem-o-loks (Weck Surgical Instruments, Teleflex Medical, Durham, NC, USA) or with Endoscopic Rotating Multiple Clips (Ethicon Endo-Surgery). After the liver tumors were resected, residual bleeding sites were controlled with suture ligation or electrocautery. A silicone drain was applied to the raw liver transection plane.

Figure 3 | Intraoperative gross pathological specimen and H&E staining of resected specimen of a patient with advanced ICC who underwent radical liver resection. (A) and (B) A hard mass with a size of 7.5 x 5.3 cm was observed on the segment 4b, 5,6 of the liver, which was closely adjacent to the gallbladder. There were multiple satellite nodules surrounding the tumor. (C) Two lesions in segments 7 and segment 8 were completely resected. (D) H&E staining of resected tumor samples revealed predominately necrosis (>95%), hyaline change and cholesterol deposits.

Outcome and follow-up

Pathological examination confirmed a diagnosis of moderate-to-poorly differentiated ICC with lymph node metastasis and without macrovascular and microvascular invasion. The resection margin was negative. H&E staining of resected tumor samples revealed predominant necrosis (>95%). The postoperative course was uneventful, and the patient was discharged 14 days after the surgery. Postoperative lenvatinib and pembrolizumab were continued for one year according to the usual dose and pattern. Follow-up was carried out at two-month intervals during the first year. Follow-up appointments consisted of history and physical evaluation, cell blood count, blood biochemistry, tumor markers, ultrasonography or enhanced CT. To date, the patient has survived 15 months following resection of the tumors, with no evidence of local recurrence or distant metastasis. Figure 4 shows this patient’s timeline of initial diagnosis, combination therapy, radical surgery, adjuvant therapy and follow-up.

Figure 4 | Timeline of initial diagnosis, combination therapy, radical surgery, adjuvant therapy and follow-up.

Discussion

Intrahepatic cholangiocarcinoma is a highly malignant biliary tumor with poor outcomes. Early diagnosis and curative surgical resection remain the only options for long-term survival in patients with ICC. However, the rate of early diagnosis of ICC is low, and most patients (60%-70%) have lost their chance of surgery at the time of diagnosis (4). Even if patients undergo successful surgical resection, the high postoperative recurrence rate leads to only a 5-year survival of 20 to 40% in patients with ICC (16). Patients with unresectable BTC generally survive for less than 12 months after diagnosis (17). The combination of gemcitabine and cisplatin is currently the first-line treatment for patients with advanced BTC (6). Due to primary or acquired resistance to chemotherapeutic treatment, the median overall survival (OS) remains poor. Therefore, novel effective therapeutic approaches with distinct mechanisms of action are required for BTC. In recent years, immunotherapy has become the most promising approach for cancer therapy (18).

Multiple studies have evaluated the efficacy of immune checkpoint inhibitors as monotherapy in BTC. To date, KEYNOTE-158 is the largest study of ICIs monotherapy with pembrolizumab (19). Among 104 patients with advanced biliary cancers after progression or intolerance to standard therapy, the confirmed ORR was 5.8%; the median duration of response (DOR) was not reached (6.2-26.6 months). The median OS and progression-free survival (PFS) were 7.4 and 2.0 months, respectively. The anticancer activity of nivolumab has been evaluated in 54 patients with unresectable or metastatic cholangiocarcinoma or gallbladder cancers (20). Among the 46 enrolled patients, investigator-assessed PR and stable disease (SD) were achieved by 10 patients (22%) and 17 patients (32%), with a consequent DCR of 59%. Four responders (40%) achieved a durable objective response lasting at least one year. The mPFS was 3.68 months, and the mOS was 14.24 months. Although PD-1 blockade was recommended in the new NCCN guidelines for unresectable and metastatic ICC patients with dMMR or MSI-H, the limited antitumor effect of ICIs monotherapy in advanced cholangiocarcinoma has prompted investigators to explore different combination therapy strategies.

Evidence has accumulated that combined therapeutic strategies, including ICIs in combination with immunotherapy, chemotherapy, anti-angiogenics, or local therapy, improve the treatment efficacy over ICIs alone. In biliary tract cancer, durvalumab is being investigated in phase I studies in Asian patients (21). The results showed the superiority of the durvalumab–tremelimumab combination versus durvalumab alone in terms of OS (10.1 vs. 8.1 months) and DCR at 12 weeks (32.2% vs. 16.7%). Gemcitabine and cisplatin plus durvalumab for patients with advanced BTC have been evaluated in the phase 3 TOPAZ-1 study (22). A total of 685 patients with inoperable, locally advanced, recurrent, or metastatic BTC were randomly assigned to receive durvalumab or a placebo. OS and PFS were significantly improved in the durvalumab group. ORR was 26.7% for Durvalumab and 18.7% for placebo. Local tumor destruction combined with immunotherapy may have a synergetic effect against solid tumors (23). In addition to reducing the tumor burden, radiotherapy can lead to the triggering of antitumor immunity and the reprogramming of the tumor microenvironment (24). Zhao Q et al. (25) reported four cases of refractory advanced ICC or hilar cholangiocarcinoma effectively managed with anti-PD-1 antibody treatment following or concomitant with stereotactic body radiation therapy (SBRT). The median OS was greater than one year. Radical surgical resection was performed in one patient with a large lesion (12.9 × 11.8 cm) and multiple satellite lesions who was initially deemed unresectable. Thermal ablation therapy has shown remarkable local tumor control as a minimally invasive option for patients with unresectable hepatic malignancies, including cholangiocarcinoma (26). Xie C et al. (27) explored the efficacy of the combination of tremelimumab and microwave ablation in 20 patients with advanced BTC. An ORR of 12.5% and a DCR of 50% were observed. The median PFS, TTP, and OS were 3.4 months, 3.3 months and 6.0 months respectively. The combination treatment was well tolerated, with less than 10% of patients experiencing grade 3-4 toxicity. The outcomes of representative studies on the combination of ICIs with immunotherapy, chemotherapy, anti-angiogenics or local therapy in advanced BTC are summarized in Table 1 (14, 21, 22, 27–41).

Table 1 | Selected published trials on the combination of ICIs with immunotherapy, chemotherapy, anti-angiogenics or local therapy in advanced biliary tract cancer.

There are few reports on the efficacy of the combination of angiogenesis/checkpoint blockade with chemotherapy in advanced ICC. In a recent phase II trial, the combination of toripalimab and lenvatinib plus gemcitabine and oxaliplatin (GEMOX) was evaluated as a first-line therapy for locally progressed or metastatic ICC (40). The ORR was 80% (24/30), and the DCR was 93.3% (28/30) among the 30 enrolled patients. CR was observed in one patient. Three patients with locally advanced disease received resection after being successfully downstaged. The median PFS was 10.0 months, and the median DOR was 9.8 months. In this case, we adopted similar combination treatment strategies for a patient with stage IIIB ICC. To our knowledge, this is the first case report describing a patient with unresectable ICC who received conversion therapy with lenvatinib, pembrolizumab and GP chemotherapy and subsequently underwent radical liver resection.

The combination treatment has significant antitumor activity. After six cycles of treatment, the patient with initial unresectable ICC finally received radical resection without recurrence or distant metastasis after 15 months of follow-up. The possible mechanisms of the synergistic antitumor effect of combination therapy with ICIs, antiangiogenic agents, and chemotherapy may include the following: First, chemotherapy may improve the effectiveness of immunotherapy by reducing the microenvironment’s immunosuppressive effects, increasing the cross-presentation of tumor antigens, and facilitating immune cell infiltration into the tumor core (42–44). Second, antiangiogenic therapy has several immunostimulatory effects, including an increase in the trafficking of T cells into tumors and a decrease in immunosuppressive cytokines and T regulatory cells (45–47). We found that the tumor marker CA19-9 dramatically decreased after only three cycles of treatment. The results of previous studies indicated that a decrease in plasma tumor markers after lenvatinib treatment was an important predictor of successful R0 resection for advanced HCC (48).

With the development of systemic therapy, especially immunotherapy, there has been a growing number of reports of unresectable BTCs being converted into resectable ones followed by surgical removal of the tumors.

A recent systematic review including patients with unresectable ICC from 10 trials indicated a surgical conversion rate of 17.3% (27/132) with the use of various therapeutic modalities (49). Miyamoto R et al. (50) reviewed the retrospective studies about conversion surgery with chemotherapy for initially unresectable BTC. The conversion rate ranged from 0.65 to 80%, and the R0 resection rate ranged from 4.8 to 80%. In a phase 2 (MISPHEC) trial, Edeline J. et al. (51) reported that 22% of patients with advanced ICC receiving radioembolization plus chemotherapy could be downstaged to surgical intervention, of which 8 patients survived after 24 months of follow-up. By means of systemic or regional therapy, adequate tumor downstaging was accomplished, and patients could obtain a generally favorable prognosis (52–54). Le Roy B. et al. (55) reported the short- and long-term outcomes of patients with advanced ICC treated with surgery after chemotherapy were comparable to those of patients with initially resectable ICC treated with surgery alone. Recently, an open-label phase II trial studied the efficacy of combining lenvatinib with a PD-1 inhibitor for the treatment of advanced BTC for the first time (39). Thirty-eight patients were enrolled, with an ORR of 42.1%, and a DCR of 76.3%. Thirteen (34.2%) achieved downstaging and underwent surgery, and six of them (46.2%) achieved a pathologic response. In order to augment the response rate and increase the proportion of patients downstaged to resectability, prospective studies comparing efficacy are needed to evaluate the optimal conversion therapy strategies for advanced ICC.

In our study, Lenvatinib and Pembrolizumab in combination with GP chemotherapy had a manageable safety profile and was well tolerated. The main treatment-related adverse events were haematological toxicities. In the ABC-02 randomized phase 3 study comparing gemcitabine plus cisplatin with gemcitabine alone in patients with advanced BTC, the rate of any hematologic toxic effect was 32.3% in the GP group and 23.6% in the gemcitabine alone group (6). In a single-arm, phase II trial including 50 patients with advanced BTCs, toripalimab in combination with GP chemotherapy showed encouraging efficacy and safety (33). Chemotherapy combined with immunotherapy did not increase hematological toxicity compared to chemotherapy alone. Jian Z et al. (40) reported that Grade 3 or 4 neutropenia and thrombocytopenia observed in 10% and 3.3% patient who received toripalimab, lenvatinib in combination with GEMOX chemotherapy for advanced ICC.

Conclusion

In conclusion, we reported a case of initially unresectable and advanced ICC that was successfully downstaged to surgical curative-intent treatment after receiving lenvatinib and pembrolizumab combined with GP chemotherapy. The findings suggested that this combination therapy was a potentially feasible and safe conversion therapy strategy for these patients. The dynamic change in the level of tumor markers might be an approach for the detection of tumor response. The exact safety and efficacy of antiangiogenic TKI and anti-PD-1 antibodies combined with chemotherapy in patients with advanced BTC need to be compared with chemotherapy alone in a prospective controlled study.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

WZ contributed to design of work. Z-YZ, B-XZ and X-PC contributed to acquisition, analysis, and interpretation of data. WZ and CL contributed to drafting and revising. All authors were accountable for all aspects of the work and approved the final version.

Funding

This study was supported by the National Natural Science Foundation of China (No. 81860117).

Acknowledgments

The authors would like to thank all members of the study team, the patient and their family. We also thank Dr. Dong Kuang from Department of Pathology in Tongji Hospital, for his valuable comments on the pathology interpretation.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Abbreviations

ICC, Intrahepatic cholangiocarcinoma; GP, gemcitabine plus cisplatin; ICIs, Immune checkpoint inhibitors; HCC, hepatocellular carcinoma; BTC, biliary tract cancer; ORR, objective response rate; AJCC, American Joint Committee on Cancer; MDT, multidiscipline team; PR, partial response; OS, overall survival; ESMO, European Society for Medical Oncology; DCR, disease control rate; DOR, duration of response; PFS, progression-free survival; SD, stable disease; SBRT, stereotactic body radiation therapy; GEMOX, Gemcitabine and Oxaliplatin.

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ORIGINAL RESEARCH
published: 09 January 2023
doi: 10.3389/fimmu.2022.1111246

Identification of a CD4+ conventional T cells-related lncRNAs signature associated with hepatocellular carcinoma prognosis, therapy, and tumor microenvironment

Lin Zhu  ¹,²,³,⁴,⁵†, Xiu-Ping Zhang  ¹,²,³,⁴†, Shuai Xu  ⁶†, Ming-Gen Hu  ²,³,⁴, Zhi-Ming Zhao ² ,³,⁴, Guo-Dong Zhao  ²,³,⁴, Zhao-Hui Xiao  ²,³,⁴ and Rong Liu  ¹,²,³,⁴,⁵*

¹ Medical School of Chinese PLA, Beijing, China , ² Faculty of Hepato-Biliary-Pancreatic Surgery, the First Medical Centre, Chinese People’s Liberation Army (PLA) General Hospital, Beijing, China, ³ Institute of Hepatobiliary Surgery of Chinese PLA, Beijing, China,⁴ Key Laboratory of Digital Hepatobiliary Surgery, PLA, Beijing, China, ⁵ The First Clinical Medical School, Lanzhou University, Lanzhou, China,⁶ Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Theodore Welling, New York University, United States
Kui Wang, Eastern Hepatobiliary Surgery Hospital, China

*Correspondence:
Rong Liu
liurong301@126.com

†These authors have contributed equally to this work and share first authorship

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 29 November 2022

Accepted: 19 December 2022

Published: 09 January 2023

Citation:
Zhu L, Zhang X-P, Xu S, Hu M-G, Zhao Z-M, Zhao G-D, Xiao Z-H and Liu R (2023) Identification of a CD4+ conventional T cells-related lncRNAs signature associated with hepatocellular carcinoma prognosis, therapy, and tumor microenvironment . Front. Immunol. 13:1111246. doi: 10.3389/fimmu.2022.1111246

Background

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide, and CD4+ T lymphocytes can inhibit hepatocarcinogenesis and mediate tumor regression. However, few studies have focused on the prognostic power of CD4+ Tconv-related lncRNAs in HCC patients.

Method

We obtained data from TCGA and GEO databases and identified CD4+Tconv-related lncRNAs in HCC. The risk score was constructed using lasso regression and the model was validated using two validation cohorts. The RS was also assessed in different clinical subgroups, and a nomogram was established to further predict the patients’ outcomes. Furthermore, we estimated the immune cell infiltration and cancer-associated fibroblasts (CAFs) through TIMER databases and assessed the role of RS in immune checkpoint inhibitors response.

Results

We constructed a CD4+ Tconv-related lncRNAs risk score, including six lncRNAs (AC012073.1, AL031985.3, LINC01060, MKLN1-AS, MSC-AS1, and TMCC1-AS1), and the RS had good predictive ability in validation cohorts and most clinical subgroups. The RS and the T stage were included in the nomogram with optimum prediction and the model had comparable OS prediction power compared to the AJCC. Patients in the high-risk group had a poor immune response phenotype, with high infiltrations of macrophages, CAFs, and low infiltrations of NK cells. Immunotherapy and chemotherapy response analysis indicated that low-risk group patients had good reactions to immune checkpoint inhibitors.

Conclusion

We constructed and validated a novel CD4+ Tconv-related lncRNAs RS, with the potential predictive value of HCC patients’ survival and immunotherapy response.

Keywords: CD4+ conventional T cells, long non-coding RNA, hepatocellular carcinoma, tumor microenvironment, prognostic signature

1 Background

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death with an increasing incidence worldwide (1). Risk factors of HCC pathogenesis include hepatitis virus infection, alcohol, non-alcohol fatty liver disease (NAFLD), and aflatoxin B1 intake (2). Research on the prevention and treatment of HCC continues to receive attention. According to 2022 Barcelona Clinic Liver Cancer (BCLC) recommendations, HCC is primarily treated by surgery (resection and transplant) and/or in combination with multiple adjuvant therapies, such as ablation, transarterial chemoembolization (TACE), and systemic treatment (3–8). These treatments, combined with molecular biomarkers like alpha-fetoprotein, have reduced liver cancer-related deaths to a certain extent, but the prognosis of HCC patients remains unsatisfactory. Therefore, it is necessary to find novel effective molecular biomarkers to guide clinical treatment and improve outcomes for HCC patients.

Early studies focused more on protein-coding genes because of their crucial roles in the biological process of HCC and noncoding RNAs were often ignored. However, in recent years, increasing evidence has confirmed that long noncoding RNAs (lncRNAs) play important roles in the pathological process, including HCC (9). LncRNAs played various roles in the HCC process. Previous studies showed that lncRNA HULC was upregulated in HCC and promoted proliferation, metastasis, and drug resistance of HCC (10, 11). While lncRNA-XIST could inhibit the growth and metastasis of HCC by interacting with miR-92b (12). LncRNAs also played key roles in regulating tumor microenvironment (TME) and tumor immune microenvironment (TIME). Up-regulating lncRNAs, like lncRNA-IFI6 and EGOT, could inhibit the expression of IFN-stimulated genes (ISGs) and lead to immunosuppression (13, 14). In addition, some HCC-related lncRNAs exist in body fluids that are easy to detect and analyze, thus they have the potential to become novel biomarkers for HCC patients (9).

Previous studies indicated that CD4+ T lymphocytes could inhibit hepatocarcinogenesis and mediate tumor regression (15, 16). In addition, some non-synonymous cancer mutations were immunogenic, and most immunogenic mutations could be recognized by CD4+ T lymphocytes (17). Deletion of CD4+ T lymphocytes led to the development of HCC (18). These findings demonstrated that CD4+ T conventional lymphocytes (CD4+ Tconv) played a key role in HCC. However, due to the limitations of sequencing technology, few studies focused on the prognostic ability of CD4+ Tconv-related lncRNAs in HCC patients.

With the development of single-cell RNA sequencing (scRNA-seq) technology, researchers can obtain more data about liver cancer and study HCC in greater depth. In this way, bioinformatic analysis can be used to process the accumulating scRNA-seq data, which can help us identify the key lncRNAs, predict the prognosis of HCC patients and learn more about TME and TIME of HCC. This study aimed to establish a CD4+ Tconv-related lncRNAs prognostic signature to predict the prognosis and systemic treatment effect of HCC patients. Based on scRNA-seq data and gene expression data, we constructed a CD4+ Tconv-related lncRNAs prognostic signature. We evaluated the signature efficacy in different clinical subgroups and assessed the relationship between risk signature and TME. Furthermore, we analyzed the systematic treatment efficacy in different risk groups, hoping to find more potential applications of our novel risk signature in clinical treatment.

2 Methods

2.1 Data collection

Gene expression data and corresponding clinical data for HCC were downloaded from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). Patients without survival information were excluded. HCC scRNA-seq data (GSE140228) in both platforms (SMART-seq2 and droplet-based platforms) were obtained from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/) (19).

2.2 Identification of CD4+Tconv-related lncRNAs in HCC

The Tumor Immune Single-cell Hub (TISCH) is a scRNA-seq database focusing on TME (20). We used TISCH to extract CD4+ Tconv-related genes from HCC scRNA-seq data. Pearson correlation was applied to calculate the correlation between lncRNAs and CD4+ Tconv-related genes. LncRNAs with absolute correlation coefficients > 0.5 and p <0.001 were considered CD4+Tconv-related lncRNAs (21). Co-expression networks were visualized using Cytoscape software (22).

2.3 Construction of CD4+Tconv-related lncRNAs risk score

The least absolute shrinkage and selection operator (LASSO) regression was performed to identify the optimal prognostic lncRNAs with 10-fold cross-validation (23). Univariate Cox regression was performed to further evaluate the LASSO results, and finally, six CD4+Tconv-related lncRNAs significantly associated with overall survival (OS) were selected. Based on these lncRNAs, a CD4+Tconv-related lncRNAs risk score (RS) was established using multivariate Cox regression. All patients were divided into the high-risk group and low-risk group based on the median RS, and Kaplan-Meier (K-M) survival analyses were performed to estimate OS between the two groups. Time-dependent receiver operating characteristic (ROC) curve analysis was used to assess the prognostic value of RS over time. In addition, all HCC patients in TCGA were randomly assigned into validation 1 and validation 2 groups according to a ratio of 1:1. K-M survival analysis and time-dependent ROC curve analysis were also performed in both validation cohorts.

To further understand the clinical relevance of the RS, we explored the role of the RS in different clinical subgroups (age, sex, American Joint Committee on Cancer (AJCC) stage, TNM stage, prior malignancy, pharmaceutical therapy, and radiation therapy). We assessed the Hazard Ratio (HR) for RS in different subgroups and used the forest plot to demonstrate the results.

Based on the risk score and other clinical data, we constructed a nomogram using “survival” and “rms” packages. The concordance index (C-index) and calibration curves were used to assess the accuracy and predictive ability of the model. In addition, Decision Curve Analysis (DCA) was used to evaluate and compare the clinical utility between the nomogram and AJCC stage.

2.4 Functional enrichment analysis

The “clusterProfiler” R package was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses (24). The difference between high-risk group and low-risk groups were estimated by Gene Set Enrichment Analysis (GSEA) algorithm (25). The gene set file for functional enrichment analysis was obtained from the MSigDB database (https://www.gsea-msigdb.org).

2.5 Tumor Microenvironment Analysis and Immunotherapeutic Effect

The Tumor Immune microenvironment analysis and immunophenoscore Estimation Resource (TIMER) database is a comprehensive resource for systematical analysis of immune filtrates and we use TIMER 2.0 to estimate the immune cell infiltrations between high-risk and low-risk groups (26). The “ESTIMATE” package was used to assess the immune scores for different risk groups (27). Meanwhile, immune checkpoint genes were used to estimate the immunotherapeutic sensitivity between high-risk and low-risk groups (28), and immunophenoscore obtained from The Cancer Group Atlas (TCIA, https://tcia.at/home) was used to predict the response to checkpoint blockade (29). Tumor mutation burden (TMB) data was obtained from the TCGA database and the “maftools” R package was used to analyze the TMB.

2.6 Statistical analysis

All statistical analyses were performed using R version 4.1.1. Differences between the two risk groups were assessed using the Mann-Witney-Wilcoxon test. Survival analysis was performed using the R packages “survival” and “survminer”. P<0.05 was considered statistically significant. The median value of continuous variables was recognized as the cutoff value.

3 Results

3.1 Analysis of liver cancer single-cell sequencing data

The workflow of this study was shown in Supplementary Figure 1 . We obtained single-cell sequencing (scRNA-seq) data from GSE140228 and analyzed the immune microenvironment of hepatocellular carcinoma based on the TISCH database. GSE140228 provides scRNA-seq data based on two platforms (SMART-seq2 and droplet-based platforms). We analyzed and visualized the major cell type of liver cancer, with conventional CD4+ T cells accounting for a large proportion of the HCC immune microenvironment. The HCC cancer scRNA-seq data analysis of GSE140228 in droplet-based and SMART-seq2 platforms were shown in Figure 1 and Supplementary Figure 2 respectively.

Figure 1 | HCC single-cell sequencing data from the droplet-based platform. (A) The UMAP plot of different cell types. (B) Ratios of different cell types.

3.2 Identification and functional enrichment of CD4+ Tconv-related genes and lncRNAs

We obtained CD4+ Tconv-related genes from GSE140228 in two platforms and identified 478 intersect CD4+ Tconv-related genes. Based on the intersecting CD4+ Tconv-related genes, the molecular mechanisms of these genes were clarified through the GO and KEGG pathway enrichment analysis. The enrichment analysis indicated the significant enrichment of immune-related functions and pathways, like MHC protein complex binding, MHC class II protein complex binding, T cell receptor binding, antigen processing and presentation, Th1 and Th2 cell differentiation, Th17 cell differentiation, natural killer cell mediated cytotoxicity, etc. We used network graphs to further demonstrate the relationship between selective GO and KEGG pathways results. Based on GSE140228 and TCGA data, we built a CD4+ Tconv-related gene and lncRNA co-expression network and identified 3877 CD4+ Tconv-related lncRNAs. The functional enrichment results and co-expression network were shown in Figure 2 .

Figure 2 | Functional enrichment and the co-expression network of CD4+ Tconv-related genes. (A, B) The dot plot and the network graph of GO analysis. (C, D) The dot plot and the network graph of the KEGG pathways analysis. (E) Co-expression network of CD4+ Tconv-related genes and lncRNAs.

3.3 The construction and validation of CD4+ Tconv-related lncRNAs prognosis signature for HCC

We obtained the CD4+ Tconv-related genes from scRNA-seq data of HCC and identify the CD4+ Tconv-related lncRNAs for further research. Based on TCGA-LIHC data, we extracted survival-related lncRNAs and used the lasso method and cox regression to construct an HCC prognostic risk signature score model. The lasso regression analysis was shown in Supplementary Figure 3 . The risk signature finally contained six lncRNAs and the risk score (RS) was calculated as follows: RS = (0.14741432*AC012073.1 exp.) + (0.07721218*AL031985.3 exp.) + (0.74153487*LINC01060 exp.) + (0.25333105*MKLN1-AS exp.) + (0.07475760*MSC-AS1 exp.) + (0.08527743*TMCC1-AS1 exp.). According to the median RS, all patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve analysis demonstrated that patients in the high-risk group had a worse prognosis (p<0.0001). Time-dependent ROC analysis was used to evaluate the predictive ability of the model, and the AUC values and 95% confidence interval (CI) at 1, 3, and 5 years for predicting OS were 0.71 (0.65-0.76), 0.66 (0.59-0.72), 0.67 (0.58-0.75) respectively.

TCGA-LIHC patients were randomly divided into two groups in a 1:1 ratio, and we used these two groups as validation cohorts for internal validation. The results showed that the high-risk group had a poorer prognosis in both validation cohorts, all six lncRNAs were highly expressed in the high-risk group, and time-dependent ROC analysis further confirmed the stability of the RS model (Validation cohort 1, AUC and 95% CI at 1, 3, and 5 years for predicting OS were 0.68 (0.60-0.77), 0.67 (0.58-0.76), and 0.71 (0.61-0.81) respectively; Validation cohort 2, AUC and 95% CI at 1, 3, and 5 years for predicting OS were 0.69 (0.60-0.78), 0.64 (0.54-0.75), 0.64 (0.50-0.77) respectively). We also demonstrated the trend of AUC over time in three cohorts and we found that the AUC value is relatively stable over time. The K-M curves, distribution of risk score, survival status and lncRNAs expression, and time-dependent ROC results were shown in Figure 3 and Supplementary Figure 4 . We analyzed the demographic data of both groups and the results showed that both groups had similarly representative clinical factors. The outcomes were shown in Supplementary Table 1 .

Figure 3 | Construction and validation of CD4+ Tconv-related lncRNAs prognosis signature. (A–C) K-M plots of training cohort, validation cohort 1, and validation cohort 2. (D–F) RS distribution, survival status, and heatmap of expression of six CD4+ Tconv-related lncRNAs in high-risk and low-risk groups in three cohorts. (G–I) Time-dependent ROC analysis of three cohorts.

3.4. Identifying the role of RS in clinical subgroups and establishing a prognostic nomogram.

We analyzed RS in patients with different clinical characteristics (including age, gender, AJCC stage, T stage, N stage, M stage, prior malignancy, pharmaceutical therapy, and radiation therapy) to further develop the application value of RS and test its accuracy. The forest plot demonstrated that the high RS was a poor prognostic factor in most clinical subgroups except for patients in the N1 stage, M1 stage, and receiving radiation therapy subgroups. The forest plot was shown in Figure 4A . Microvascular invasion was one of the most important prognostic factors in HCC, and it was necessary to estimate the relationship between RS and microvascular invasion. According to the previous study, MYC, CREBZF, HOXD13, and ZBTB17 were the most significant upstream regulators of the vascular invasion-related transcriptome (30). We compared the gene expression between two groups and high-risk group patients had higher expression levels of vascular invasion-related genes, which partly contributed to a poor prognosis. The results were shown in Figures 4B–E .

Figure 4 | (A) Forest plot demonstrating the role of RS in different clinical subgroups. (B–E) Expression levels of vascular invasion-related genes in high-risk and low-risk groups. **P < 0.01, ***P < 0.001.

To improve the clinical utility of the CD4+ Tconv-related lncRNAs model, we constructed a nomogram to predict the OS of patients. Each patient had an integrated point based on their AJCC T stage and RS group. Using the integrated points, we can predict the 1, 3, and 5 years survival rates of patients. The concordance index (C-index) and 95% CI of the nomogram was 0.685 (0.651-0.719), indicating that the nomogram had a good ability to distinguish between high-risk and low-risk patients. Calibration curves were used to test the consistency between the predicted risk and actual risk of the prediction model, and the results showed that the OS predicted by the nomogram was close to the actual OS probability. Furthermore, we used DCA to compare the differences between our risk model and the AJCC stage, and the DCA showed that, in most cases, the risk model was not inferior to the AJCC stage in predicting OS of HCC patients. The nomogram, calibration curves, and DCA were shown in Figure 5 .

Figure 5 | Prognostic nomogram construction. (A) Nomogram integrating the RS and T stage. (B) Calibration curves for predicting 1,3 and 5 years OS. (C) DCA of the nomogram and AJCC stage.

3.5 The relationship between RS and tumor microenvironment

The TME plays an important role in the occurrence and development of liver cancer, so it is necessary to evaluate the relationship between RS and TME. We calculated immune scores in HCC patients using the ESTIMATE method, and we found that high-risk group patients tended to have higher immune scores. To further explore the TIME, we use the TIMER database and the MCPcounter method to estimate the cell infiltration of HCC patients. The high-risk group had a higher proportion of immune cell infiltration in B cell plasma, T cell follicular helper, macrophage M0 and myeloid dendritic cell resting, while the low-risk group had a higher infiltration rate of activated natural killer (NK) cells and activated mast cells. The MCPcounter result indicated that the high-risk group tended to have higher MCPcounter scores in CAFs. Correlation analyses of immune infiltration cells and RS achieved similar results. The results were shown in Figure 6 .

Figure 6 | The landscape of TME. (A) The relative cellular abundance of immune cells was calculated by CIBERSORT. (B) The ImmuneScore was calculated by ESTIMATE. (C) The relative infiltration degree of CAFs between the two groups. (D–I) The correlation analysis of immune infiltrating cells and RS. ns, no significance, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

We assessed the top 30 mutated genes in both risk groups. Furthermore, we determined several checkpoint genes (CD274, CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT) and found that most checkpoint genes had higher expression in the high-risk group (31). The oncoplot of TMB and expression levels of checkpoint genes were shown in Figure 7 .

Figure 7 | The analysis of TMB and expression levels of immune checkpoints genes. (A, B) Top 30 gene mutations in high-risk and low-risk groups. (C–H) Expression levels of immune checkpoints genes in high-risk and low-risk groups.

3.6 Evaluation of response to immunotherapy and chemotherapy in patients with liver cancer

The CD4+ Tconv-related lncRNA RS were closely related to TME and time in HCC patients. To explore the role of RS in response to immunotherapy, we obtained immunophenoscore (IPS) from the TCIA database and found that patients in the low-risk group tended to have higher IPS, suggesting that patients in the low-risk group were more sensitive to immunotherapy. The results demonstrated that our RS may contribute to the clinical treatment of HCC patients. We performed GSEA to further evaluate functional pathways that were significantly enriched in high-risk and low-risk groups. The results indicated that these pathways were related to cell cycle, MAPK signaling pathway, cytokine-cytokine receptor interaction, and NOD-like receptor signaling pathway. The results were shown in Figure 8 .

Figure 8 | Application of RS in chemotherapy and immunotherapy. (A) IPS scores for high-risk and low-risk groups. (B) GSEA results.

4 Discussion

HCC, the most common primary liver cancer, is a common solid tumor with a high recurrence rate and poor prognosis (32, 33). Systemic therapy is recognized as a crucial management component for HCC patients and a possible combination of immune checkpoint inhibitors (ICIs) is a promising approach for the treatment of HCC patients (34). However, the majority of patients do not respond to immunotherapy and some may deteriorate and develop hyperprogressive diseases (35, 36). CD4+ Tconv plays an important role in the antitumor response and can enhance patient immunity by secreting cytokines and activating CD8+ T lymphocytes (37, 38). Furthermore, LncRNAs are important prognostic markers for predicting the prognosis of HCC patients (39). Therefore, it is essential to identify the biomarkers of CD4+ Tconv-related lncRNAs that can guide patient-specific therapy choices (34). In this study, we conducted a comprehensive analysis of the prognostic value of CD4+ conventional T cells-related lncRNAs in hepatocellular carcinoma based on single-cell RNA sequencing data. We constructed a CD4+ Tconv-related lncRNAs risk score with good predictive power in validation cohorts and most clinical subgroups. We also constructed a nomogram with comparable OS predictive power compared to the AJCC staging system. Our risk score can also predict the immune cell infiltrations of the tumor immune microenvironment and response to immune checkpoint inhibitors.

In our research, we obtained the scRNA-seq data from GSE140228 and identified the CD4+ Tconv-related genes through the TISCH database. We demonstrated CD4+ Tconv-related lncRNAs using a co-expression network. Through Cox regression and LASSO algorithm, six CD4+ Tconv-related lncRNAs (AC012073.1, AL031985.3, LINC01060, MKLN1-AS, MSC-AS1, TMCC1-AS1) were identified, and used to construct a prognostic risk model. All these lincRNAs played essential roles in HCC biological process. Studies showed that lncRNA AC012073.1 played an oncogenic role in influencing HCC prognosis and was associated with N6-methyladenosine and ferroptosis (40, 41). AL031985.3 was associated with pyroptosis, immune response, ferroptosis, glycolysis, and autophagy in HCC (42–45). LINC01060 was aberrantly expressed in various cancers, including glioma and pancreatic cancer, and was significantly associated with poor clinical prognosis (46, 47). MSC-AS1 functioned as a key oncogene by inducing cell growth, cell cycle progression, and inflammatory mediators secretion (48). TMCC1-AS1 predicted poor prognosis and accelerated epithelial-mesenchymal transition in liver cancer (49). MKLN1-AS increased HDGF expression by acting as a molecular sponge for miR-654-3p, inducing pro-oncogenic effects during HCC progression (50). All these lincRNAs were highly expressed in the high-risk group. The accuracy and stability of the risk model were validated in Validation Cohort 1 and Validation Cohort 2, and the forest plot was used to explore the role of the risk model in different clinical subgroups. RS was a risk factor for most clinical subgroups, except in patients with N1 stage or M1 stage tumors or those receiving radiation therapy. We further developed a nomogram model containing risk score and T stage, used calibration curves to assess the predictive power of the nomogram, and compared the effect between the nomogram model and the AJCC stage. Our nomogram had good predictive power of OS, especially for 3-year OS, and it also had the comparable predictive ability of OS compared with the AJCC stage.

Furthermore, TME plays an essential role in tumor development. Recent clinical trials have demonstrated that immune cell infiltration of TME in situ is considered a valuable indicator of the prognosis and immunotherapy response of cancers (51). HCC processed a rather intricate TME, which contained abnormal extracellular matrix, immune cells, CAFs, tumor-associated endothelial cells or macrophages, and their products (52, 53). Cell-to-cell interactions within the TME, as well as genomic and epigenomic alterations, greatly contribute to tumor heterogeneity and determine its malignant nature including prognosis and drug resistance (52). We used multiple computational methods to assess the abundance of immune cells and other cancer-related cell infiltration to reveal TME in HCC patients. Our results showed that plasma cells, follicular helper T (Tfh) cells, macrophage M0 and myeloid dendritic cell resting were more enriched in the high-risk group, while activated NK cells and activated mast cells were more enriched in the low-risk group. ScRNA-seq results showed that B cells, predominantly plasma cells, were more enriched in HCC-infiltrating immune cells compared with the cirrhotic and healthy liver, and HCC patients with a low proportion of tumor-infiltrating plasma cells presented a better prognosis (54). Tfh cells were a subset of CD4+ T cells and were essential for the germinal center formation and the development of most high-affinity antibodies and memory B cells (55). NK cells can respond to cell-surface ligands and release cytokines (53). Tumor-associated macrophages could promote the expansion of human hepatocellular carcinoma stem cells by producing IL6 (56). Dendritic cells (DCs) were the most potent antigen-presenting cells (57). and our results suggested a higher infiltration rate of resting myeloid DCs in the high-risk group, which may contribute to a poorer immune response. The data from the ESTIMATE method showed that the high-risk group had a higher immune score, which could represent the higher levels of immune cell infiltration in the TME and was consistent with previous studies (58, 59). However, our study showed that high risk patients had poor immunotherapy response and high immune cell infiltration levels did not mean a better immunotherapy response. Exhausted or dysfunctional T cells may partly contribute to the results. Moreover, experiments and sequencing data were required to further explain the outcomes. In addition, the MCPcounter results indicated the high-risk group was associated with higher infiltration levels of CAFs. CAFs can maintain and enhance the stemness of HCC, including proliferation, self-renewal, migration, invasion, drug resistance, tumorigenesis, and metastasis of HCC cells (60). In summary, we inferred that the poor prognosis of high-risk group patients may be related to this tumor immunosuppressive microenvironment and high infiltration levels of CAFs.

In addition, we found that the expression levels of immune checkpoint genes were higher in the high-risk group. Negative regulation of immune checkpoint molecules was one of the major factors leading to the exhaustion or dysfunction of effector memory CD8+ T cell, and late exhausted or dysfunctional T cells, representing impaired proliferation ability and reduced production of cytokines, further led to a weakened anti-tumor response (61). High expression levels of TIGIT and PDCD1 were recognized as immunosuppressed tumors with the poorest prognosis (62). Meanwhile, we further predicted the response to immunotherapy in high- and low-risk groups of HCC patients using IPS, with patients in the low-risk group associated with increased immunogenicity. These results suggested that the RS was a potential predictor of immunotherapy response, which meant that RS could provide a reference for clinical practice.

Currently, many studies are focused on precision genomic medicine, and many predictive models have been developed in an attempt to predict patient outcomes. Many lncRNAs, like N6-methyladenosine-related lncRNAs, ferroptosis-related lncRNAs, pyroptosis-related lncRNAs, glycolysis-Related lncRNAs, contributed to a better understanding of HCC biological processes and helped predict the prognosis of HCC (40–45). However, few studies concentrated on CD4+ Tconv-related lncRNAs, and the roles of CD4+ Tconv-related lncRNAs remained unclear. It is generally believed that the complex heterogeneity greatly hinders the therapeutic effect of molecularly targeted drugs in patients with advanced HCC. The advent of scRNA-seq has contributed to a better understanding of tumor heterogeneity in malignant cells at the transcriptomic level (52). Furthermore, the droplet-based platform captures large-scale cells at the expense of limited gene coverage, whereas SMART-seq2 provides deep coverage for smaller numbers of cells (63). The combination of these two techniques can enable an in-depth understanding of the TME landscape (19). We constructed a CD4+ Tconv-related lncRNAs RS to predict the prognosis of patients and hoped to provide references for clinical practice.

Our study had some limitations. First, the data were based on public databases and the RS should be further validated through large-scale prospective studies and mechanistic experimental studies. Second, future studies could identify specific T cell subsets to further estimate the TIME of HCC. The model was constructed based on TCGA cohorts, which may compromise the applicability of the conclusion in other cohorts. Finally, some risk factors of HCC patients, like AFP levels and viral hepatitis, were missing from the database, which may reduce the predictive power of the nomogram.

5 Conclusion

In conclusion, we constructed a novel CD4+ Tconv-related lncRNAs risk signature based on scRNA-seq data. The risk signature could reflect the TME, drug response, and prognosis of HCC patients, which could provide references for clinical practice.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: https://portal.gdc.cancer.gov/; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE140228.

Author contributions

LZ, XPZ and SX conducted the analysis and wrote this manuscript. RL, LZ and XPZ designed this study. MGH, ZMZ, GDZ, ZHX contributed to manuscript writing and data analysis. All authors contributed to the article and approved the submitted version.

Acknowledgments

All authors would like to thank the Key Laboratory of Digital Hepatobiliary Surgery for their support.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2022.1111246/full#supplementary-material

Supplementary Figure 1 | Flowchart of the study.

Supplementary Figure 2 | HCC single-cell sequencing data from the SMART-seq2 platform. (A) The UMAP plot of different cell types. (B) Ratios of different cell types.

Supplementary Figure 3 | Lasso regression. (A) Cross-validation for tuning parameter selection in lasso regression. (B) Validation of tuning parameter selection by lasso regression.

Supplementary Figure 4 | Time-dependent ROC curves of (A) training cohort, (B) validation cohort 1, and (C) validation cohort 2.

Supplementary Table 1 | Baselines of patients in two validation cohorts

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ORIGINAL RESEARCH
published: 23 January 2023
doi: 10.3389/fimmu.2023.1073133

The effect of anti-PD-1/PD-L1 antibodies combined with VEGF receptor tyrosine kinase inhibitors versus bevacizumab in unresectable hepatocellular carcinoma

Hui Zeng ¹, Qi Xu ², Jinyu Wang ³, Xiaoqing Xu ⁴, Jun Luo ¹, Lei Zhang ⁵, Cong Luo ², Jieer Ying ² and Jingjing Li ²*

¹ Department of Interventional Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China, ² Department of Hepato-Pancreato-Biliary & Gastric Medical Oncology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China, ³ Medical Records and Statistics Department, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China, ⁴ The Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China, ⁵ Radiology Department, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Chun Wai Mai, UCSI University, Malaysia
Zuzana Macek Jilkova, Centre Hospitalier Universitaire de Grenoble, France

*Correspondence:
Jingjing Li
lijj@zjcc.org.cn

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 18 October 2022

Accepted: 02 January 2023

Published: 23 January 2023

Citation:
Zeng H, Xu Q, Wang J, Xu X, Luo J, Zhang L, Luo C, Ying J and Li J (2023) The effect of anti-PD-1/PD-L1 antibodies combined with VEGF receptor tyrosine kinase inhibitors versus bevacizumab in unresectable hepatocellular carcinoma . Front. Immunol. 14:1073133. doi: 10.3389/fimmu.2023.1073133

Introduction

Immune checkpoint inhibition (ICI) plus bevacizumab (BEV) is the standard first-line treatment for unresectable hepatocellular carcinoma (uHCC). We aimed to assess the efficacy and safety of ICI plus bevacizumab and ICI plus receptor tyrosine kinase inhibitor (TKI) in this patient population.

Methods

This retrospective single-institution study enrolled 94 patients with uHCC who received ICI plus TKI or bevacizumab as the first-line treatment. Progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and disease control rate (DCR) were used to evaluate treatment efficacy. RECIST v1.1 criteria were used to calculate the objective clinical response. Common Terminology Criteria for Adverse Events were used to report and categorize adverse events.

Results

By the last follow-up interview on May 15, 2022, there were 57 deaths, and 19 patients did not develop disease progression. Thirty patients received sintilimab/atezolizumab plus bevacizumab (ICI + BEV group), and 64 received ICI plus TKI (ICI + TKI group). The median OS was 430 days (95% CI, 266-NA) in the ICI+TKI group and 498 days (95% CI, 349-NA) in the ICI+BEV group (HR, 1.20; 95% CI, 0.69-2.07; P = 0.52). There was no significant difference between the two groups in the median PFS (182 vs. 221 days, P=0.67). In the ICI+TKI group, the ORR and DCR were 28.1% and 67.2%, respectively. In the ICI+BEV group, the ORR and DCR were 26.7% and 66.7%, respectively. The overall incidence of adverse events was similar between the two groups. Palmar-plantar erythrodysesthesia syndrome (23[36%]) occurred only in the ICI + TKI group. Patients who received ICI+BEV were more prone to upper gastrointestinal bleeding (2 [7%]), with one patient with grade 4 requiring emergency DSA treatment.

Conclusion

This study found that ICI+TKI and ICI+BEV as first-line treatments were similar in OS, PFS, and tumor response in uHCC. Different populations are suitable for different regimens because of the different adverse events.

Keywords: hepatocellular carcinoma, first-line treatment, immune checkpoint inhibition, VEGF receptor tyrosine kinase inhibitors, bevacizumab

1 Introduction

Hepatocellular carcinoma (HCC) is the most common malignancy worldwide, ranking 6th in incidence and 3rd in mortality in 2020 (1, 2). In China, the incidence and mortality rates of HCC are higher than the global average (1). The success of the SHARP study led to an era of targeted therapy for unresectable HCC (uHCC), and sorafenib has become the standard first-line treatment for uHCC (3). However, due to limited efficacy and intolerable treatment-related adverse events, the overall survival of patients with uHCC is poor and has not improved over the last decade. The KEYNOTE-224 (4) and CheckMate-040 (5) studies indicated that immune checkpoint inhibition (ICI) might be a treatment option for patients with uHCC. After the failure of the KEYNOTE-240 (6) and CheckMate-459 studies (7), a new therapeutic mode of immunotherapy in uHCC needs to be explored. Some studies have demonstrated that a combination of ICI and vascular endothelial growth factor (VEGF) receptor tyrosine kinase inhibitors (TKI) could prolong the survival of patients with uHCC (8, 9). Meanwhile, the IMbrave 150 (10) and Oriental-32 studies (11) showed that ICI plus bevacizumab, a monoclonal antibody that binds VEGF, improved the prognosis of patients with uHCC compared with that using sorafenib alone. However, no randomized controlled trials or retrospective studies have been conducted to compare the efficacy of ICI plus bevacizumab or ICI plus TKI in patients with uHCC. In this study, we aimed to compare the efficacy and safety of ICI plus VEGF receptor (VGFR) TKI or bevacizumab as first-line treatment for uHCC and explore the potential advantages of the two models.

2 Materials and methods

2.1 Study population

Patients with uHCC who received ICI plus VEGFR TKI or bevacizumab therapy at Zhejiang Cancer Hospital from January 2018 to June 2021 were enrolled in this study. Eligible patients were pathologically or clinically diagnosed with u HCC (not amenable to curative surgery or local treatment). The patients had not previously received systemic therapy for uHCC before receiving ICI plus VEGFR TKI or bevacizumab therapy. Patients received at least one dose of the study drug and underwent at least one post-baseline radiographic tumor response assessment according to the Response Evaluation Criteria in Solid Tumors, version 1.1 (RECIST 1.1). Patients lost to follow-up and those who had no clinical efficacy evaluation were excluded. All patients were followed up to the date of death or May 15, 2022. Clinical information, disease stage, drug information, and adverse events (AEs) were collected and recorded. The study complied with all relevant ethical regulations and was approved by the Institutional Review Board (IRB) and Ethics Committee (EC) of Zhejiang Cancer Hospital (approval no. IRB-2022-332(ke)).

2.2 Treatment

Treatment information, including the treatment start date, drug name, drug dose, laboratory data, radiological evaluation, and treatment-emergent AEs, was systematically recorded. The dosage of ICIs was administered intravenously according to the instructions; pembrolizumab, sintilimab, camrelizumab, and tislelizumab were administered at a dosage of 200 mg every 3 weeks, and atezolizumab was administered at a dosage of 1200 mg every 3 weeks. Bevacizumab was administered at a dose of 15 mg/kg every 3 weeks. No dose reduction was permitted for ICI or bevacizumab. The VEGFR-TKI drugs included lenvatinib, sorafenib, and apatinib. Lenvatinib, sorafenib, and apatinib were administered orally once daily at an initial dosage of 8 mg, 400 mg, and 250 mg, respectively.

2.3 Outcome assessment

Patients were followed up on May 15, 2022. Patients who did not experience disease recurrence at the time of the last follow-up or who had died were censored at the last follow-up or date of death, respectively. RECIST v1.1 criteria were used to calculate the objective clinical response the professional radiologists evaluated at the center of Zhejiang Cancer Hospital. Computed tomography/magnetic resonance imaging (CT/MRI) was performed every six weeks to assess treatment response. Progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and disease control rate (DCR) were used to evaluate treatment efficacy. Safety data were continuously assessed by monitoring adverse events, laboratory testing, vital signs, and physical examination. The Common Terminology Criteria for Adverse Events (CTCAE, version 4.0) were used to report and categorize adverse events.

2.4 Statistical analysis

Numerical data are described as mean ± SD and differences between groups were compared using the t-test. Categorical data are described by ratios, and the chi-square test was conducted to compare the differences between groups. The Kaplan–Meier method was used to estimate OS and PFS, and the log-rank test was used to analyze the comparisons. Cox proportional hazard modeling was used to estimate the hazard ratios (HRs) of each clinicopathological feature for PFS and OS. Univariate and multivariate logistic regression models were used to determine the predictors associated with ICI + TKI or ICI + BEV treatment response. Statistical significance was set at P < 0.05. Statistical analyses were performed using R-3.6.3 software.

3 Results

3.1 Baseline characteristics of all patients

A total of 94 patients with uHCC who received ICI plus VEGFR TKI or bevacizumab as first-line treatment were included in this study ( Figure 1 ). Thirty patients received sintilimab/atezolizumab plus bevacizumab treatment (ICI + BEV group). In comparison, 64 patients received anti-PD-1 antibodies plus VEGFR TKI therapy (ICI + TKI group). In the ICI+TKI group, anti-PD-1 antibodies included pembrolizumab, sintilimab, camrelizumab, and tislelizumab; TKIs included sorafenib, lenvatinib, and apatinib. The demographic and baseline characteristics of the 94 patients are summarized in Table 1 . In the ICI+TKI group, the median age was 57.6 years, and 87.5% were male; in the ICI+BEV group, the median age was 53.7 years, and 86.7% were male. Most patients were infected with HBV (87.5% in the ICI+TKI group and 93.3% in the ICI+BEV group). The ECOG performance score (ECOG PS), Barcelona Clinic Liver Cancer (BCLC) stage, portal vein tumor thrombus (PVTT), extrahepatic metastases (EHS), sites of metastasis, and ascites were well balanced between the two groups. The high AFP level group (AFP ≥ 200 ng/mL) and low AFP level group (AFP < 200 ng/mL) were also similar between the two groups. More patients in the ICI + BEV group had prior surgery.

Figure 1 | Trial profile.

Table 1 | Baseline Characteristics of the Study Participants.

3.2 Treatment and efficacy

By the last follow-up interview on May 15, 2022, of the total of 94 patients, there were 57 deaths, including 37 patients in the ICI+TKI group and 20 patients in the ICI+BEV group, with a median follow-up for overall survival of 450 days. Nineteen patients (20.2%) did not show disease progression, including 16 and 3 patients in the ICI+TKI and ICI+BEV groups, respectively. In the ICI+TKI group, 34 patients died of disease progression and 3 of unknown causes. In the ICI+BEV group, 18 patients died of disease progression and 2 of unknown causes. No treatment-related deaths occurred in this study. The median OS was 430 days (95% CI, 266 to NA days) in the ICI + TKI group and 498 days (95% CI, 349 to NA days) in the ICI + BEV group (HR, 1.20; 95% CI, 0.69 to 2.07; P = 0.52; shown in Figure 2A ). The median PFS was 182 days (95% CI, 113–227 days) in the ICI+TKI group and 221 days (95% CI, 141–449 days) in the ICI+BEV group (HR, 0.90; 95% CI, 0.56 to 1.45; P = 0.67; shown in Figure 2B ). Eighteen (28.1%) and 8 (26.7%) patients in the ICI+TKI and ICI+BEV groups, respectively, achieved an objective response. Of these patients, one CR was observed in each group, and the remaining patients showed PR. In total, there were 25 (39.1%) and 12 (40%) patients in the ICI+TKI and CI+BEV groups, respectively. Therefore, the overall radiologically confirmed ORR and DCR were 28.1% and 67.2%, respectively, in the ICI + TKI group, and 26.7% and 66.7%, respectively, in the ICI + BEV group ( Table 2 ).

Figure 2 | Kaplan-Meier estimates of progression-free and overall survival (A) Progression-free survival. (B) Overall survival.

Table 2 | Tumour responses according to RECIST version 1.1.

3.3 Subgroup analyses

To determine whether a specific patient population could benefit from ICI+TKI or ICI+BEV, we conducted a subgroup analysis (shown in Figure 3 ). An exploratory multivariate analysis using a Cox proportional hazards model identified no statistically significant differences in OS and PFS between subgroups.

Figure 3 | Subgroup analysis of progression-free survival and overall survival in intention-to-treat population at final analysis. (A) Progression-free survival. (B) Overall survival.

3.4 Safety

All treatment-related adverse events with an incidence of > 5%, or grades 3-4, are shown in Table 3 . In total, all patients who received ICI+TKI and 29 (97%) of 30 patients who received ICI+BEV experienced an adverse event. The overall incidence of adverse events was similar between the two groups; however, adverse events differed between the ICI+TKI and ICI+BEV groups. Fatigue was the most common adverse event in both groups (51 [80%] vs. 25 [83%]), with most being grade 1-2 and with no significant difference between the two groups. Palmar-plantar erythrodysesthesia syndrome occurred only in the ICI + TKI group, with an incidence rate of 36%; there was no palmar-plantar erythrodysesthesia syndrome in the ICI + BEV group. Of note, patients who received ICI + BEV were more prone to upper gastrointestinal bleeding (2 [7%]), and one patient with grade 4 bleeding required emergency DSA treatment.

Table 3 | Common treatment-related adverse events.

4 Discussion

To our knowledge, data on head-to-head comparisons across ICIs combined with VEGFR TKI and bevacizumab as first-line treatment for uHCC are sparse. This study is the first to compare the efficacy and safety of ICI plus VEGFR TKI and ICI plus bevacizumab as first-line treatment in patients with uHCC.

In 2008, the SHARP trial (3) demonstrated the superiority of sorafenib over placebo (OS, 10.7 months versus 7.9 months), which was a breakthrough in uHCC systemic therapies. Sorafenib has been the primary standard for uHCC for a decade. ICIs have shown significant therapeutic effects in various cancers. In the phase 2 KEYNOTE-224 trial (4), pembrolizumab was effective and tolerable in patients with uHCC who had previously been treated with sorafenib. Another phase 1/2 CheckMate 040 (5) trial showed that nivolumab could increase tumor reduction and ORRs of 15–20% in patients with uHCC. Compared with previous data on sorafenib, ICI showed better efficacy and safety in uHCC and is expected to become a new treatment regimen. However, the phase 3 KEYNOTE 240 trial (6) failed to show a statistically significant superiority of pembrolizumab over placebo in terms of OS and PFS. Concurrently, in another phase 3 trial Checkmate 459 trial (7), nivolumab treatment also did not significantly improve OS compared with sorafenib (15.2 months versus 13.4 months). Both the KEYNOTE 240 and CheckMate 459 trials failed, possibly related to the study endpoint set as OS. Under the existing conditions, the proportion of patients with uHCC receiving second-line and later- line treatment has increased significantly (12), which has had a great impact on OS. Nevertheless, the clinical activity and favorable safety profile of ICIs have been observed in patients with uHCC.

Subsequently, the IMbrave 150 study showed that atezolizumab, an anti-PD-L1 antibody, plus bevacizumab had better outcomes in patients with uHCC than sorafenib, setting a new first-line median OS duration of 19 months (10). Similarly, the anti-PD-1 antibody sintilimab plus bevacizumab versus sorafenib improved OS in Chinese patients (11). The treatment mode of ICI plus TKI is widely used in clinical practice. Several previous small-sample studies have shown that ICI plus TKI improves the survival of patients with uHCC, including pembrolizumab plus lenvatinib, camrelizumab plus apatinib, and nivolumab plus lenvatinib (8, 9, 13). By 2022, the European Society for Medical Oncology Congress phase 3 LEAP-002 trial showed that, compared with lenvatinib, pembrolizumab plus lenvatinib did not meet its dual primary endpoints of OS and PFS as a first-line treatment for patients with uHCC (14). However, there were trends toward improvement in OS and PFS for patients who received pembrolizumab plus lenvatinib versus those who received lenvatinib alone. Sorafenib was used as controls for IMbrave150 and Orient-32, while that for the LEAP-002 was lenvatinib. Lenvatinib was marketed globally in the REFLECT study, a non-inferiority designed phase III clinical trial, and has become the standard first-line treatment for advanced liver cancer with sorafenib (15). In the REFLECT study, lenvatinib had a slight advantage over sorafenib in OS (HR=0.92, 95%CI: 0.79-1.06) but had a considerable advantage in PFS (HR=0.66, 95% CI: 0.57-0.77). In addition, a network meta-analysis showed that the estimated median HR for OS and PFS of atezolizumab plus bevacizumab versus lenvatinib was 0.63 (95% CI 0.39-1.04) and 0.91 (95% CI 0.42-1.99) (16), which was similar to those observed by Sonbol (17). In other words, ICI+TKI is not superior to lenvatinib but may be superior to sorafenib. This was proven in a phase 3 study at the 2022 European Society for Medical Oncology Congress. The trial showed that camrelizumab (anti-PD-1 IgG4 monoclonal antibody) plus rivoceranib (apatinib; VEGFR2-TKI) significantly prolonged PFS and OS and improved ORR in uHCC compared to sorafenib (18).

The choice of ICI+BEV or ICI+TKI for the first-line treatment of patients with uHCC is a practical problem that is a challenge for clinicians. Our study collected two relatively matched groups of patients at baseline from one center during the same period and compared the efficacy of ICI plus TKI and ICI plus bevacizumab in the first-line treatment of uHCC. The results showed no statistical differences between the two groups in terms of OS, PFS, and ORR. In the subgroup analysis, the results of the Kaplan–Meier analysis showed no statistically significant difference between the two groups. This suggests that the short-term and long-term efficacies of ICI+TKI and ICI+BEV in the first-line treatment of uHCC are similar.

The main difference between the two groups was the combination of drugs with ICI namely, bevacizumab in one group and receptor TKI in the other. Bevacizumab is a recombinant human monoclonal IgG1 antibody that acts by inhibiting the biological activity of human VEGF (19). The TKI drugs used in this study included sorafenib, lenvatinib, and apatinib. Although the target sites of these drugs include FGFR, PDGFR, and KIT, the main target site of these drugs is still VEGFR (20–22). In tumor tissues, VEGF released by hypoxic cancer cells and vascular endothelial cells plays an important role in promoting tumor growth, invasion, and metastasis. VEGF can enhance the activity of regulatory T cells (Tregs), promote the release of immunosuppressive cytokines, and mobilize tumor-associated macrophages (TAMs) to promote their polarization toward the M2 phenotype (23–25). VEGF also activates myeloid-derived suppressor cells (MDSCs), which in turn release more VEGF (26). VEGF-induced Tregs, TAMs, and MDSCs reduce the proliferation and function of CD8+ cells. VEGF also prevents antigen-activated CD8+ cells from infiltrating tumor tissues through its effect on tumor angiogenesis (27). Bevacizumab and molecular- targeted drugs can inhibit VEGF activity. These agents increase antigen presentation by dendritic cells, promote T-cell activation in the priming phase, and improve T-cell migration from lymph nodes to tumor sites by normalizing the tumor blood vessels (28, 29). In addition, these agents have been found to inhibit the production of Tregs, TAMs, and MDSCs at tumor sites and to negatively regulate the expression of immunosuppressive cytokines such as TGF-β and IL-10 (30, 31). Therefore, administration of PD-1/PD-L1 antibody under these conditions could enhance the antitumor activity of T cells, which explains the synergistic effect of ICI combined with VEGFR-targeting drugs. Molecular targeted drugs, such as lenvatinib, can inhibit FGFR1–FGFR4, RET, KIT, and PDGFRα, which can also increase the efficacy of ICI (32, 33), but their effects may be limited and cannot play a decisive role in the treatment of uHCC.

In addition to the efficacy, the treatment-related adverse events also deserve special attention. In our study, most patients in both groups experienced adverse events. Common treatment-related adverse events in both groups included fatigue, increased ALT or AST, proteinuria, and decreased platelet count, consistent with previous studies (10, 11, 34). There were no significant differences in immune-related adverse events between the two groups. However, the incidence of grade 3/4 serious adverse events in the ICI + TKI group was higher than that in the ICI + BEV group. The main difference was that the incidence of palmar-plantar erythrodysesthesia syndrome in the ICI+TKI group was 12%, which has been observed in previous studies (3, 15, 35). In contrast, no such adverse events were observed in the ICI + BEV group. Severe palmar-plantar erythrodysesthesia syndrome has a significant impact on the patients’ quality of life. Therefore, patients in the IC+BEV group had better self-perception. However, it is of particular concern that patients who received ICI + BEV were at a greater risk of upper gastrointestinal hemorrhage. In this study, two patients in the ICI+BEV group developed complications related to upper gastrointestinal hemorrhage. Among them, a patient with cirrhosis and severe esophagogastric varices detected by esophagogastroduodenoscopy developed life-threatening upper gastrointestinal bleeding after ICI+BEV treatment and received emergency DSA treatment. The patient did not restart IC + BEV treatment. A real-world study found no correlation between the grade of varices at pretreatment esophagogastroduodenoscopy and bleeding events (36). The main effect of bevacizumab is inhibition of the VEGF signaling pathway. VEGF plays a vital role in maintaining the architecture and integrity of the microvasculature of endothelial cells. When bevacizumab blocks VEGF signaling, endothelial cells are impaired in their ability to repair and renew wounds, leading to an increased risk of bleeding (37, 38). In addition, some studies have found that the risk of bleeding is positively correlated with the dose level of bevacizumab (39), and the dose of bevacizumab in HCC is relatively high (15 mg/kg); therefore, the risk of bleeding cannot be ignored. We still need to carefully choose bevacizumab in such patients to prevent life-threatening upper gastrointestinal bleeding.

Our study has some limitations. First, it was retrospective in nature, with the possibility of selection bias. Atezolizumab plus bevacizumab, or sintilimab plus bevacizumab, is expensive, and the patients in this group were better off financially than those in the ICI + TKI group. Bevacizumab was previously associated with an increased risk of bleeding in patients with uHCC with significant cirrhosis; therefore, these patients avoided bevacizumab. Second, patients were only selected from one hospital, and the results may be influenced by practices specific to that hospital. Third, this study had a small sample size and few events. Fourth, the drugs used in this study were diverse, especially in the ICI+TKI group, which included four ICIs and three TKIs. These limitations may have had an impact on the results of this study. More studies, especially phase III randomized controlled trials, are needed to further validate the results of this study.

5 Conclusion

We compared the efficacy and safety of ICIs combined with VEGFR TKI and ICIs combined with bevacizumab as first-line treatments for uHCC. Our research found that the two treatment regimens were similar in OS, PFS, and tumor response. Concurrently, there were some differences in the adverse events between the two treatment regimens. Therefore, this observation may provide a significant reference for future clinical practice.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving human participants were reviewed and approved by Institutional Review Board (IRB) and Ethics Committee (EC) of Zhejiang Cancer Hospital. The ethics committee waived the requirement of written informed consent for participation.

Author contributions

Concept and design: JJL and HZ. Recruitment and management of patients and approval of final manuscript: JJL, QX, JW, XX, JL, LZ, CL, JY, and HZ. Statistical analysis: JW and JJL. Drafting of manuscript: JJL, XX, JW, and HZ. All authors contributed to the article and approved the submitted version.

Funding

This study was funded by Zhejiang Provincial Natural Science Foundation of China (LQ20H160005).

Acknowledgments

We thank the patients and their families.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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ORIGINAL RESEARCH
published: 25 January 2023
doi: 10.3389/fimmu.2023.1115706

Alkaline phosphatase combined with γ-glutamyl transferase is an independent predictor of prognosis of hepatocellular carcinoma patients receiving programmed death-1 inhibitors

Lei Xu ¹†, Lin Chen ¹†, Bin Zhang ¹†, Zhicheng Liu ¹, Qiumeng Liu ¹, Huifang Liang ¹, Yifa Chen ¹, Xiaoping Chen ¹,²*‡, Chao Leng ¹*‡ and Bixiang Zhang ¹*‡

¹ Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China, ² Department of Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Mingyue Cai, The Second Affiliated Hospital of Guangzhou Medical University, China
Hongwei Cheng, Xiamen University, China

*Correspondence:
Xiaoping Chen
chenxpchenxp@163.com
Chao Leng
lengchaojdld@163.com
Bixiang Zhang
bixiangzhang@hust.edu.cn

†These authors have contributed equally to this work and share first authorship

‡These authors share last authorship

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 04 December 2022

Accepted: 09 January 2023

Published: 25 January 2023

Citation:
Xu L, Chen L, Zhang B, Liu Z, Liu Q, Liang H, Chen Y, Chen X, Leng C and Zhang B (2023) Alkaline phosphatase combined with γ-glutamyl transferase is an independent predictor of prognosis of hepatocellular carcinoma patients receiving programmed death-1 inhibitors. Front. Immunol. 14:1115706. doi: 10.3389/fimmu.2023.1115706

Background

Immunotherapy plays an increasingly critical role in the systemic treatment of HCC. This current study aimed to establish a novel prognostic predictor of Programmed death 1 (PD-1) inhibitor therapy in hepatocellular carcinoma (HCC) independent of Child-Pugh grade.

Methods

Our study screened patients with HCC who received PD-1 inhibitors at Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology from January 2018 to December 2020. ALG grade was determined by the patient’s serum ALP and GGT levels before the initiation of PD-1 inhibitors. The endpoints of our study were overall survival (OS) and progression free survival (PFS). Follow-up ended at May 31, 2022.

Results

Eighty- five patients (77 with Child−Pugh grade A, 8 with Child−Pugh grade B at baseline) were enrolled according to the inclusion criteria. Patients with Child−Pugh grade A achieved longer PFS and OS than those with Child−Pugh grade B. Patients with ALG grade 3 at baseline showed worse tumor response and poorer survival, and ALG grade could stratify patients with Child−Pugh grade A into subgroups with significantly different prognosis.

Conclusions

ALG grade, combining ALP and GGT, is a novel and readily available prognostic marker and the predictive effect of ALG grade on patient prognosis is independent of Child−Pugh grade.

Keywords: hepatocellular carcinoma, Child-Pugh grade, ALG grade, PD-1 inhibitors, prognosis

Introduction

Hepatocellular carcinoma (HCC) accounts for approximately 90% of primary liver cancers, and it is one of the leading causes of cancer-related death worldwide (1). Immunotherapy plays an increasingly critical role in the systemic treatment of HCC (2, 3). Previous clinical trials have confirmed the definite antitumor efficacy of programmed death-1(PD-1) inhibitors on HCC (4, 5). The newly released results of RATIONALE-301 (Tislelizumab versus Sorafenib) and LEAP-002 (Lenvatinib plus pembrolizumab versus Lenvatinib) on the European Society for Medical Oncology in 2022 highlighted the key role of PD-1 inhibitors monotherapy in the treatment of HCC (NCT03412773, NCT03713593). PD-1 inhibitors are emerging as a critical part of guideline-recommended first-line therapy for advanced hepatocellular carcinoma (NCT03298451) (6).

Preserved liver function is the premise of various treatments for HCC, and it is an important predictor of superior prognosis (7). Previous research confirmed that better liver function was positively associated with the prognosis of patients after hepatectomy (8), local therapy (9, 10) and systemic therapy (11, 12). Child−Pugh grade is extensively used to evaluate patient liver function when systemic therapy is recommended, and most patients are in Child−Pugh grade A, so it is necessary to develop other liver function evaluation systems to further stratify them into different groups according to their prognosis.

Alkaline phosphatase (ALP) and γ-glutamyl transferase (GGT) are readily available as part of serum liver function tests, and they are elevated in the presence of cholestasis or liver parenchymal damage. Previous studies suggested that elevated serum ALP and GGT were independent risk factors for the prognosis of HCC patients (13, 14). Other scholars have found that ALP and GGT have the potential to be used as biomarkers to predict the cancer risk and mortality of all comers (15, 16). Few studies have explored the role of ALP and GGT in the prognosis of HCC patients receiving programmed death 1(PD-1) inhibitors.

In this retrospective study, we analyzed the data of patients with HCC who received PD-1 inhibitors to explore the prognostic effect of ALP and GGT in HCC.

Methods

Patients

Our study screened patients with HCC who received PD-1 inhibitors at Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology from January 2018 to December 2020. To further explore the risk factors associated with PD-1 inhibitor therapy for hepatocellular carcinoma, we enrolled patients receiving PD-1 inhibitors monotherapy without targeted drug therapy. The inclusion criteria were as follows: 1) confirmed HCC diagnosed pathologically or clinically; 2) regular treatment with PD-1 inhibitors no less than 2 cycles; 3) no history of molecularly targeted drug therapy; 4) case data sufficient for efficacy evaluation; 5) at least 18 months of follow-up since the start of the PD-1 inhibitors. We collected the patients’ basic information, treatment history, and relevant fluid examination and imaging results. The current study was approved by the Medical Ethics Committee of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology (TJ-IRB20220936).

Treatment evaluation

Follow-up ended at May 31, 2022. The endpoints of our study were overall survival (OS) and progression free survival (PFS). Overall survival was defined as the interval between the initiation of PD-1 inhibitor treatment and death or the last follow-up, and PFS was defined as the interval from the initiation of PD-1 inhibitor therapy to confirmed tumor progression or the last follow-up. Tumor response was evaluated according to the immune Response Evaluation Criteria in Solid Tumors (iRECIST) (17). Tumor response was divided into four grades: immune complete response (iCR), immune partial response (iPR), immune unconfirmed progressive disease (iUPD), immune confirmed progressive disease (iCPD) and immune stable disease (iSD). Tumor assessment was performed every 6-12 weeks. Liver cirrhosis was confirmed according to the diagnostic criteria issued by the Chinese Medical Association (18).

Alkaline phosphatase & gamma-glutamyl transferase (ALG) grade

ALG grade was determined by the patient’s serum ALP and GGT levels before the initiation of PD-1 inhibitors. The normal ranges of serum ALP and GGT are 40-130 U/L and 10-71 U/L, respectively, in Tongji Hospital. Both serum ALP and GGT higher than normal values were recorded as ALG grade 3, serum ALP or GGT higher than normal was defined as ALG grade 2, serum ALP and GGT within normal ranges were recorded as ALG grade 1.

Statistics

Data analysis was performed by SPSS 26.0 and GraphPad Prism 8 software. Continuous variables were compared by independent samples t-test or Mann−Whitney U test, while categorical variables were compared by chi-square test and Fisher’s exact probability test. Spearman rank correlation analysis was used to test the correlation between rank variables. Kaplan−Meier survival analysis and log-rank test were used to calculate survival curves and compare differences. Univariate and multivariate cox regression models were performed to explore the risk factors of overall survival and progression free survival. P < 0.05 was considered statistically significant.

Results

Patient characteristics

A total of 395 HCC patients received PD-1 inhibitors at Tongji Hospital during the study period, and 85 patients (76 males and 9 females) were enrolled according to the inclusion criteria. Among the enrolled patients, forty-four received radical hepatectomy before immunotherapy. Different brands of PD-1 inhibitors were used in enrolled patients (sintilimab: n=50, pembrolizumab: n=9, camrelizumab: n=18, toripalimab: n=8), and none of them received molecularly targeted drugs. Seventy−seven of the enrolled patients (77/85, 90.6%) lived with Child−Pugh grade A at baseline, and the other 8 patients (8/85, 9.4%) lived with grade B. Patients were classified as ALG grade 1 (n=33), ALG grade 2 (n=26), and ALG grade 3 (n=26) according to serum ALP and GGT levels before the first PD-1 inhibitor treatment. The patients’ baseline characteristics were summarized in Table 1. It is worth noting that there was significant difference in the proportion of macrovascular invasion among patients with different ALG grades. Spearman rank correlation analysis showed that ALG grade was positively correlated with tumor macrovascular invasion (r=0.297, P=0.006).

Table 1 | Demographic characteristics of 85 patients with HCC who received immunotherapy.

At the end of follow-up, 36 patients suffered tumor progression, 23 patients died during follow-up, and none of them died from complications related to immunotherapy.

Tumor response

Among total 85 patients, five (5/85, 5.9%) achieved iPR, forty-five (45/85, 52.9%) patients achieved stable disease, and others (35/85, 41.2%) suffered tumor progression. The objective response rate (ORR) and disease control rate (DCR) were 5.9% (5/85) and 58.8% (50/85) in our cohort. Subgroup analysis showed that the DCR in patients with ALG grade 3 was significantly lower than that in patients with ALG grades 1, but there was no significant difference in DCR between patients with ALG grades 1 and 2. Patients with different ALG grades achieved similar ORRs. Table 2 summarized the tumor response of patients with different ALG grades.

Table 2 | Tumor response of patients with different grade of ALG.

Survival analysis

The mean PFS and OS of patients in the cohort were 12.986 (95% CI: 11.574-14.398) months and 15.326 (95% CI: 14.231-16.421) months, respectively. Child−Pugh grade and Albumin-Bilirubin (ALBI) grade are the most commonly used grading system for liver function evaluation. Both PFS and OS of patients with Child−Pugh grade A (PFS: 13.528 months, 95% CI: 12.087-14.968; OS: 15.930 months, 95% CI: 14.909-16.950) were significantly superior than those of patients with Child−Pugh grade B in our study (PFS: 9.513 months, 95% CI: 4.927-14.908; OS: 12.481 months, 95% CI: 9.952-15.009) (PFS: P=0.018, OS: P=0.002) (Figures 1A, B). PFS and OS were similar in patients with different ALBI grades in our study (Figures 1C, D).

Figure 1 | Prognostic analysis of patients receiving PD-1 inhibitors; (A). PFS of all enrolled patients according to Child-Pugh grade at baseline; (B). OS of all enrolled patients according to Child-Pugh grade at baseline; (C). PFS of all enrolled patients according to ALBI grade at baseline; (D). OS of all enrolled patients according to ALBI grade at baseline.

To explore more effective predictors, we conducted analysis according to the serum ALP and GGT values before the first dose of PD-1 inhibitors. Firstly, in the whole cohort, patients with normal serum ALP and GGT before anti-PD-1 treatment achieved longer PFS and OS than those with elevated serum ALP and GGT (Figures 2A, B). Subsequently, we found that ALP and GGT could further stratify the prognosis of patients with the same Child−Pugh grade. Elevated serum ALP and GGT predicted poor prognosis in patients with Child−Pugh grade A (Figures 2C, D). We further combined ALP and GGT as a new indicator to enhance prediction accuracy and ease of use. Both the PFS and OS of patients with ALG grade 1 (PFS: 15.316 months, 95CI: 13.640-17.082; OS: 17.127 months, 95CI: 16.240-18.015) were significantly longer than those of patients with ALG grade 3 (PFS: 9.984 months, 95CI: 7.203-12.765; OS: 12.481 months, 95CI: 9.952-15.009) (PFS: P=0.001, OS: P=0.010). Patients with ALG grade 2 (PFS: 12.832 months, 95CI: 10.263-15.401; OS: 15.885 months, 95CI: 14.066-17.703) also had longer PFS and OS than those with ALG grade 3, but the difference was not statistically significant (PFS: P=0.124, OS: P=0.103) (Figures 3A, B). The ALG grade could also stratify patients with Child−Pugh grade A into subgroups with significantly different prognosis (Figures 3C, D).

Figure 2 | Prognostic analysis of patients receiving PD-1 inhibitors; (A) Survival analysis of all enrolled patients with different serum ALP levels at baseline: (B) Survival analysis of all enrolled patients with different serum GGT levels at baseline; (C) Survival analysis of patients with Child-Pugh grade A according to serum ALP levels at baseline; (D) Survival analysis of patients with Child-Pugh grade A according to serum GGT levels at baseline.

Figure 3 | Prognostic analysis of patients receiving PD-1 inhibitors; (A) PFS of all enrolled patients with different ALG grade at baseline; (B) OS of all enrolled patients with different ALG grade at baseline; (C) PFS of patients with Child-Pugh grade A according to serum ALG grade at baseline; (D) OS of patients with Child-Pugh grade A according to serum ALG grade at baseline. Log-rank test was used to compare the prognostic differences between groups.

Moreover, there was no correlation between ALG grade and Child−Pugh grade in our study (P=0.100), indicating that the prognostic predictive effect of ALG grade is independent of Child−Pugh grade. Receiver operating characteristic (ROC) curves were generated to compare the predictive effects of indicators. The area under ROC curve of ALG grade, Child−Pugh grade, serum ALP and GGT levels at baseline for PFS was 0.657, 0.563, 0.620, and 0.610, respectively. The area under ROC curve of ALG grade, Child-Pugh grade, serum ALP and GGT levels at baseline for OS was 0.672, 0.585, 0.640, and 0.617, respectively. ALG grade is better than Child−Pugh grade in predicting the prognosis of patients, and has no correlation with Child−Pugh grade.

Prognostic factors

We established cox regression models to explore risk factors of PFS and OS (Tables 3, 4). Univariate analysis showed that ALG grade 3 (HR: 3.446, 95% CI: 1.519-7.819), ALP>130 U/L (HR: 2.342, 95% CI: 1.212-4.526), GGT>71 U/L (HR: 2.575, 95% CI: 1.209-5.485) and Child−Pugh grade B before immunotherapy (HR: 2.766, 95% CI: 1.143-6.692) were associated with PFS of patients receiving PD-1 inhibitors. Meanwhile, liver cirrhosis (HR: 3.500, 95% CI: 1.379-8.885), ALG grade 3 (HR: 4.087, 95% CI: 1.437-11.621), ALP>130 U/L (HR: 3.005, 95% CI: 1.324-6.822), GGT>71U/L (HR: 2.711, 95% CI: 1.006-7.307), AFP≥200 ng/mL (HR: 2.353, 95% CI: 1.018-5.440) and Child−Pugh grade B before immunotherapy (HR: 4.343, 95% CI: 1.595-11.827) were associated with the OS of patients. Multivariate Cox regression proved that ALG grade 3 (HR: 3.446, 95% CI: 1.519-7.819) was an independent risk factor of PFS, liver cirrhosis (HR: 3.514, 95% CI: 1.383-8.928) and Child−Pugh grade B before immunotherapy (HR: 4.518, 95% CI: 1.652-12.537) were independent risk factors of OS.

Table 3 | Cox regression analysis of PFS for patients receiving immunotherapy.

Table 4 | Cox regression analysis of Overall survival for patients receiving immunotherapy.

Discussion

Our study confirmed that elevated baseline serum ALP and GGT levels, as indicators of liver parenchymal damage, were prognostic risk factors of HCC patients receiving PD-1 inhibitors, and the interference of molecularly targeted drugs was excluded according to the inclusion criteria. We also reported that ALG grade, a new combination of ALP and GGT, was a superior predictor of prognosis for the first time, and ALG grade can further stratify patients with Child−Pugh grade A into subgroups with different prognoses.

PD-1 inhibitor therapy for HCC is an emerging systemic therapy, and liver function at baseline is a confirmed prognostic factor. Child−Pugh grade is one of the most common clinical indicators for evaluating liver reserve capacity, and studies have confirmed that the prognosis of patients with Child−Pugh Grade B were worse than that of patients with Child−Pugh Grade A (19), and Albumin-Bilirubin (ALBI) grade is another common liver function scoring method that can replace Child−Pugh score (20). Our study found that Child-Pugh grade, rather than ALBI grade was associated with patient prognosis. Since most patients receiving systemic therapy have good liver function reserve in the early stage, Child−Pugh grade and ALBI grade cannot accurately stratify the prognosis of such patients, we explored other indicators in serum liver function tests. In our study, both PFS and OS of patients with normal ALP and GGT levels at baseline were superior than those of patients with elevated ALP and GGT receiving PD-1 inhibitors. Previous studies supported that elevated ALP and GGT were associated with poor survival of patients receiving liver resection (21, 22), liver transplantation (23, 24), local therapy (25, 26) and molecular targeted therapy (27, 28). This was consistent with our findings in anti-PD-1 therapy, and it is noteworthy that serum ALP and GGT levels could stratify patients with Child−Pugh grade A, enhancing the accuracy of prediction in the current study. Cox regression further confirmed the results of survival analysis. Liver function is also a critical factor in the long-term survival of patients, and this is similar to our findings. We further found that ALG grade 3 was also a prognostic risk factor for patients with Child−Pugh grade A, and there was no significant correlation between Child−Pugh grade and ALG grade. ALG grade is a reliable prognostic factor independent of Child−Pugh grade, but ALG grade 3 was an independent risk factor for PFS but not for OS in our study. The reason of this result may be: 1. most of the patients enrolled have not yet reached the clinical endpoint, only 23 patients died during follow-up; 2. the follow-up time (18 months) was relatively short; 3. Statistical differences should not be the only factor in the validity of prognostic indicators, ALG grade 3 was risk factor for PFS which has confirmed the prognostic effect of ALG grade to a certain extent.

Serum ALP and GGT are common diagnostic indicators for liver diseases. As early as 1985, elevated GGT was systematically reported to be associated with the occurrence of liver cancer (29). Drug-induced liver injury is common in patients with advanced HCC, elevated serum ALP and GGT levels hinder recovery from drug-induced liver injury (30, 31), and the concentrations of ALP and GGT in the bile duct can be used as indicators to quantify bile duct injury (32). GGT increases the level of intracellular glutathione, which can promote tumor cells to maintain balance in reactive oxygen species and avoid cell death through redox pathways during pro-oxidant therapy (33), GGT-positive tumors possess stronger proliferative ability and drug resistance (34, 35). Researchers have demonstrated that granulocyte colony-stimulating factor (G-CSF) upregulates GGT1 and enhances the immunosuppressive function of myeloid-derived suppressor cells, and GGT inhibitors can alleviate tumor immunosuppression and the tumor-promoting effect of G-CSF (36). Alkaline phosphatase is ubiquitous expressed in human body, which has four types: intestinal ALP, placental ALP, germ cell ALP and single tissue non-specific ALP (37). Previous studies have found that placental ALP is highly expressed in HCC cell lines, and this type of ALP can transform cells with specific survival advantages to malignant transformation (38). Tumor cells with ALP overexpression are more likely to form immunogenic cold tumors, and targeted killing of ALP-overexpressing tumor cells can convert immunogenic cold tumors to hot (39). This further confirms the plausibility of our finding that GGT and ALP are adverse long-term prognostic factors for HCC patients receiving PD-1 inhibitors.

There are still some limitations in the current study: First, this is a single-center, retrospective clinical study, and the level of evidence for the findings is not strong enough; Second, the number of participants in the study was relatively small, sufficient sample size and follow-up time can more realistically analyze the prognosis of patients; Third, systemic treatments are diverse for HCC patients, and our study only excluded the interference of molecular targeted therapy on risk factor analysis. Mutation and amplification of oncogenes and tumor suppressor genes also affect the long-term prognosis of patients with HCC (40). Multicenter, prospective clinical trials are still needed to further explore the role of ALG grade in the prognosis of patients with HCC receiving PD-1 inhibitors.

Our study indicated that ALG grade 3 at baseline predicted poor prognosis of patients with HCC receiving PD-1 inhibitors. ALG grade, combining ALP and GGT, is a novel and readily available prognostic marker and the predictive effect of ALG grade on patient prognosis is independent of Child−Pugh grade.

Data availability statement

The original contributions presented in the study are included in the article/supplementary materials. Further inquiries can be directed to the corresponding authors.

Ethics statement

Our study was approved by the Medical Ethics Committee of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology (TJ-IRB20220936). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

LX and CL wrote the paper; LX, BinZ, and ZL collected and analyzed the clinical data;QL and HL added part of clinical information; XC revised the manuscript; CL and BixZ designed this subject. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by National Natural Science Foundation of China (serial number: 81802931; 81400653).

Acknowledgments

We sincerely express gratitude for all our colleagues at Hepatic Surgery Center of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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ORIGINAL RESEARCH
published: 31 January 2023
doi: 10.3389/fimmu.2023.1067352

Tumor infiltrating T cell states and checkpoint inhibitor expression in hepatic and pancreatic malignancies

Shanshan Wan ¹†, Ende Zhao ¹,²†, Daniel Freeman ³, Daniel Weissinger ², Benjamin A. Krantz ²,⁴, Gregor Werba ², Lauren G. Khanna ⁴, Despina Siolas ²,⁴, Paul E. Oberstein ²,⁴, Pratip K. Chattopadhyay ²,³,⁵*, Diane M. Simeone ¹,²,³* and Theodore H. Welling ¹,²*

¹ Department of Surgery, NYU Langone Health, New York, NY, United States, ² Perlmutter Cancer Center, NYU Langone Health, New York, NY, United States, ³ Pathology, NYU Langone Health, New York, NY, United States, ⁴ Internal Medicine, NYU Langone Health, New York, NY, United States, ⁵ Talon Biomarkers, Mendham, NJ, United States

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Raquel Blanco, National Center for Biotechnology, Spanish National Research Council (CSIC), Spain
Pil Soo Sung, The Catholic University of Korea, Republic of Korea
Geoffrey William Mccaughan, The University of Sydney, Australia

*Correspondence:
Theodore H. Welling
Theodore.Welling@nyulangone.org
Diane M. Simeone
Diane.Simeone@nyulangone.org
Pratip K. Chattopadhyay
pratip@talonbiomarkers.com

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 11 October 2022

Accepted: 18 January 2023

Published: 31 January 2023

Citation:
Wan S, Zhao E, Freeman D, Weissinger D, Krantz BA, Werba G, Khanna LG, Siolas D, Oberstein PE, Chattopadhyay PK, Simeone DM and Welling TH (2023) Tumor infiltrating T cell states and checkpoint inhibitor expression in hepatic and pancreatic malignancies. Front. Immunol. 14:1067352. doi: 10.3389/fimmu.2023.1067352

Hepato-pancreatico-biliary (HPB) malignancies are difficult-to-treat and continue to to have a high mortality and significant therapeutic resistance to standard therapies. Immune oncology (IO) therapies have demonstrated efficacy in several solid malignancies when combined with chemotherapy, whereas response rates in pancreatic ductal adenocarcinoma (PDA) are poor. While promising in hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), there remains an unmet need to fully leverage IO therapies to treat HPB tumors. We therefore defined T cell phenotypic states, particularly in terms of immune checkpoint receptor expression, in the tumor microenvironment of HPB patients utilizing novel, multiparameter flow cytometry and bioinformatics analysis. We demonstrate the presence of CD103⁺ tissue resident memory T cells (TRM), CCR7⁺ central memory T cells, and CD57⁺ terminally differentiated effector cells across all HPB cancers, with simultaneous expression of multiple co-inhibitory checkpoint receptors. Terminally differentiated T cells lacking co-stimulatory receptors were more prevalent in PDA, whereas T cells expressing both co-inhibitory and co-stimulatory receptors were most prevalent in HCC, especially in early stage. HCC patients had significantly higher TRM with a phenotype that might confer restored activation in response to immune checkpoint therapies. Further, T-cell activation state and checkpoint expression did not change robustly in response to chemotherapy in PDA patients. These results support that HCC patients might benefit most from combined checkpoint therapies, whereas efforts other than cytotoxic chemotherapy will likely be necessary to increase overall T cell activation in CCA and PDA for future clinical development.

Keywords: pancreatic adenocarcinoma, hepatocellular carcinoma, cholangiocarcinoma, immune oncology, tumor immunity, T cell biology, checkpoint inhibitors

Introduction

Immune oncology (IO) treatments have demonstrated significant efficacy for many solid malignancies and new indications are actively under study in many ongoing clinical trials across cancer types (1, 2). Checkpoint inhibitor (CI) IO treatment has thus far been the cornerstone of these treatments, mainly targeting programmed death receptor 1 (PD-1; CD279) and its ligand PD-L1 (CD274; B7-H1) or cytotoxic T lymphocyte-associated protein 4 (CTLA4; CD152) (3). However, of hepato-pancreatico-biliary (HPB) cancers, pancreatic ductal adenocarcinoma (PDA) and cholangiocarcinoma (CCA) have been refractory to many chemotherapy regimens, including IO treatments, with the exception of hepatocellular carcinoma (HCC). While efficacy for HCC has been notable, therapeutic responses have still been limited with the initial Checkmate-40 trial evaluating nivolumab demonstrating an objective response rate (ORR) of 14% (4), and the phase III trial failing to meet statistical significance with respect to improving overall survival (OS). However, the recent ImBrave trial for advanced HCC patients evaluating atezolizumab and bevacizumab demonstrated an ORR of 27% with an improvement in median overall survival (OS) (HR 0.59, median OS 19.2 months versus 13.4 months) (5). Recently the TOPAZ-1 trial, targeting PD-L1 in addition to standard chemotherapy, demonstrated an increase in overall survival of 20% and ORR of 26.7% for advanced biliary cancer patients (2). IO therapy has thus far shown no clinical benefit in PDA patients except in cases of microsatellite instability. While many novel CI targeting agents and costimulatory checkpoint agonists are in early phase clinical trials in other cancer types, the possible efficacy and biologic rationale of these agents in HPB cancers remains to be determined.

In order to fully leverage IO treatments in HPB cancers, the tumor immune microenvironment (TME) landscape needs to be further defined to identify and evaluate immune targets as strategies for future clinical trials. Thus far the translational assessment of the HPB TME has been limited, due to insufficient tissue availability, inadequate technologies, or a lack of correlation to important clinical parameters. Consequently, the landscape and quantitation of T cell phenotypes in relation to stage, treatment, and type of HPB malignancy requires definition. Further, identifying T cell phenotypes in hepatic and pancreatic malignancies may elucidate resistance mechanisms to anti-PD-1 therapy and help to select patients for the ideal combination of IO targeting agents.

Tissue-resident memory T cells (TRM) are a subset of T cells with both effector and memory function, residing in peripheral tissues (6). TRM cells are characterized by the expression of tissue retention markers CD103 and CD69, and reduced expression of migration potential markers, such as CCR7 (7, 8). Additionally, C-X-C motif chemokine receptor 6 (CXCR6; CD186) and PD-1 were also reported to be core markers of TRM cells (9). In recent studies, TRM cells were reported to be a component of tumor-infiltrating lymphocytes (TILs), associated with improved response to immunotherapy and favorable clinical outcome (6). The phenotype, functional state and implication of TRM cells in HPB cancers is still unclear and needs further investigation.

In this study we analyzed tumors from 36 patients with HPB malignancies, including PDA, (HCC), and intrahepatic CCA. We hypothesized that distinct T cell phenotypes are present in each cancer type and that T cell phenotypes may correlate with clinical stage and prognosis. To explore these hypotheses, we utilized a novel high parameter flow cytometry platform to identify T cell activation states, and checkpoint molecule expression based on T cell surface marker expression. Our data show distinct HPB tumor microenvironmental T cell states and is the first study demonstrating major differences in T cell phenotypes and checkpoint molecule surface expression among HCC, PDA and CCA patients.

Methods

Patient subjects, tissue procurement and processing

Thirty-six patients with PDA (n=20), HCC (n=11), or intrahepatic CCA (n=5), identified at our multidisciplinary pancreatic and liver tumor programs underwent informed consent for tissue procurement and clinical-pathological demographic information under an IRB approved protocol. All samples were confirmed to be malignant by pathologist review. Subjects who were being considered for surgical resection were eligible as were PDA patients undergoing biopsy in preparation for either standard of care neoadjuvant chemotherapy or for chemotherapy as primary treatment. Biopsies of PDA patients were performed of either primary pancreatic tumors or liver metastases by EUS or percutaneous radiographic guidance, respectively (2-4 18-gauge cores). All HCC specimens were collected from surgical resection. CCA specimens were collected from either biopsy (n=2) or surgical resection (n=3). Freshly procured tissue was immediately placed in tissue storage media at 4°C, which was processed into small pieces with a scalpel and enzymatically digested into single cell suspensions in gentleMACS C tubes with Human Tissue Dissociation Kit by gentleMACS Octo Dissociator with Heaters (Miltenyi Biotec) for 30 minutes, then filtered through a 40 uM strainer. Cells were pelleted and freshly stained with antibodies for flow cytometry analysis within two hours from time of sample acquisition.

High parameter flow cytometry

A 25 color antibody panel was developed using ColorWheel software (10) to include T cell markers of differentiation, activation and trafficking along with co-inhibitory and co-stimulatory checkpoint molecules. The following mouse anti-human antibodies were used, all purchased from BD Biosciences, except where indicated: CD186 (Brilliant Blue (BB)515), CD137 (BB630), CD244 (BB700), CD57 (BB780), CD45RO (allophycocyanin (APC)), HLA-DR (R700APC), GITR (Cyanin 7 (Cy7)-APC), CD278 (Brilliant Violet (BV)421)), CD95 (BV510), CD103 (BV605), CD183 (BV650), CD134 (BV705), CD69 (BV750), CD4 (BV785), CCR7 (Brilliant Ultraviolet (BUV) 395), LIVE/DEAD Fixable Blue (ThermoFisher Scientific, Carlsbad, CA), CD3 (BUV496), CD25 (BUV563), CD366 (BUV661), PD-1 (BUV737), CD8 (BUV805), TIGIT (phycoerythrin (PE)), CD272 (PE-CF594), CD127 (PE-Cy5) and CD152 (PE-Cy7) (10). Data were collected on a custom BD Biosciences FACSymphony A5 30-parameter flow cytometer, then compensated and analyzed with FlowJo software (BD Biosciences). Data were checked for quality of staining and fluorescence aggregates, dead cells and cell doublets were excluded. Fluorescence intensity thresholds were then determined for each marker to distinguish positive from negative expression.

Dimension reduction analysis

Unbiased identification of cell clusters was performed using FlowJo software with Flow cytometry-based self-organizing maps (FlowSOM) (11) and t-distributed stochastic neighbor embedding (tSNE) (12) analysis. We used a combination of unbiased dimension reduction based on marker expression and manual gating validation of distinct cell clusters to analyze our datasets and compare among clinical parameters. The cell number of each sample was adjusted to a similar number by DownSample plugin in FlowJo and concatenated into one file to keep comparable cell numbers among different samples. The data was analyzed with tSNE, FlowSOM and ClusterExplorer plugins in FlowJo downloaded from https://www.flowjo.com/exchange/.

Combinatorial analysis

CD4⁺ and CD8⁺ T cell data were exported respectively for each sample for rapid computation of combinatorial phenotypes using the CytoBrute platform (RocketML) (10). Phenotypes consisting of every single marker, and all possible combinations of 2-15 markers were constructed, and enumerated across all samples. The top 1,000 most frequent combinatorial phenotypes were reported and compared across study groups. For some analyses, flow cytometry data files, gating thresholds and comparison groups were uploaded to TerraFlow (TerraFlow.app) for construction of all possible 1-5 marker phenotypes across the dataset. Phenotypes whose frequency differed statistically significantly between phenotypes were reported. TerraFlow also generated core phenotypes that summarize the most significant families of phenotypes. Finally, a simple set of markers that distinguished groups was identified using recursive feature elimination (13).

Statistical analysis

Data were presented in boxplot as median ± interquartile range (IQR): box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. Group comparisons were performed with the Mann-Whitney U test. Multiple comparisons were performed with the Kruskal-Wallis test. P < 0.05 were considered statistically significant.

Results

Patient clinical presentation and sample analysis

Tumor tissue from HCC (n=11 patients), CCA (n=5 patients) and PDA (n=20 patients) were collected from HPB cancer patients by either core needle biopsy or at time of surgical resection. The stage and clinical characteristics of these patients are listed in Table 1. HCC and intrahepatic CCA patients frequently had underlying liver disease (72 and 60%, respectively) with NASH, HBV, and HCV being the most common etiologies at 9, 27, and 36%, in HCC patients. None of the HCC patients had prior therapy. One of the CCA patients (20%) received chemotherapy (Gemcitabine + Cisplatin) before biopsy. Of the PDA patients, 45% had received prior neoadjuvant chemotherapy consisting of gemcitabine-based (20%) or FOLFIRINOX (25%) chemotherapy. Two PDA patients (10%) had received radiotherapy along with their chemotherapy. No PDA stage IV patients (n=5) had received chemotherapy prior to biopsy. PDA primary patient tumors demonstrated advanced disease of either lymph node metastasis or distant metastasis in 65% of patients. 35% (n=7) of PDA cases were from core biopsy tissue (2-4 18-gauge cores). Samples were accepted for analysis if there was sufficient CD4⁺ and CD8⁺ content (n>200 cells), respectively.

Table 1 | Patient clinical characteristics and demographics.

T cell distribution in HPB cancers

We began by quantitating the distribution of T cell subsets in the 3 HPB cancer types, to identify whether there may be distinct features in CD4⁺ and CD8⁺ T cell differentiation, activation, trafficking, phenotype, co-inhibitory and co-stimulatory checkpoint molecules in the tumor microenvironment (TME) using single marker analysis. The overall percentage of T cells were similar among all three cancer types. The majority of TILs were CD4⁺ T cells in all HPB tumors (Figure 1A), with an average percentage of 43.9% (1.15-82.7). PDA, HCC and CCA patients had a similar percentage of CD4⁺ T cells, CD8⁺ T cells, and CD4/CD8 ratio. In PDA, the median percentage of CD4⁺ T cells and CD8⁺ T cells were 54.6% (14.2%-78.6%) and 25.8% (12.2%-62.1%), and the CD4/CD8 ratio was 2 (0.23-4.42). In HCC, the median percentage of CD4⁺ T cells and CD8⁺ T cells were 40.95% (1.15%-82.6%) and 22.7% (0.35%-56.9%), and the CD4/CD8 ratio was 1.7 (0.56-71.29). In CCA, the median percentage of CD4⁺ T cells and CD8⁺ T cells were 53.4% (7.37%-66%) and 22.7% (1.98%-29.5%), and the CD4/CD8 ratio was 2.32 (1.25-3.72) (Figure 1A). The overwhelming majority (>90%) of CD4⁺ and CD8⁺ TILs in both PDA and HCC were antigen-experienced memory T cells (CD45RO⁺) (Figures 1B, C). Here we assessed tissue resident memory T cells (TRM), central memory (TCM) and terminally differentiated effector T cells. CD103, a marker for TRM cells with increased tumor antigen sensitivity and improved response to immunotherapy, was more prevalent in CD8⁺ than CD4⁺ cells in overall HPB patients. TRM-associated tissue homing markers CD69 and CXCR6 were highest in HCC TILs (Figures 1B, C) (14, 15) suggesting superior TRM recruitment and retention in HCC compared to PDA and CCA. Based on CCR7 expression (16), a marker for TCM with migratory capacity to secondary lymphoid organs (SLO) (17), a median of 27.37% (0.53%-99.22%) of CD8⁺ T cells and 31.98% (0-90.96%) of CD4⁺ T cells in HCC had a TCM phenotype; In PDA, 12.61% (0.26%-80.13%) of CD8⁺ T cells and 21.32% (0-56.97%) of CD4⁺ T cells had a TCM phenotype; In CCA, 13.17% (0.68%-14.85%) of CD8⁺ T cells and 20.29% (0.22-43.58%) of CD4⁺ T cells were TCM. TCM in HCC trended higher when compared to TCM in PDA and CCA, but did not reach statistical significance, based on CCR7 as a single marker (Figures 1B, C). CD127 (the IL7-receptor) also commonly marks TCM, but exhibited a different pattern than CCR7, as it trended to higher levels (amongst CD8⁺ T-cells) or is significantly elevated (amongst CD4⁺ T-cells) in PDA and CCA compared to HCC. CD57 is a marker for terminally differentiated effector T cells (18, 19). CD57+ memory CD8+ T effector cells have been shown to express cytolytic enzymes such as perforin and granzymes but may be more susceptible to apoptosis and senescence following stimulation (20). CD57⁺CD4⁺ T cells were 17.82% (6.68%-32.1%), 10.48% (3.98%-25.9%), 13.28% (5.26%-41.1%) in HCC, PDA and CCA, respectively, and CD57⁺ CD8⁺ T cells were 37.81% (24.83%-56.21%), 33.03% (11.67%-60.35%), 18.17% (16.83%-58.52%) in HCC, PDA and CCA respectively (Figures 1B, C). These findings demonstrated the presence of TRM, TCM and terminally differentiated effector CD8⁺ and CD4⁺ TILs with similar frequencies among HPB patients.

Figure 1 | Landscape of T cell differentiation, activation state, inhibitory and stimulatory checkpoint receptors of tumor infiltrating T cells in HCC, PDA and CCA. (A) Percentage of CD4+ T cells and CD8+ T cells in CD3+ T cells in HCC (n=11), PDA (n=20) and CCA (n=5) patients. (B, C) Boxplots showing frequency of T cell differentiation markers and activation markers in CD8+ T cells (B) and CD4+ T cells (C) in HCC, PDA and CCA patients. Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. Kruskal-Wallis test. (D) Inhibitory and stimulatory checkpoint receptors frequency of CD8+ T cell. Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. *p < 0.05; ** p < 0.01; Kruskal-Wallis test. (E) Inhibitory and stimulatory checkpoint receptors frequency of CD4+ T cell. Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. *p < 0.05; ** p < 0.01; Kruskal-Wallis test.

Immune checkpoint expression in HPB malignancies

Immune checkpoint molecules play crucial regulatory roles in promoting or suppressing anti-tumor immune responses and are therefore a target of ongoing therapeutic development for cancer immune therapy (21). The most common co-stimulatory and co-inhibitory immune checkpoint receptors on T cells were analyzed for each individual marker distribution in HPB patients (Figures 1D, E). Overall, co-inhibitory immune checkpoint receptors were more frequently expressed than co-stimulatory immune checkpoint receptors in HPB patients with PD-1 and TIGIT being the most frequent (Figures 1D, E). PD-1⁺ percentage in CD8⁺ T cells in HCC were 74.76% (46.79%-84%) compared to PDA 62.96% (1.52%-82.83%) and CCA 56.4% (22.98%-79.77%), although not statistically significant. PD-1⁺ percentage in CD4⁺ T cells were comparable among HCC 53.9% (35.77%-80.03%), PDA 45.76% (3.67%-72.11%) and CCA 51.15% (9.66%-71.16%). Notably, in all of the HCC patients of our cohort, PD-1⁺ percentage exceeded 46% and 35% in CD8⁺ and CD4⁺ T cells. In contrast, both PDA and CCA patients showed a wider range of PD-1 expression. In addition, TIGIT was expressed at a significantly higher frequency in HCC and CCA patients in CD4⁺ and in HCC CD8⁺ T cells compared to PDA patients. BTLA was expressed significantly higher in CD8⁺ T cells of CCA samples. This data suggests that HCC samples had more antigen-experienced T cells expressing co-inhibitory checkpoints such as PD-1, TIGIT and BTLA. Notably, CTLA4 was expressed at the lowest frequency of co-inhibitory immune checkpoint receptors in overall HPB patients (<9% median percentage, p < 0.001 compared to PD-1) (Figures 1D, E). Co-stimulatory checkpoint receptors median percentage of expression was less than 12% for OX40 (CD134), GITR, and 4-1BB (CD137) (Figures 1D, E). ICOS was the highest expressed co-stimulatory immune checkpoint receptor and significantly higher in HCC (35.38% in CD8, 61.66% in CD4), and CCA (23.27% in CD8, 48.19% in CD4), compared to PDA patients (3.27% in CD8, 22.34% in CD4). GITR was significantly higher in CD8⁺ T cells from CCA patients, compared to CD8⁺ T cells from HCC or PDA patients. These data demonstrated distinct single marker immune checkpoint expression profiles among HCC, PDA and CCA.

T cell phenotypes unique to HPB cancer type

Using specialized data analysis tools, like CytoBrute and TerraFlow, high parameter flow cytometry data can be mined to examine all the theoretically possible phenotypes. In this study, as many as 3²² theoretical phenotypes may be present in the data (where three conditions – positive, negative, and omitted are analyzed for each of 22 markers). Because some markers were rarely expressed, and because of the challenge of interpreting long higher-order phenotypes of marker combinations, we limited CytoBrute analysis to 15 markers and TerraFlow analysis was designed to sample all possible sets of six markers. Cell phenotypes that were exceedingly rare were removed from downstream analysis and comparison across patient groups. While the single marker analysis noted some differences among HPB cancers, we set out to identify unique, multi-parameter, T cell populations in these 3 distinct tumor types and ultimately to identify T cell populations of potential clinical significance. We first utilized unbiased hierarchical clustering and t-distributed stochastic neighbor embedding (tSNE) plots along with supervised annotation to visualize T cell subsets and significant phenotypes among HPB cancer patients (Figure 2A). tSNE projection of CD8⁺ T cells identified 33 clusters in HCC (n=11), PDA (n=20) and CCA (n=5) patients. Statistically significant enriched clusters in HCC (CH1, CH5, CC11) and PDA (CP3, CP5, CP7, CP-C2, CP-C3) were identified. When examining the CD8⁺ T cell compartment, several populations were noted in HCC patients to be more prevalent than in PDA and CCA patients (Figure 2B and Supplementary Figure 1). HCC patients demonstrated higher CD103⁺CD69⁺ TRM cells expressing co-stimulatory immune checkpoint receptor ICOS. Notably, these HCC-enriched ICOS⁺ TRM frequently also expressed one or more co-inhibitory immune checkpoint receptors including PD-1, TIGIT and Tim-3 (Figures 2C, D and Supplementary Figures 1C, D). These T cells are consistent with the phenotype of T partial exhausted (TPEX) which are exhausted memory cells that are capable of generating anti-tumor T effector (Teff) cells in response to immune checkpoint blockade. Distinct HCC-enriched clusters shown in tSNE, CH1 (ICOS⁺CD69⁺TIGIT⁺) and CH5 (ICOS⁺CD69⁺CD103hi HLA-DR⁺PD-1hiTIGIT⁺Tim-3⁺) demonstrated that ICOS⁺ TRM cells could potentially be suppressed by multiple co-inhibitory checkpoint receptors in HCC (Figures 2B, D). ICOS⁺ clusters CH1 and CH5 were significantly lower in PDA. Similarly, CH5 was significantly lower in CCA (Figure 3B). We used TerraFlow to provide further insight into the results observed in our other analyses. TerraFlow identified a number of phenotypes, not revealed by tSNE analysis, that differed between HCC and PDA (Figure 2E), variously highlighting the elevation of ICOS, TIGIT, CD69, and PD1 in distinguishing PDA from HCC (Supplementary Figures 2A–E) using machine learning and recursive feature analysis (14). TerraFlow revealed that HCC was highly enriched for ICOS+ TRM that frequently expressed PD-1 and/or TIGIT (Figure 2E), suggesting that immune checkpoint blockade of PD1 and/or TIGIT might release inhibition of the already expressed costimulatory molecule ICOS. Supplemental Figures 2C, F illustrate the p-values for all phenotypes compared between HCC and PDA. Importantly, the two critical markers that distinguished HCC and PDA-infiltrating CD8⁺ T cells were ICOS and CD69 (Supplementary Figures 2A–C). HCC was highly enriched for ICOS⁺ TRM that frequently expressed PD-1 and/or TIGIT (Figure 2E), further indicating the effector potential by boosting TRM via immune checkpoint blockade. In addition, a discrete T cell cluster CC11 (CD69⁺CD57hi) was enriched in HCC, suggesting HCC patients had more terminally differentiated T cells with potent effector function but can become senescent and undergo activation-induced cell death (Figures 2B–D). In HCC, two CD4⁺ T cell clusters, C2 and C6, were noted to have unique CD69⁺CD103⁺ TRM populations expressing ICOS, PD-1 and TIGIT (Figures 3A–D and Supplementary Figures 3A–D). C2 expressed higher CD95 and BTLA than C6, suggesting heterogeneity among these two populations with exhausted features. Consistently, TerraFlow analysis showed that HCC was enriched for CD4⁺ T cells expressing both co-stimulatory ICOS and co-inhibitory checkpoint PD-1, as well as CD57⁺ effector cells (Figure 3E).

Figure 2 | Distinct CD8+ T cell phenotypes in liver and pancreatic cancer. (A) t-distributed stochastic neighbor embedding (tSNE) projection of CD8+ T cells showing manual-gated clusters identified from HCC (n=11), PDA (n=20) and CCA (n=5) patients. Significantly enriched clusters in HCC (CH1, CH5, CC11) and PDA (CP3, CP5, CP7, CP-C2, CP-C3) are labeled. (B) Boxplots showing percentage of indicated HCC-enriched clusters and phenotype for CH1, CH5 and CC11 in CD8+ T cells from tSNE (A) comparing HCC, PDA and CCA. Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. *p < 0.05; Kruskal-Wallis test. (C) Histograms showing individual marker expression of the HCC-enriched clusters CH1 (red), CH5 (green) and CC11 (blue) in CD8+ T cells. (D) Expression of TRM-related markers, co-inhibitory/senescent markers, co-stimulatory markers and memory/activation markers in 33 clusters of CD8+ T cells is shown in heatmap which is arranged based on the expression level of CD103. (E) Combinatoric frequency analysis of CD8+ T cell marker combinations (TerraFlow-Methods) most frequent in HCC. Boxplots showing representative phenotypes of CD8+ T cell that are significantly increased in HCC compared to PDA. Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. **p < 0.01; *** p < 0.001; Kruskal-Wallis test. (F) Boxplots showing percentage of indicated PDA-enriched clusters and phenotype for CP3, CP5, CP7, CP-C2 and CP-C3 in CD8+ T cells from tSNE (A). Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. *p < 0.05, Kruskal-Wallis test. (G) Histograms of individual marker expression of the PDA-enriched clusters CP3 (blue), CP5 (red), CP7 (purple), CP-C2 (orange) and CP-C3 (green) in CD8+ T cells. (H) Combinatoric frequency analysis of CD8+ marker combinations (TerraFlow-Methods) most frequent in PDA. Boxplot showing phenotypes of CD8+ T cell that are significantly higher in PDA compared to HCC. Representative statistically and biologically significant phenotypes are shown. Non-parametric Mann-Whitney U test *p<0.05, **p<0.01, ***p<0.001.

Figure 3 | Distinct CD4+ T cell phenotypes in liver and pancreatic cancers. (A) tSNE projection of CD4+ T cells showing 16 manual-gated clusters identified from HCC (n=11), PDA (n=20) and CCA (n=5) patients. Significantly enriched clusters in HCC (C2, C6) and PDA (C3, C12, C13) are labeled. (B) Boxplots showing percentage of HCC-enriched clusters C2 and C6 in CD4+ T cells from tSNE (A). Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. **p < 0.01, Kruskal-Wallis test. (C) Histograms show the individual marker expression of CD4+ T cell clusters enriched in HCC, C2 (red) and C6 (blue). (D) Expression of TRM-related markers, co-inhibitory/senescent markers, co-stimulatory markers and memory/activation markers in 16 clusters of CD4+ T cells is shown in heatmap which is arranged based on the expression level of CD103. (E) Combinatoric frequency analysis of CD4+ marker combinations (TerraFlow-Methods) most frequent in HCC. Boxplots showing representative phenotypes of CD4+ T cells that are significantly higher in HCC compared to PDA. Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. *p < 0.05, **p < 0.01, Kruskal-Wallis test. (F) Boxplots showing percentage of PDA-enriched clusters C3, C12 and C13 in CD4+ T cells from tSNE (A). Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. *p < 0.05,**p < 0.01, Kruskal-Wallis test. (G) Histograms showing the individual marker expression CD4+ T cell clusters enriched in PDA of C3 (red), C12 (green) and C13 (blue). (H) Combinatoric frequency analysis of CD4+ marker combinations (TerraFlow-Methods) most frequent in PDA. Boxplots showing representative phenotypes of CD4+ T cell that are significantly higher in PDA compared to HCC. Representative statistically and biologically significant phenotypes are shown. Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. **p < 0.01, Kruskal-Wallis test.

In PDA, unique CD8⁺ or CD4⁺ T cells phenotypes were enriched that frequently expressed co-inhibitory immune checkpoint PD-1 but were lacking all co-stimulatory immune checkpoint receptors including ICOS, OX40, 4-1BB and GITR (Figures 2F–H, 3F–H and Supplementary Figures 3E, F). PDA patients were enriched for PD-1⁺CD103⁻ CD8⁺ T population (CP3) and PD-1⁺CD8⁺ TRM (CP5, CP-C3). PDA patients had higher naïve CD8⁺ T cells (CP-C2: CD45RO⁻) and effector memory T cells (TEM) (CP7: CD45RO⁺CD127⁺CCR7⁻) that were lacking ICOS and PD-1 expression. Interestingly, one subset of TRM (CP-C3) also exhibited high CD57 expression in addition to PD-1, indicating exhaustion/senescence. Both HCC and CCA had significantly lower frequency of ICOS⁻ TRM (CP5) and TEM (CP7) compared to PDA. Consistently PDA was enriched for CD4⁺ T cells lacking co-stimulatory checkpoint receptors, including naïve CD4⁺ T cells (C3: CD127⁺CD45RO⁻PD-1⁻), and memory CD4⁺ T cells that expressed PD-1 and/or TIGIT (C12: CD69⁺CD45RO⁺PD-1⁺ TIGIT⁺ and C13: CD127hiCD45RO⁺ PD-1⁻TIGIT⁺) (Figures 3F, G). All of these PDA-enriched T cell populations shared a striking characteristic which is the absence of all four co-stimulatory immune checkpoint receptors, ICOS, GITR, OX40 and 4-1BB (Figures 2H, 3D–H, Supplemental Figure 3F), suggesting T cell anergy. These findings suggest the tumor microenvironment in PDA could suppress activation of anti-tumor T cells and/or induce T cell anergy by repressing co-stimulatory immune checkpoint signals (22). The hyporesponsive state of these anergic T cells may contribute to tumor immune evasion and resistance to immune checkpoint blockade such as anti-PD-1/PD-L1 and anti-CTLA-4. Altogether, with tSNE and combinatorial expression analysis, we identified dysfunctional T cell populations uniquely enriched in HCC, CCA and PDA patients. HCC patient tumors showed higher TPEX cells expressing co-stimulatory immune checkpoint ICOS and multiple co-inhibitory immune checkpoint receptors, most frequently PD-1 and TIGIT; whereas, PDA contained more anergic PD-1⁺ T cells lacking co-stimulatory immune checkpoint receptors, indicating PDA as a less immune responsive tumor compared with HCC and CCA.

T cell phenotypes associated with HPB cancer clinical prognosis

While the tSNE analysis allowed identification of unique T cell populations in HPB cancers, we sought to further account for the heterogeneity among patients and quantify populations most likely to have clinical significance. Therefore, we evaluated whether individual T cell subpopulations were predictive of clinical prognosis in both HCC and PDA patients based on unbiased hierarchical analysis. Early stage HCC patients (Stage I-II) demonstrated a greater degree of partially exhausted CD8⁺ TRM co-expressing ICOS, PD-1, TIGIT and Tim-3 (Figure 4 and Supplementary Figure 4). Specifically, early stage HCC tumors showed a significant increase in CD103⁺CD8⁺ TRM expressing high PD-1 and ICOS (C1) and CD127⁺ CD8⁺ and CD4⁺ TEM (C4 in CD8 and C1 in CD4) when compared to late stage patients (stage IIIA-B) (Figure 4). Late stage HCC tumors showed more CD8⁺ (C6) and CD4⁺ (C7) memory T cells co-expressing TIGIT, a marker for exhaustion (Figure 4). Thus, combined blockade of PD-1, TIGIT and/or Tim-3 could be potentially beneficial in HCC patients.

Figure 4 | HCC disease stage and effect on T cell phenotypes. (A) tSNE projection of CD8+ T cells showing 6 manual-gated clusters identified from early stage (n=6) and late stage (n=4) HCC patients. Significantly enriched clusters in early stage (C1, C4) and late stage (C6) HCC patients are labeled. (B) Boxplots showing percentage of early stage-enriched CD8+ T cell clusters C1 and C4 and late stage-enriched cluster C6 in HCC patients. *p < 0.05, Kruskal-Wallis test. (C) Histograms showing the individual marker expression of early stage-enriched CD8+ T cell clusters C1 (red), C4 (green), and late stage-enriched cluster C6 (blue) in HCC patients. (D) Expression of TRM-related markers, co-inhibitory/senescent markers, co-stimulatory markers and memory/activation markers in 6 clusters of CD8+ T cells is shown in heatmap which is arranged based on the expression level of CD103. (E) tSNE projection of CD4+ T cells showing 7 manual-gated clusters identified from early stage (n=7) and late stage (n=5) HCC patients. Significantly enriched clusters in early stage (C1) and late stage (C7) HCC patients are labeled. (F) Boxplots showing percentage of early stage HCC enriched CD4+ T cell cluster C1 and late stage HCC enriched cluster C7. *p < 0.05, Kruskal-Wallis test. (G) Histograms showing individual marker expression of early stage HCC enriched CD4+ T cell cluster C1 (red) and late stage HCC enriched cluster C7 (blue). (H) Expression of TRM-related markers, co-inhibitory/senescent markers, co-stimulatory markers and memory/activation markers in 7 clusters of CD4+ T cells is shown in heatmap which is arranged based on the expression level of CD103.

For primary pancreatic tumors, late stage (III-IV) PDA demonstrated higher ICOS⁻PD-1⁻CD127hiCD8⁺ (C12) and CD4⁺ T cells (C10) compared to early stage PDA tumors (Stage I-II) (Figure 5 and Supplementary Figure 5). The absence of co-stimulatory immune checkpoint receptors and PD-1 in more advanced PDA disease suggests hypo-responsive T cell phenotypes that may not be properly activated even when co-inhibitory checkpoints were blocked. Early-stage PDA patients showed increase of CD8⁺ CD103⁺ and/or CD69⁺ TRM that also expressed PD-1 and/or HLA-DR (C6, C8 and C10), and higher CD4⁺ memory T cells with low level of PD-1 (C6) (Figure 5), suggesting that early-stage PDA contained more activated TRM expressing PD-1. However, there was a noted absence of co-stimulatory checkpoint receptors, possibly limiting their full activation (Figures 5C, D, G, H). Additional T cell populations were significantly different among early and late stage HPB cancers but at quantitatively much lower frequencies (Supplementary Figures 4, 5).

Figure 5 | PDA disease stage and effect on T cell phenotypes (A) tSNE projection of CD8+ T cells showing 16 manual-gated clusters identified from early stage (n=11) and late stage (n=4) PDA patients. Significantly enriched clusters in early stage (C6, C8, C10) and late stage (C12) PDA patients are labeled. (B) Boxplots showing percentage of early stage-enriched CD8+ T cell clusters and late stage-enriched clusters. Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. *p < 0.05, Kruskal-Wallis test. (C) Histograms showing individual marker expression of early stage-enriched CD8+ T cell clusters in C6 (orange), C8 (red), C10 (purple) and late stage-enriched cluster C12 (blue). (D) Expression of TRM-related markers, co-inhibitory/senescent markers, co-stimulatory markers and memory/activation markers in 16 clusters of CD8+ T cells is shown in heatmap which is arranged based on the expression level of CD103. (E) tSNE projection of CD4+ T cells showing 12 manual-gated clusters identified from early stage (n=13) and late stage (n=5) PDA patients. Significantly enriched clusters in early stage (C6) and late stage (C10) PDA patients are labeled. (F) Boxplots showing percentage of early stage-enriched CD4+ T cell cluster C6 and late stage-enriched cluster C10. Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. *p < 0.05, Kruskal-Wallis test. (G) Histograms showing the individual marker expression of early stage-enriched CD4+ T cell clusters C6 (red) and late stage-enriched clusters C10 (blue). (H) Expression of TRM-related markers, co-inhibitory/senescent markers, co-stimulatory markers and memory/activation markers in 12 clusters of CD4+ T cells is shown in heatmap which is arranged based on the expression level of CD103.

Neoadjuvant chemotherapy induced alterations in the T cell compartment and PDA TME niche

Neoadjuvant chemotherapy results in a cytotoxic tumor response and has been hypothesized to increase neo-antigen presentation and potentially alter the TME. To characterize the effect of neoadjuvant chemotherapy in PDA patients, we compared patients who had received neoadjuvant chemotherapy (n=7) to those who did not receive chemotherapy (n=9) (Figure 6 and Supplementary Figures 6, 7). Following treatment, there was no significant change in overall CD4 and CD8 T cell percentage (data not shown), however, there was a significant increase in CD103hiCD69⁺ CD8⁺ TRM (C5, C9 and C16) and CD4⁺ TRM (C15) that expressed PD-1 but not TIGIT or Tim-3, suggesting activation of TRM following neoadjuvant chemotherapy (Figures 6A–H). However, these TRM expressed PD-1 but lacked co-stimulatory checkpoints (Figures 6C, D, G, H), which may render them less effective against tumor cells. On the contrary, we also identified a relatively small frequency population of CD8⁺ TRM (C1) enriched in treatment-naïve PDA patients (Figure 6B). Those TRM were triple positive for PD-1, TIGIT and Tim-3, indicating a highly immune inhibitory phenotype (Figures 6C, D). Neoadjuvant-treated patients showed a significant reduction of Tim-3⁺TIGIT⁺ CD4⁺ T cells (Figure 6I) while there was an enrichment of Tim-3⁻TIGIT⁻CD4⁺ TRM after neoadjuvant treatment (Figure 6J), suggesting early T cell activation after neoadjuvant treatment without expansion of a highly exhausted phenotype. Indeed, TIGIT and Tim-3 in CD4⁺ T cells are the most critical set of markers that distinguish naïve versus neoadjuvant-treated groups (Supplementary Figure 6E). While the overall populations of PD-1⁺ or TIGIT⁺ T cells remained stable following chemotherapy, the Tim-3⁺ CD8⁺ and CD4⁺ T cells were significantly reduced following treatment, indicating a decrease of an exhausted T cell phenotype (Supplementary Figure 7E).

Figure 6 | Effect of neoadjuvant chemotherapy on T cell phenotypes in PDA patients (A) tSNE projection of CD8+ T cells showing 30 FlowSOM clusters identified from PDA patients treated with (n=6) or without (n=8) neoadjuvant chemotherapy. Significantly enriched clusters in neoadjuvant treatment group (C5, C9, C16) and no treatment group (C1) are labeled. (B) Boxplots showing phenotypes of CD8+ T cell clusters enriched following neoadjuvant chemotherapy. Box middle lines, median; box limits upper and lower quartiles; box whiskers, 1.5x the interquartile range. *p<0.05, **p < 0.01, Kruskal-Wallis test. (C) Histograms show the single marker phenotypes of CD8+ T cell clusters C5 (red), C9 (purple) and C16 (orange) enriched in PDA patients treated with neoadjuvant chemotherapy and cluster C1 (blue) enriched in PDA patients without neoadjuvant treatment. (D) Expression of TRM-related markers, co-inhibitory/senescent markers, co-stimulatory markers and memory/activation markers in 30 clusters of CD8+ T cells is shown in heatmap which is arranged based on the expression level of CD103. (E) tSNE projection of CD4+ T cells showing 30 FlowSOM clusters identified from PDA patients treated with (n=7) or without (n=9) neoadjuvant chemotherapy. Significantly enriched cluster C15 in neoajuvant treatment group is labeled. (F) Boxplot shows cluster C15 (p<0.05) increased in CD4+ T cells from PDA patients treated with neoadjuvant. (G) Histograms show the phenotype of C15 (red) in CD4+ T cells from PDA patients treated with neoadjuvant chemotherapy. (H) Expression of TRM-related markers, co-inhibitory/senescent markers, co-stimulatory markers and memory/activation markers in 30 clusters of CD4+ T cells is shown in heatmap which is arranged based on the expression level of CD103. (I, J). Combinatoric frequency analysis of CD4+ marker combinations in response to neoadjuvant chemotherapy (TerraFlow-Methods). Boxplots showing representative phenotypes of CD4+ T cells that are significantly higher in treatment-naïve PDA patients (I) or patients receiving neoadjuvant chemotherapy (J).

T cell Phenotypes in PDA primary tumors and liver metastases

We next examined whether there was any alteration in the T cell immune contexture among PDA liver metastatic tumors versus primary PDA tumors. PDA liver metastases demonstrated higher CD8⁺ TEM (Supplementary Figure 8A–D) and CD4⁺ TEM (Supplementary Figures 8E–H), exhibiting an activated phenotype with CD127 and/or HLA-DR expression. Conversely, PDA primary tumors had greater PD-1⁺ICOS⁻ CD8⁺ TRM (CP5, Supplementary Figures 8B–D) and PD-1hiTIGIT⁺CD57⁺ senescent CD4⁺ memory T cells (C4, Supplementary Figures 8F–H), suggesting a more chronic exhausted and/or senescent state in the primary tumor compared to liver metastasis. (Supplementary Figure 8C, D, G–J). Interestingly, we also found a small population of TRM enriched in primary PDA expressing both co-stimulatory, ICOS and 41-BB, and co-inhibitory, PD-1, TIGIT, Tim-3 and BTLA, immune checkpoint receptors, as well as CD95 (Fas) that leads to apoptotic cell death (C9, Supplementary Figures 8I, J). This TRM population may represent activated T cells with an exhausted phenotype and may be prone to activation-induced apoptosis.

Discussion

In this study, we utilized high-parameter flow cytometry for the first time to characterize the phenotypic and functional state of tumor infiltrating T cell populations across HPB malignancies. We conducted a broad and comparative assessment of immune checkpoint receptors, trafficking receptors, activation and phenotype markers at the protein level and identified key differences in T cell states among HCC, PDA and CCA that mapped to clinical features of these diseases. This novel technology has multiple potential advantages over mass cytometry (CyTOF), such as increased sensitivity and detection of T cells in scant tumor samples including core needle biopsies. Furthermore, in comparion to other large HCC cohorts utilizing RNA sequencing and/or immunohistochemistry, our study is largely consistent with the previous studies, with our study having the added benefit of simultaneously measuring multiple cell surface markers concurrently to better define immune cell populations (23, 24). This was a particular advantage in our current study in that we were able to assess patients with advanced stages of disease where tissue is only obtainable with core needle biopsy. Thus we were able to directly 1) compare T cell subsets in HPB malignancies, 2) identify whether clinical prognosis correlates with T cell populations, and 3) for PDA patients, determine the effects of chemotherapy and site of disease on tumor T cell populations. Additionally, we were specifically able to characterize across these disease states the presence of T cell populations that may be targetable using immunological modifying drugs in clinical development.

Although protein analysis using our unique high parameter flow cytometry approach offers a powerful tool for characterizing TIL, there are limitations to this study. First, we did not perform direct measurements of cell function; thus, we cannot confirm that cells expressing immune checkpoints commonly classified as coinhibitory are truly anergic or inhibited in vivo. Second, we did not report expression for the ligands of the immune checkpoint molecules we analyzed; engagement of these ligands is typically required for eliciting inhibitory or activating pathways in T-cells. Third, it is unknown how many co-inhibitory or co-stimulatory molecules are required to engage an activating or inhibiting pathway in a T-cell. It may be that expression levels below the limit of detection of our flow cytometry assays are sufficient to change a cellular program.

Most tumor-infiltrating T cells we examined were of an antigen-experienced CD45RO⁺ memory phenotype. Memory T cells comprise diverse subsets including central memory (TCM), effector memory (TEM) and tissue-resident (TRM) memory T cells. Their generation and functional state are dynamically shaped by tissue site and tumor microenvironment (6). Single marker evaluation highlighted the similar presence of CD45RO⁺ memory T cells and CD103⁺ TRM, while TCM trended higher in HCC patients. CXCR6⁺ T cells and CD69⁺ CD4⁺ T cells were also significantly higher in HCC patients, suggesting better T cell trafficking and retention in HCC tumors compared to PDA. In addition, CD57⁺ terminally differentiated effector T cells were present in the three cancer types, with a slight increase of CD57⁺CD4⁺ T cells in HCC compared to PDA. Furthermore, we profiled both co-stimulatory and co-inhibitory immune checkpoint receptors. Co-inhibitory checkpoint receptors were generally more prevalent than co-stimulatory checkpoint receptors, with PD-1 and TIGIT being the most frequently expressed followed by TIM3 and BTLA in HPB cancer patients. CTLA4 was among the lowest frequencies of checkpoint receptors in HPB cancer patients. PD-1 was noted on over 46% and 35% of CD8⁺ and CD4⁺ T cells in HCC patients, respectively, but was much more heterogenous in PDA (5%-85% for CD8⁺ and 3%-75% for CD4⁺) and CCA (24%-80% for CD8⁺ and 10%-76% for CD4⁺) patients. Notably, ICOS was expressed at higher levels in both liver cancers than in PDA, suggesting tumor-tissue specific features and a dysfunctional T cell state in PDA. While single marker analysis was notable, we sought to further define the unique T cell populations and states across these HPB diseases and match our results to clinical parameters.

Therefore we leveraged the power of multiparameter flow cytometry to explore the diverse phenotypic and functional states of T cell subsets. Utilizing the bioinformatics techniques of dimension reduction and clustering via tSNE and the combinatoric TerraFlow platform, unbiased analysis of multiple simultaneous T cell surface markers was possible, as has been recently described for peripheral T cells in advanced melanoma patients (10). In PDA we noted extremely small populations of partially exhausted T cells, most of them consistent with terminally exhausted T cells expressing co-inhibitory but not co-stimulatory immune checkpoint receptors. These findings may explain the relative lack of response rates to anti-PD-1 therapy in PDA (25, 26). In HCC patients, generally known for better anti-PD-1 treatment response rates of 15-20% (4, 27), we found a higher frequency of partially exhausted TRM cells, expressing not only elevated levels of PD-1, but also TIGIT, Tim-3, and the co-stimulatory checkpoint ICOS. TRM is positively associated with response to ICI in several cancer types. In cancer patients responding to anti-PD-1 or anti-PD-1/CTLA4, clonally expanded T cells showed elevated TRM and cytotoxicity programs, suggesting rapid response capability of TRM in response to ICI (28). This suggests that targeting of PD-1 or its ligand in HCC can occasionally be successful, and resistance to anti-PD-1/PD-1 ligand monotherapy in HCC may be overcome by combination of agents directed simultaneously against these co-inhibitory and co-stimulatory checkpoint signals. Interestingly, this partially exhausted T cell phenotype (TPEX) was more commonly noted in early stage HCC. This provides rationale to consider multimodality therapies for earlier stage HCC and that over time, a more terminally exhausted T cell state develops in more advanced disease. In contrast to HCC, advanced stage PDA patients had a higher frequency of naïve T cells, suggesting that increased antigen turnover and presentation may be required for success of immunotherapy in PDA. Early stage PDA patients contained higher activated TRM cells with TIGIT or PD-1 than late stage PDA patients, but were lacking co-stimulatory molecules and altogether were much lower in frequency than in HCC patients. This indicates early stage PDA harbored terminally exhausted T cells, partially explaining the failed response of PD-1 immunotherapy in PDA. Further, we were able to quantify the presence and level of expression of checkpoint inhibitors and activators, including their functional status, function, i.e. exhaustion status, in T cell subsets in a comprehensive, unbiased manner. There is limited data about checkpoint molecule co-expression of T cells in HCC, PDA, and CCA and their clinical relevance.

It has been shown that in the absence of co-stimulation, the tumor-antigen specific TCR signal alone induces T cell anergy rather than activation (29). It has been postulated that cytotoxic chemotherapy might enhance T cell activation by increasing antigen turnover within the TME (30). Comparing samples of PDA patients treated with neoadjuvant chemotherapy with treatment naïve samples, we observed a statistically significant increase in activated CD8⁺ and CD4⁺ TRM cells with treatment, characterized by high CD103, CD127, CD69, and PD-1 expression. The clinical significance of this change is unclear given the relative paucity of other checkpoint receptors and the small size of these T cell populations (less than 2-3% of T cells in all patients). Similarly, there was a reduction from the baseline percentage of CD4⁺Tim-3⁺TIGIT⁺ of exhausted T cells but an induction of CD4⁺Tim-3⁻TIGIT⁻CD127⁺ TRM following neoadjuvant treatment. A recent study showed a relative increase in PD-1, TIGIT, and 4-1BB expression on peripheral circulating T cells (outside of the TME) in PDA patients receiving cytotoxic chemotherapy (31), but did not assess for other T cell checkpoint receptors or activation markers or intra-tumoral T cells. Other than these changes in isolated, small populations of T cells, there was no evidence of significant induction of either 1) increased T cell trafficking and differentiation, 2) increased T cell activation, or 3) robust checkpoint molecule expression changes in PDA patients following chemotherapy. When comparing primary PDA samples to PDA liver metastases, we noticed higher T cell activation but lack of a core TRM phenotype in metastatic samples, suggesting organ specific differences in TME complexity and consequently potential differential susceptibility to immunotherapies.

One strength of our study is the ability to study checkpoint molecule co-expression and other T cell marker surface protein expression and assess its clinical/translational relevance by quantification of checkpoint molecule expression and other markers in relation to each other, the disease stage or the exhaustion profile. Similar to the previous studies investigating the TME in human HCC using a variety of complex immunophenotyping techniques (32–34), our study not only assessed similar broad parameters, but also served to extend the detail of the phenotypic analysis of these T cell subsets, particularly across disease states, stage, and in the case of PDA, in the context of chemotherapy. The information gained by these analyses aids in understanding which T cell populations are present in patient tumors to help guide future decision making regarding checkpoint targeting therapy. A weakness of our study is that while our platform allowed for simultaneous assessment of 25 markers on small portions of tissue, additional platforms (e.g. intracellular markers and myeloid subsets) could not be simultaneously assessed due to finite amounts of tissue. It is also possible that varying chemotherapy regimens may have differing effects on the T cell subsets in the TME of PDA patients and similarly differing metastatic sites may possess a varying TME in PDA patients.

In summary, we have identified unique T cell states in the TME of hepatobiliary and pancreatic malignancies. We further identified altered T cells states related to disease stage in HCC and PDA. Terminally exhausted T cells were more prevalent in a graded fashion from HCC, to CCA, and ultimately PDA, whereas functionally exhausted T cells, capable of restored activation, were most prevalent in HCC, especially in early stage HCC. With data from preclinical studies of HCC showing that combined therapy targeting multiple inhibitory checkpoint receptors exhibited synergistic treatment efficacy by restoring TILs-mediated anti-tumor immunity (35, 36) and the detailed immunophenotyping presented in this study, HCC patients may benefit most from combined checkpoint therapies whereas efforts to increase overall T cell activation in CCA and PDA warrant further investigation.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving human participants were reviewed and approved by Perlmutter Cancer Center, NYU Langone Health. The patients/participants provided their written informed consent to participate in this study.

Author contributions

SW, EZ, PC, DiS and TW developed the study concept and were responsible for the study design. SW, EZ, DW, DiS. and TW wrote the manuscript, which was then revised and approved by all co-authors. SW, EZ, GW and DF helped acquire human biospecimens and processed samples for flow analysis. SW, EZ and PC analyzed the flow data. SW and DF were responsible for the R and combinatorial analysis. SW, EZ, DW, BK, GW, LK, DeS, and PO collected patient information and maintained the databases. BK, LK, DeS, PO and PC provided study guidance and feedback on the manuscript. PC, DiS and TW directed the study. DiS and TW provided grant support. All authors contributed to the article and approved the submitted version.

Funding

This study was funded by NIH R01CA245005 (DMS), P30CA016087 and Perlmutter Cancer Center Developmental Projects Program (CCSG) (THW) and NYULH research program startup funds (PKC).

Conflict of interest

DF and PKC own TerraFlow, which was used for some of the analyses presented in the paper.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1067352/full#supplementary-material

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Copyright © 2023 Wan, Zhao, Freeman, Weissinger, Krantz, Werba, Khanna, Siolas, Oberstein, Chattopadhyay, Simeone and Welling. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

CORRECTION
published: 23 March 2023
doi: 10.3389/fimmu.2023.1177713

Corrigendum: Tumor infiltrating T cell states and checkpoint inhibitor expression in hepatic and pancreatic malignancies

Shanshan Wan ¹†, Ende Zhao ¹,²†, Daniel Freeman ³, Daniel Weissinger ², Benjamin A. Krantz ²,⁴, Gregor Werba ², Lauren G. Khanna ⁴, Despina Siolas ²,⁴, Paul E. Oberstein ²,⁴, Pratip K. Chattopadhyay ²,³,⁵*, Diane M. Simeone ¹,²,³* and Theodore H. Welling ¹,²*

¹ Department of Surgery, NYU Langone Health, New York, NY, United States, ² Perlmutter Cancer Center, NYU Langone Health, New York, NY, United States, ³ Pathology, NYU Langone Health, New York, NY, United States, ⁴ Internal Medicine, NYU Langone Health, New York, NY, United States, ⁵ Talon Biomarkers, Mendham, NJ, United States

Edited and Reviewed by:
Hongda Liu, Nanjing Medical University, China

*Correspondence:
Theodore H. Welling
Theodore.Welling@nyulangone.org
Diane M. Simeone
Diane.Simeone@nyulangone.org
Pratip K. Chattopadhyay
pratip@talonbiomarkers.com

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 01 March 2023

Accepted: 14 March 2023

Published: 23 March 2023

Citation:
Wan S, Zhao E, Freeman D, Weissinger D, Krantz BA, Werba G, Khanna LG, Siolas D, Oberstein PE, Chattopadhyay PK, Simeone DM and Welling TH (2023) Corrigendum: Tumor infiltrating T cell states and checkpoint inhibitor expression in hepatic and pancreatic malignancies. Front. Immunol. 14:1177713. doi: 10.3389/fimmu.2023.1177713

Keywords: pancreatic adenocarcinoma, hepatocellular carcinoma, cholangiocarcinoma, immune oncology, tumor immunity, T cell biology, checkpoint inhibitors

A corrigendum on

Tumor infiltrating T cell states and checkpoint inhibitor expression in hepatic and pancreatic malignancies
by Wan S, Zhao E, Freeman D, Weissinger D, Krantz BA, Werba G, Khanna LG, Siolas D, Oberstein PE, Chattopadhyay PK, Simeone DM and Welling TH (2023). Front. Immunol. 14:1067352.doi: 10.3389/fimmu.2023.1067352

In the published article, there was an error regarding the affiliation for Pratip K. Chattopadhyay. As well as having affiliations 2,4, they should also have Talon Biomarkers, Mendham, NJ, United States.

In the published article, there was an error in the author list, and author Daniel Freeman was erroneously listed as 6th author. Author Pratip K. Chattopadhyay was erroneously not listed as a co-corresponding author. Author Diane M. Simeone was erroneously not listed as a co-corresponding author. The corrected author list appears below.

Shanshan Wan1†, Ende Zhao1,2†, Daniel Freeman⁴, Daniel Weissinger², Benjamin A. Krantz2,3, Gregor Werba², Lauren G. Khanna³, Despina Siolas2,3, Paul E. Oberstein2,3, Pratip K. Chattopadhyay2,4,5*, Diane M. Simeone1,2,4*, Theodore H. Welling1,2*

In the published article Freeman D, Lam L, Le T, Alexandre J, Raphael B, Grossbard M, et al. TerraFlow, A New High Parameter Data Analysis Tool, Reveals Systemic T-cell Exhaustion and Dysfunctional Cytokine Production in Classical Hodgkin Lymphoma. medRxiv. 2022 was not cited in the article. The citation has now been inserted in Methods, Combinatorial Analysis and should read:

“CD4⁺ and CD8⁺ T cell data were exported respectively for each sample for rapid computation of combinatorial phenotypes using the CytoBrute platform (RocketML) (10). Phenotypes consisting of every single marker, and all possible combinations of 2-15 markers were constructed, and enumerated across all samples. The top 1,000 most frequent combinatorial phenotypes were reported and compared across study groups. For some analyses, flow cytometry data files, gating thresholds and comparison groups were uploaded to TerraFlow (TerraFlow.app) for construction of all possible 1-5 marker phenotypes across the dataset. Phenotypes whose frequency differed statistically significantly between phenotypes were reported. TerraFlow also generated core phenotypes that summarize the most significant families of phenotypes. Finally, a simple set of markers that distinguished groups was identified using recursive feature elimination (13).”

In the published article, there was an error in the Funding statement. Funding from one author (PKC) was missing. The correct Funding statement appears below.

FUNDING

“This study was funded by NIH R01CA245005 (DMS), P30CA016087 and Perlmutter Cancer Center Developmental Projects Program (CCSG) (THW) and NYULH research program startup funds (PKC).”

In the published article, there was an error in the Conflict of Interest. The Conflict of Interest was not correctly disclosed. This sentence previously stated: “The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.” The correct Conflict of Interest appears below.

Conflict of Interest

“DF and PC own TerraFlow, which was used for some of the analyses presented in the paper.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.”

In the published article, there was an error. missing or misleading texts.

A correction has been made to Abstract. This sentence previously stated:

“We therefore defined T cell subsets in the tumor microenvironment of HPB patients utilizing a novel, multiparameter flow cytometry and bioinformatics analysis. Our findings quantify the T cell phenotypic states in relation to checkpoint receptor expression. We demonstrate the presence of CD103⁺ tissue resident memory T cells (TRM), CCR7⁺ central memory T cells, and CD57⁺ terminally differentiated effector cells across all HPB cancers, while the anti-tumor function was dampened by expression of multiple co-inhibitory checkpoint receptors. Terminally exhausted T cells lacking co-stimulatory receptors were more prevalent in PDA, whereas partially exhausted T cells expressing both co-inhibitory and co-stimulatory receptors were most prevalent in HCC, especially in early stage. HCC patients had significantly higher TRM with a phenotype that could confer restored activation in response to immune checkpoint therapies. Further, we found a lack of robust alteration in T cell activation state or checkpoint expression in response to chemotherapy in PDA patients.”

The corrected sentence appears below:

“We therefore defined T cell phenotypic states, particularly in terms of immune checkpoint receptor expression, in the tumor microenvironment of HPB patients utilizing novel, multiparameter flow cytometry and bioinformatics analysis. We demonstrate the presence of CD103⁺ tissue resident memory T cells (TRM), CCR7⁺ central memory T cells, and CD57⁺ terminally differentiated effector cells across all HPB cancers, with simultaneous expression of multiple co-inhibitory checkpoint receptors. Terminally differentiated T cells lacking co-stimulatory receptors were more prevalent in PDA, whereas T cells expressing both co-inhibitory and co-stimulatory receptors were most prevalent in HCC, especially in early stage. HCC patients had significantly higher TRM with a phenotype that might confer restored activation in response to immune checkpoint therapies. Further, T-cell activation state and checkpoint expression did not change robustly in response to chemotherapy in PDA patients.”

In the published article, there was an error. missing or misleading texts.

A correction has been made to Methods, High Parameter Flow Cytometry. This sentence previously stated:

“A 25 parameter antibody panel was developed using ColorWheel software (10) to include T cell markers of differentiation, activation, trafficking, phenotype, co-inhibitory and co-stimulatory checkpoint molecules CD186 (B515), CD137 (B610), CD244 (B710), CD57 (B780), CD45RO (R670), HLA-DR (R710), GITR (R780), CD278 (V420), CD95 (V475), CD103 (V610), CD183 (V670), CD134 (V710), CD69 (V740), CD4 (V780), CCR7 (U380), Viability dye (U450), CD3 (U515), CD25 (U585), CD366 (U670), PD-1 (U740), CD8 (U820), TIGIT (G575), CD272 (G610), CD127 (G670) and CD152 (G780) (BD Biosciences)(10). Data were collected on a custom BD FACSymphony A5 30-parameter Cell Analyzer and compensated and analyzed with FlowJo software (BD Biosciences). FACS data were checked for quality of staining and normalized. Manual compensation was done for overlap in fluorescent spectra and fluorescence aggregates, dead cells and cell doublets were excluded. Fluorescence intensity thresholds and gates were then determined for each marker to distinguish positive from negative expression. Data from CD4⁺ and CD8⁺ T cells were analyzed separately.”

The corrected sentence appears below:

“A 25 color antibody panel was developed using ColorWheel software (10) to include T cell markers of differentiation, activation and trafficking along with co-inhibitory and co-stimulatory checkpoint molecules. The following mouse anti-human antibodies were used, all purchased from BD Biosciences, except where indicated: CD186 (Brilliant Blue (BB)515), CD137 (BB630), CD244 (BB700), CD57 (BB780), CD45RO (allophycocyanin (APC)), HLA-DR (R700APC), GITR (Cyanin 7 (Cy7)-APC), CD278 (Brilliant Violet (BV)421)), CD95 (BV510), CD103 (BV605), CD183 (BV650), CD134 (BV705), CD69 (BV750), CD4 (BV785), CCR7 (Brilliant Ultraviolet (BUV) 395), LIVE/DEAD Fixable Blue (ThermoFisher Scientific, Carlsbad, CA), CD3 (BUV496), CD25 (BUV563), CD366 (BUV661), PD-1 (BUV737), CD8 (BUV805), TIGIT (phycoerythrin (PE)), CD272 (PE-CF594), CD127 (PE-Cy5) and CD152 (PE-Cy7) (10). Data were collected on a custom BD Biosciences FACSymphony A5 30-parameter flow cytometer, then compensated and analyzed with FlowJo software (BD Biosciences). Data were checked for quality of staining and fluorescence aggregates, dead cells and cell doublets were excluded. Fluorescence intensity thresholds were then determined for each marker to distinguish positive from negative expression.”

In the published article, there was an error. missing texts.

A correction has been made to Results, T cell Distribution in HPB Cancers, Paragraph 1. This sentence previously stated:

“TCM in HCC trended higher when compared to TCM in PDA and CCA, but did not reach statistical significance, based on CCR7 as a single marker (Figures 1B, C).”

The corrected sentence appears below:

“TCM in HCC trended higher when compared to TCM in PDA and CCA, but did not reach statistical significance, based on CCR7 as a single marker (Figures 1B, C). CD127 (the IL7-receptor) also commonly marks TCM, but exhibited a different pattern than CCR7, as it trended to higher levels (amongst CD8⁺ T-cells) or is significantly elevated (amongst CD4⁺ T-cells) in PDA and CCA compared to HCC.”

In the published article, there was an error. missing or misleading texts.

A correction has been made to Results, T cell phenotypes unique to HPB cancer type, Paragraph 1. This sentence previously stated:

“High parameter flow cytometry for T cell subtype analysis allows for the possibility of 10¹⁰ theoretical phenotypes of T cells based on the possible combinations of cell surface marker expression.”

The corrected sentence appears below:

“Using specialized data analysis tools, like CytoBrute and TerraFlow, high parameter flow cytometry data can be mined to examine all the theoretically possible phenotypes. In this study, as many as 3²² theoretical phenotypes may be present in the data (where three conditions – positive, negative, and omitted are analyzed for each of 22 markers). Because some markers were rarely expressed, and because of the challenge of interpreting long higher-order phenotypes of marker combinations, we limited CytoBrute analysis to 15 markers and TerraFlow analysis was designed to sample all possible sets of six markers. Cell phenotypes that were exceedingly rare were removed from downstream analysis and comparison across patient groups.”

In the published article, there was an error. missing or misleading texts.

A correction has been made to Results, T cell phenotypes unique to HPB cancer type, Paragraph 1. This sentence previously stated:

“This observation was independently validated by combinatoric analysis using the TerraFlow platform (Figure 2E).”

The corrected sentence appears below:

“We used TerraFlow to provide further insight into the results observed in our other analyses. TerraFlow identified a number of phenotypes, not revealed by tSNE analysis, that differed between HCC and PDA (Figure 2E), variously highlighting the elevation of ICOS, TIGIT, CD69, and PD1 in distinguishing PDA from HCC (Supplemental Figures 2A–E) using machine learning and recursive feature analysis(13). TerraFlow revealed that HCC was highly enriched for ICOS+ TRM that frequently expressed PD-1 and/or TIGIT (Figure 2E), suggesting that immune checkpoint blockade of PD1 and/or TIGIT might release inhibition of the already-expressed costimulatory molecule ICOS. Supplemental Figures 2C and F illustrate the p-values for all phenotypes compared between HCC and PDA.”

In the published article, there was an error. missing or misleading texts.

A correction has been made to Results, T cell phenotypes unique to HPB cancer type, Paragraph 2. This sentence previously stated:

“All of these PDA-enriched T cell populations shared a striking characteristic which is the absence of all four co-stimulatory immune checkpoint receptors, ICOS, GITR, OX40 and 4-1BB (Figures 3D–G, Supplemental Figure 3F), suggesting T cell anergy. These observations were independently validated by combinatoric TerraFlow platform (Figures 2H, 3H). These findings indicate the tumor microenvironment in PDA could suppress activation of anti-tumor T cells and/or induce T cell anergy via repressing co-stimulatory immune checkpoint signals while maintaining co-inhibitory immune checkpoint signals”

The corrected sentence appears below:

“All of these PDA-enriched T cell populations shared a striking characteristic which is the absence of all four co-stimulatory immune checkpoint receptors, ICOS, GITR, OX40 and 4-1BB (Figures 2H, 3D–H, Supplemental Figure 3F), suggesting T cell anergy. These findings suggest the tumor microenvironment in PDA could suppress activation of anti-tumor T cells and/or induce T cell anergy by repressing co-stimulatory immune checkpoint signals”

In the published article, there was an error. missing paragraph 2.

A correction has been made to Discussion, Paragraph 2. This sentence previously stated:

“Additionally, we were specifically able to characterize across these disease states the presence of T cell populations that may be targetable using immunological modifying drugs in clinical development.”

The corrected sentence appears below:

“Additionally, we were specifically able to characterize across these disease states the presence of T cell populations that may be targetable using immunological modifying drugs in clinical development.

Although protein analysis using our unique high parameter flow cytometry approach offers a powerful tool for characterizing TIL, there are limitations to this study. First, we did not perform direct measurements of cell function; thus, we cannot confirm that cells expressing immune checkpoints commonly classified as co-inhibitory are truly anergic or inhibited in vivo. Second, we did not report expression for the ligands of the immune checkpoint molecules we analyzed; engagement of these ligands is typically required for eliciting inhibitory or activating pathways in T-cells. Third, it is unknown how many co-inhibitory or co-stimulatory molecules are required to engage an activating or inhibiting pathway in a T-cell. It may be that expression levels below the limit of detection of our flow cytometry assays are sufficient to change a cellular program”.

The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2023 Wan, Zhao, Freeman, Weissinger, Krantz, Werba, Khanna, Siolas, Oberstein, Chattopadhyay, Simeone and Welling. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 13 February 2023
doi: 10.3389/fimmu.2023.1029427

Research into the characteristic molecules significantly affecting liver cancer immunotherapy

Junhong Chen, Hengwei Jin, Hao Zhou, Xufei Hei and Kai Liu *

Department of Hepatobiliary and Pancreatic Surgery II, General Surgery Center, The First Hospital of Jilin University, Changchun, China

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Shuai Wang, Langone Medical Center, New York University, United States
Gang Liu, Tianjin Medical University, China

*Correspondence:
Kai Liu
liuk@jlu.edu.cn

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 27 August 2022

Accepted: 01 February 2023

Published: 13 February 2023

Citation:
Chen J, Jin H, Zhou H, Hei X and Liu K (2023) Research into the characteristic molecules significantly affecting liver cancer immunotherapy. Front. Immunol. 14:1029427. doi: 10.3389/fimmu.2023.1029427

Background

The past decade has witnessed unprecedented scientific breakthroughs, including immunotherapy, which has great potential in clinical applications for liver cancer.

Methods

Public data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases and analyzed with R software.

Results

The LASSO and SVM-RFE machine learning algorithms identified 16 differentially expressed genes (DEGs) related to immunotherapy, namely, GNG8, MYH1, CHRNA3, DPEP1, PRSS35, CKMT1B, CNKSR1, C14orf180, POU3F1, SAG, POU2AF1, IGFBPL1, CDCA7, ZNF492, ZDHHC22, and SFRP2. Moreover, a logistic model (CombinedScore) was established based on these DEGs, showing an excellent prediction performance for liver cancer immunotherapy. Patients with a low CombinedScore might respond better to immunotherapy. Gene Set Enrichment Analysis showed that many metabolism pathways were activated in patients with a high CombinedScore, including butanoate metabolism, bile acid metabolism, fatty acid metabolism, glycine serine and threonine metabolism, and propanoate metabolism. Our comprehensive analysis showed that the CombinedScore was negatively correlated with the levels of most tumor-infiltrating immune cells and the activities of key steps of cancer immunity cycles. Continually, the CombinedScore was negatively associated with the expression of most immune checkpoints and immunotherapy response-related pathways. Moreover, patients with a high and a low CombinedScore exhibited diverse genomic features. Furthermore, we found that CDCA7 was significantly correlated with patient survival. Further analysis showed that CDCA7 was positively associated with M0 macrophages and negatively associated with M2 macrophages, suggesting that CDCA7 could influence the progression of liver cancer cells by affecting macrophage polarization. Next, single-cell analysis showed that CDCA7 was mainly expressed in prolif T cells. Immunohistochemical results confirmed that the staining intensity of CDCA7 was prominently increased in the nucleus in primary liver cancer tissues compared to adjacent non-tumor tissues.

Conclusions

Our results provide novel insights into the DEGs and factors affecting liver cancer immunotherapy. Meanwhile, CDCA7 was identified as a potential therapeutic target in this patient population.

Keywords: liver cancer, immunotherapy, immune checkpoint, M2 macrophages, CDCA7

Introduction

Liver cancer is a common digestive malignancy, responsible for nearly 8 million new cases annually (1). Many factors have been established to play a role in liver cancer occurrence, such as genomic differences, lifestyle, hepatitis virus, and fat, accounting for the gradual increase in incidence over the years. In some cases, patients with primary liver cancer can benefit from surgery in terms of long-term survival (1). Nevertheless, only 10% of liver cancer patients are indicated for surgical resection (2). For patients with advanced disease, chemotherapy, local ablation, and biological therapies are the main therapeutic modalities (3). Notwithstanding the fact that significant strides have been made in scientific research in recent years, mainstream treatment options exhibit limited efficacy. For instance, long-term use of sorafenib often leads to drug resistance and side effects that limit the drug’s benefits.

It is widely acknowledged that the human immune system normally recognizes and destroys foreign cells, including cancer cells. In the tumor microenvironment (TME), tumor fragment peptides are presented to major histocompatibility complex (MHC) molecules by antigen-presenting cells, thereby starting the killing process (4). However, multidrug resistance to chemotherapy has emerged as a significant challenge. A previous study showed that stem cells could be targeted by immunotherapy to combat resistance to common chemotherapies (5). Furthermore, sorafenib can prevent immunosuppression, increasing the effect of immunotherapy, like PD-1/L1 inhibitor therapy (6). Consequently, the combination of immunotherapy and other common therapies seems promising. Zhao et al. observed a synergistic effect through the combination of immunotherapy and common drugs for liver cancer (7). Moreover, current evidence suggests that PD-1/L1 inhibitors are correlated with a decrease in hepatitis B and C infections (8). Given that virus infection can induce liver cancer recurrence, therapy targeting PD-1/L1 can reduce relapses (8). Nivolumab, a PD-1 inhibitor, has now been approved by the FDA for treating multiple solid cancers, including liver cancer, and has yielded promising results (9). Meanwhile, the combination of ipilimumab (a CTLA-4 inhibitor) and nivolumab has been used in a clinical trial of liver cancer (10). Overall, immunotherapy has great potential for liver cancer treatment. Thus, exploring the biological molecules and mechanisms affecting liver cancer immunotherapy is essential.

Bioinformatics has become a very “hot” cross-disciplinary field for modern researchers, providing the framework for research in drug discovery, assessment, and development (11). Based on open-access data and bioinformatics algorithms, we systematically investigated the underlying molecules affecting liver cancer immunotherapy. Meanwhile, a CombinedScore was established based on the identified molecules, which showed an excellent prediction ability for the patient’s response to immunotherapy. A significant difference in immunotherapy response was observed in patients with high and low CombinedScore, and the underlying biological differences were identified between these two groups.

Methods

Data collection

Complete next-generation sequencing information and clinical parameters of 371 patients with HCC were obtained from The Cancer Genome Atlas (TCGA) data portal (https://gdc-portal.nci.nih.gov/). RNA-seq data and clinical information of external validation cohorts (ICGC-FR and-JP) samples were obtained from the ICGC portal (https://dcc.icgc.org/). Patients with complete expression profiles and corresponding clinical information were included in our analysis. The “Sva” package in R software combined data and reduced batch effects. Baseline information on enrolled patients is provided in Supplementary File 1.

Evaluation of the immunotherapy response

Evaluation of liver cancer immunotherapy was performed through the Tumor Immune Dysfunction and Exclusion (TIDE) website (http://tide.dfci.harvard.edu/) (12). Briefly, the normalized gene expression profile was the input file. The “cancer type” parameter was set as “Other”. The “previous immunotherapy” parameter was set as “No”. Based on TIDE analysis, patients were assigned a TIDE score according to gene expression profile, with a score < 0 defined as immunotherapy responders, and patients with a TIDE score > 0 considered as non-responders. In addition, the submap algorithm was applied to evaluate the patient’s response to immunotherapy according to the data set of 47 patients with melanoma (13), performed using the Submap module in GenePattern (https://cloud.genepattern.org/).

Identification of differentially expressed genes between responders and non-responders

Differentially expressed genes (DEGs) between responders and non-responders were screened by the “limma” R package based on the criteria |log2-fold change (FC)| > 1 and false discovery rate (FDR) < 0.05.

Optimal variable identification by machine learning algorithms

Two machine learning algorithms, SVM-RFE (Support Vector Machines-Recursive Feature Elimination) and LASSO (Least Absolute Shrinkage and Selection Operator), were utilized to screen the optimal molecule variables between liver cancer immunotherapy responders and non-responders (14). The glm function in R software was utilized for logistic model construction.

Biological exploration

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the “clusterProfiler” package and visualized by the “circlize” package (15). Gene Set Enrichment Analysis (GSEA) was utilized to identify biological differences between two specific groups. Chromosomal number variation (CNV) levels were obtained from the TCGA database.

Estimation of immunological characteristics in the TME

Six methods, namely, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT, and CIBERSORT-ABS, were applied to assess the levels of tumor-infiltrating immune cells (TIICs). We calculated the activity of seven critical steps of cancer immunity cycles with the ssGSEA method (16). We collected 46 key immune checkpoints (PD-1, PD-L1, CTLA-4, etc.) and evaluated their relationship with the CombinedScore. T-cell inflamed score (TIS) reflecting pre-existing anti-cancer immunity in the TME could predict the response to immune checkpoint blockade (17).

Single-cell analysis

The evaluation of specific genes at the single-cell level was conducted based on the Tumor Immune Single-cell Hub website (http://tisch.comp-genomics.org/home/) (18).

Immunohistochemistry staining

Six paired paraffin-embedded primary liver cancer tissues and corresponding adjacent non-tumor tissues were collected at The First Hospital of Jilin University. Immunohistochemistry (IHC) staining was conducted to assess the expression of CDCA7 of the primary liver cancer tissues compared with the corresponding adjacent non-tumor tissues. Briefly, slide-mounted sections were brought to room temperature, dried for 30 min, and then heated at 105°C for 10 min in a citric acid buffer (0.01 M) for antigen retrieval. Hydrogen peroxide (3%) was utilized to inactivate the endogenous enzyme at room temperature for 10 min. Following the blocking step by bovine serum albumin, the primary antibodies against CDCA7 (1:200; Rabbit# abs140624, absin) were applied to incubate the slices overnight at 4°C. After incubation with the secondary antibody at 37°C for 30 min, horseradish peroxidase and diaminobenzidine were used as chromoplast to visualize the immunohistochemical reaction. The slices were re-stained with hematoxylin and sealed with neutral gum. Finally, they were observed with an upright microscope from Leica (Germany) at ×100 and ×400 magnification, respectively.

Statistical analysis

All statistical analyses were performed in R software. Kaplan–Meier (KM) survival curve was used to compare the prognosis between the two groups. According to the data distribution, Student t-tests and Mann–Whitney U tests were applied. The receiver operating characteristic (ROC) curve was utilized to assess the prediction performance of identified variables. A p-value < 0.05 was statistically significant.

Results

DEGs associated with response to immunotherapy

The flowchart of the whole study is shown in Figure S1. First, a TIDE analysis was performed, and each patient was assigned a TIDE score, with scores of <0 and >0 defined as immunotherapy responders and non-responders, respectively (Figure 1A). KM survival analysis revealed that responders had a superior OS compared to non-responders (log-rank test, p = 0.041) (Figure 1B). Next, 569 DEGs were identified between responders and non-responders with the following cutoff criteria: |log2FC| > 1 and FDR < 0.05 (Figure 1C). We then conducted a functional enrichment analysis to explore the potential biological functions of these DEGs. Significant enrichment in biological processes like passive transmembrane transporter activity and channel activity was found (Figure S2). Additionally, KEGG analysis indicated that these DEGs were significantly enriched in cancer-related pathways, including glutamatergic synapse, neuroactive ligand−receptor interaction, and cell adhesion molecules (Figure S3). Subsequently, two distinct algorithms, LASSO and SVM-RFE, were utilized to screen the optimal immunotherapy variables. Thirty feature genes were obtained based on the LASSO algorithm to narrow the range of DEGs (Figures 1D, E). Meanwhile, we performed the SVM-RFE algorithm on a set of 147 feature genes from the top 200 DEGs based on feature value ranks (Figure 1F). After intersecting with genes obtained by the LASSO and SVM-RFE algorithms, 16 candidate genes were identified (GNG8, MYH1, CHRNA3, DPEP1, PRSS35, CKMT1B, CNKSR1, C14orf180, POU3F1, SAG, POU2AF1, IGFBPL1, CDCA7, ZNF492, ZDHHC22, and SFRP2), which were defined as candidate DEGs that determine response to immunotherapy for LIHC (Figures 1G, H).

Figure 1 | Identification of DEGs of liver cancer immunotherapy. (A) TIDE analysis was performed to evaluate the response to immunotherapy in liver cancer patients, with TIDE scores <0 and >0 defining immunotherapy and non-responders, respectively. (B) Kaplan–Meier curves between responders and non-responders. (C) The volcano plot of DEGs between responders and non-responders. (D, E) LASSO algorithm. (F) SVM-RFE algorithm. (G, H) Sixteen DEGs of immunotherapy were identified, namely, GNG8, MYH1, CHRNA3, DPEP1, PRSS35, CKMT1B, CNKSR1, C14orf180, POU3F1, SAG, POU2AF1, IGFBPL1, CDCA7, ZNF492, ZDHHC22, and SFRP2.

Performance of differentially expressed genes and logistic model

We next compared the difference in these DEGs between immunotherapy responders and non-responders. Results indicated that all DEGs associated with response to immunotherapy, including GNG8, MYH1, CHRNA3, DPEP1, PRSS35, CKMT1B, CNKSR1, C14orf180, POU3F1, SAG, POU2AF1, IGFBPL1, CDCA7, ZNF492, ZDHHC22, and SFRP2, were upregulated in the immunotherapy non-responders (Figure 2A). Results of ROC curves showed a good performance for the above DEGs in predicting immunotherapy response (Figures 2B–Q; GNG8, AUC value = 0.836; MYH1, AUC value = 0.721; CHRNA3, AUC value = 0.726; DPEP1, AUC value = 0.870; PRSS35, AUC value = 0.868; CKMT1B, AUC value = 0.746; CNKSR1, AUC value = 0.851; C14orf180, AUC value = 0.709; POU3F1, AUC value = 0.808; SAG, AUC value = 0.740; POU2AF1, AUC value = 0.835; IGFBPL1, AUC value = 0.788; CDCA7, AUC value = 0.865; ZNF492, AUC value = 0.703; ZDHHC22, AUC value = 0.683; SFRP2, AUC value =0.683). Moreover, a logistic model was constructed as follows: CombinedScore = GNG8 *−10.78483 + MYH1 *−13.871063 + CHRNA3 *7.145731 + DPEP1 *1.633124 + PRSS35*1.512479 + CKMT1B *−25.128123 + CNKSR1 *−4.046955 + C14orf180 *−11.320222 + POU3F1 *−3.030164 + SAG *−153.590288 + POU2AF1 *2.342797 + IGFBPL1 *−2.818206 + CDCA7 *−1.219284 + ZNF492 *−14.944845 + ZDHHC22 *−98.522042 + SFRP2 *−8.299996. The model yielded an excellent performance in predicting the response to immunotherapy (Figure 2R, AUC value = 0.996). Principal component analysis of the 16 DEGs could effectively distinguish between immunotherapy responders and non-responders (Figure 2S). Meanwhile, we found that the responders had a higher CombinedScore (Figure 2T).

Figure 2 | Evaluation of the performance of DEGs. (A) Expression level of characteristic genes identified in immunotherapy responders and non-responders. (B–Q) ROC curves were utilized to evaluate the prediction ability of characteristic genes in patients’ immunotherapy response. (R) The ROC curve of CombinedScore, which was calculated based on the identified characteristic molecules. (S) Principal component analysis of the identified genes on patients’ immunotherapy response. (T) The CombinedScore difference in immunotherapy responders and non-responders. *** represents p < 0.001.

The calculated CombinedScore is associated with a higher immunotherapy response

We examined the relationship between clinical and pathological characteristics and the CombinedScore and found significant differences in clinical parameters, including age, gender, and stage (Figures 3A–D). Moreover, we found that the CombinedScore was negatively correlated with the TIDE score, dysfunction score, and exclusion score (Figures 3E–G). Considering the difference between patients with a high and low CombinedScore for immunotherapy, we compared the key immune checkpoints among those patients. Interestingly, the results indicated that all immune checkpoint genes (PD-1, PD-L1, CTLA-4, etc.) were significantly upregulated in patients with a low CombinedScore (Figure 4A). Furthermore, the CombinedScore was negatively correlated with the expression of all immune checkpoints and the scores of various immunotherapy response-related pathways, including the IFN-γ signature, APM signal, and base excision repair (Figures 4B, C). Moreover, the results of the SubMap algorithm revealed that the patients with a low CombinedScore responded better to both PD-1 and CTLA4 blockades (Figure 4D).

Figure 3 | The calculated CombinedScore is associated with clinicopathological features. (A) Relationship of the CombinedScore and clinicopathological features in the TCGA-LIHC cohort. (B–D) Differences in clinicopathological features, including gender, age, and stage between high- and low-CombinedScore groups. (E–G) The CombinedScore had a negative correlation with TIDE score, dysfunction score, and exclusion score. ** represents p < 0.01; *** represents p < 0.001; NS represents not significant.

Figure 4 | The calculated CombinedScore is associated with patient immunotherapy. (A) The key immune checkpoint (PD-1, PD-L1, PD-L2, CTLA-4, etc.) expression in patients with high- and low-CombinedScore in the TCGA-LIHC cohort. (B) The correlations between the CombinedScore and the expression of immune checkpoints. (C) The correlations between the CombinedScore and scores of immunotherapy-predicted pathways. (D) Submap algorithm was conducted to evaluate sensitivity to PD-1 and CTLA-4 blockades in patients with a high- and low-CombinedScore. * represents p < 0.05; ** represents p < 0.01; *** represents p < 0.001.

Validation of the CombinedScore in the ICGC cohort

Next, the ICGC cohort was used to validate our results. The ICGC-FR and ICGC-JP were selected, and a significant batch effect was observed. Using the “sva” package in the R software, we performed a combination of data and significantly reduced the batch effect (Figures 5A, B). In the combined ICGC cohort, we calculated the CombinedScore for validation. A negative correlation was found between the CombinedScore and the TIDE score, dysfunction score, and exclusion score in the combined ICGC (Figures 5C–E). Moreover, all immune checkpoint genes were significantly upregulated in patients with a low CombinedScore (Figure S4A). Furthermore, the CombinedScore was negatively related to the expression of key immune checkpoints and the scores of various immunotherapy response-related pathways, like the IFN-γ signature, APM signal, and base excision repair (Figures S4B, C). Meanwhile, in the ICGC cohort, we found that the patients with a low CombinedScore were more sensitive to PD-1 and CTLA4 blockades (Figure S4D).

Figure 5 | External validation in the ICGC cohort. (A) ICGC-FR and ICGC-JP were selected for validation. (B) The “Sva” package was used for data combination (ICGC-FR and ICGC-JP). (C–E) The CombinedScore had a negative correlation with TIDE score, dysfunction score, and exclusion score in the combined ICGC cohort.

Calculated CombinedScore is associated with liver cancer immune microenvironment

It is well established that a complex immune microenvironment can affect the response to immunotherapy in LIHC patients. We adopted several immune assessment algorithms to investigate the relationship between the CombinedScore and the TME. Interestingly, the ESTIMATE algorithm revealed that the immune score, stromal score, and ESTIMATE score were significantly lower in the high-score group than in the low-score group (Figures 6A–C). The high-score group had higher tumor purity (Figure 6D). We employed six algorithms (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, and EPIC) to estimate the levels of TIICs. It was found that higher infiltration levels of numerous TIICs (including CD8+ T cells, CD4+ T cells, NK cells, macrophages, and DCs) were concentrated in the low-score group (Figure 6E). Spearman correlation analysis further suggested that most recognized TIICs were negatively correlated with the CombinedScore (Figure 6F). Furthermore, there were significant negative correlations between the CombinedScore and the activities of the anti-cancer immunity cycles, such as the release of cancer cell antigens, anti-cancer immune priming and activation, and immune cell trafficking (Figure S5). Consistently, these findings indicated that anti-cancer activity was higher in patients with lower CombinedScore.

Figure 6 | Immune microenvironment analysis. (A–D) Comparisons of the stromal score, immune score, ESTIMATE score, and tumor purity in high- and low-CombinedScore groups. (E) Heatmap showing immune cell infiltration levels in high- and low-CombinedScore groups by six immune infiltration algorithms (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, and EPIC) was used to quantify the immune microenvironment of liver cancer patients. (F) Correlation analysis between the abundance of immune cells infiltrating from six immune infiltration algorithms and the CombinedScore. (G, H) Results of the GSEA in high- and low-CombinedScore groups. *** represents p < 0.001.

Biological difference

Current evidence suggests that biological differences can lead to diverse treatment outcomes. GSEA indicated that in patients with a high CombinedScore, many metabolism pathways were activated, including butanoate metabolism, fatty acid metabolism, glycine serine and threonine metabolism, primary bile acid biosynthesis, and propanoate metabolism (Figure 6G). For patients with a low CombinedScore, cell adhesion molecules (cams), cytokine–receptor interaction, ECM–receptor interaction, hematopoietic cell lineage, and neuroactive ligand–receptor interaction were enriched (Figure 6H). To further explore genomic differences, we quantified the percentage of copy number and the corresponding GISTIC score of TCGA-LIHC patients (Figures 7A, B). Patients with a low CombinedScore had a higher level of frequency of amplification at chromosome 20q and 22q sites and a higher deletion frequency at the 3p, 4p, 4q, 5p, 5q, 7q, 11p, 13q, 14q,15q,16p, 16q, 17p, 19p, and 22q sites (Figure 7C). Moreover, there are some genomic differences between patients with a high- and low CombinedScore in SCNA level (Figure 7D).

Figure 7 | Genomic burden. (A, B) The copy number burden of TCGA-LIHC patients in percentage and GISTIC score level. (C) The genomic difference between patients with high- and low-CombinedScore in amplification frequency and deletion frequency levels. (D) The genomic difference between patients with a high- and low CombinedScore in SCNA level. * represents p < 0.05.

CDCA7 is associated with patient survival

Univariate Cox and KM survival analyses were conducted based on the identified DEGs, and CDCA7 was significantly correlated with patient survival (Figures 8A–D). The correlation analysis indicated that the expression of CDCA7 was positively correlated with Tregs, follicular helper T cells, activated CD4+ memory T cells, resting dendritic cells, and memory B cells, and negatively correlated with resting NK cells, activated NK cells, monocytes, and resting mast cells (Figure 8E). We explored the correlation between CDCA7 and macrophages. Moreover, the result of the CIBERSORT algorithm showed that CDCA7 was positively correlated with M0 macrophages but negatively correlated with M2 macrophages (Figure 8E). Furthermore, the correlation analysis showed that the CombinedScore was positively correlated with M2 macrophages and negatively associated with M0 macrophages (Figures 8F, G). Accordingly, CDCA7 was used for further exploration. Single-cell analysis showed that CDCA7 was expressed mainly in prolif T cells (Figure 8H). Furthermore, our immunohistochemical results confirmed that the staining intensity of CDCA7 was prominently increased in the nucleus in primary liver cancer tissues compared to adjacent non-tumor tissues (Figures 9A–D).

Figure 8 | CDCA7 is associated with patients’ survival. (A) Univariate Cox analysis of the characteristic molecules. (B–D) KM survival curves of CDCA7, POU3F1, and PRSS35. (E) Correlation of immune cell infiltration levels with group infiltration levels using the CIBERSORT algorithm. (F, G) Correlation of CombinedScore infiltration levels with M0 and M2 macrophage. (H) Single-cell analysis of CDCA7 in liver cancer. * represents p < 0.05; ** represents p < 0.01; *** represents p < 0.001.

Figure 9 | Representative immunohistochemical staining of CDCA7 in primary liver cancer tissues and corresponding adjacent non-tumor tissues. (A) The expression of CDCA7 in primary liver cancer tissues (×100). (B) The expression of CDCA7 in corresponding adjacent non-tumor tissues (×100). (C) The expression of CDCA7 in primary liver cancer tissues (×400). (D) The expression of CDCA7 in corresponding adjacent non-tumor tissues (×400).

Discussion

Liver cancer is the leading cause of cancer-related deaths globally and ranks fifth in the United States (1). The incidence of liver disease in developing countries is higher than in developed countries, making liver cancer one of the deadliest cancers (19). Various dietary exposures, genomic differences, lifestyle, hepatitis virus, and fat are risk factors for liver cancer (20). Current evidence suggests that liver cancer is associated with a poor prognosis, with only 5% to 15% of patients indicated for surgical resection with early-stage disease (21). Consequently, much emphasis has been placed on identifying new treatment options. Interestingly, it is widely believed that combining drugs and altering drug administration/delivery methods can improve malignant tumor outcomes (22). The death of cancer cells is usually mediated by the immune system. MHC molecules of class I and II receive tumor-fragmented peptides during this process (23). Additionally, tumor progression can be prevented by targeting tumor growth biomarkers and connective tissue formation (24). There is no doubt that immunotherapy has great potential for clinical application in liver cancer patients.

Here, we performed a TIDE analysis to evaluate the response rate to immunotherapy in LIHC patients. Based on LASSO logistic regression and SVM-RFE algorithms, we identified GNG8, MYH1, CHRNA3, DPEP1, PRSS35, CKMT1B, CNKSR1, C14orf180, POU3F1, SAG, POU2AF1, IGFBPL1, CDCA7, ZNF492, ZDHHC22, and SFRP2 as DEGs of liver cancer immunotherapy. During clinical practice, detecting the relative expression of these characteristic genes could help clinicians predict the response to liver cancer immunotherapy, guiding individualized therapy. We then constructed a logistic model based on DEGs for immunotherapy, which was also verified in the combined ICGC cohort. Notably, the complex interaction between the TME and tumor cells significantly affects immune escape and immunotherapeutic efficacy (25). Therefore, understanding the TME status and the proportion of immune cell infiltration might contribute to optimizing treatment strategies and assessing tumor prognosis.

We investigated the underlying players of the CombinedScore in terms of TME features. Patients with a lower CombinedScore had higher ESTIMATE, immune, and stromal scores and lower tumor purity. The results of six different immune filtration platforms revealed that patients with a lower CombinedScore had relatively high innate and adaptive TIIC infiltration, while patients with a higher CombinedScore exhibited relatively low TIIC infiltration. As a result, patients with a lower CombinedScore exhibited higher activity of preexisting antitumor immunity in TME.

Interestingly, the results of the TIDE algorithm suggested that the CombinedScore was negatively correlated with the TIDE score and dysfunction score. Indeed, the TIDE score integrated T-cell dysfunction and removal characteristics and simulated tumor-immune escape with different levels of TIICs (26). In many solid tumors, although TIICs significantly infiltrate the tumors, T-cell dysfunction is observed with higher tumor infiltration of macrophages (M2) and the overexpression of many inhibitory immune checkpoints, which could weaken the ability of TIICs to kill tumor cells and promote the growth and progression of tumors, resulting in tumor invasion and metastasis (26, 27). This finding was consistent to a certain extent with our results. We found that the CombinedScore was negatively related to the scores of several immunotherapy-predicted signatures and steps of the cancer-immunity cycle.

Furthermore, our study confirmed that the CombinedScore had a negative relationship with the expression of key immune checkpoints, showing a higher expression in the low CombinedScore group. These results emphasized that although patients with a low CombinedScore had more TIICs in the TME, its immune cell dysfunction and immune escape were also stronger, which could weaken the ability of immune cells to kill tumor cells, consistent with the literature. Due to the higher immunosuppression and lower immunoreactivity in the TME, such patients were often more suitable for immune checkpoint inhibitor therapy (28). We also confirmed that the low CombinedScore group may respond better to immune checkpoint inhibitor treatment using the SubMAP algorithm.

We then performed GSEA to investigate the significant pathways differentially activated between high- and low-CombinedScore groups. GSEA showed that upregulated pathways included butanoate metabolism, fatty acid metabolism, glycine serine and threonine metabolism, primary bile acid biosynthesis, and propanoate metabolism. In most tumors, lipid metabolism is abnormally activated, which allows them to produce, prolong, and desaturate fatty acids to fuel their growth (29). An alteration in cancer cell energy metabolism influences immune responses in the TME, which promote tumor growth (30). It has been shown that PIWIL1 enhances energy production by regulating fatty acid metabolism and inhibits liver cancer progression (31). While elucidating the results of logistic modeling, we also discussed the roles of individual characteristic molecules of immunotherapy, specifically in tumor immunomodulation.

Interestingly, we found that CDCA7 exhibited a negative association with tumor prognosis. A previous study showed that CDCA7 was highly expressed in HCC, which could facilitate cell proliferation and invasion of HCC by recruiting CEBPB to elevate the expression of EZH2 (32). Cai et al. also demonstrated that the downregulation of CDCA7 suppressed EZH2 expression to arrest angiogenesis (33). Moreover, the target gene of Myc CDCA7 is reportedly overexpressed in human cancer and promotes tumor transformation (34, 35). Li et al. indicated that CDCA7 was highly involved in EMT by regulating the expression of Smad4 and Smad7 in the TGF-β signaling pathway (36). Thereafter, we focused on analyzing the specific association between CDCA7 and immunocyte infiltration and found that CDCA7 was negatively correlated with M2 macrophages and positively correlated with M0 macrophages. Our findings suggested that CDCA7 could influence liver cancer cell progression by affecting macrophage polarization, providing a theoretical basis for liver cancer immunotherapy. Future studies are warranted to validate the effects of CDCA7 on tumor biology and macrophage polarization in biological experiments such as cell culture and mouse models.

In summary, based on high-quality data and analysis, our findings provide a foothold for future studies in liver cancer immunotherapy. We developed and validated a well-grounded logistic model according to TIDE results in LIHC, which could effectively predict the immunotherapy response of liver cancer patients. Meanwhile, several limitations and shortcomings were found in this study. First, the findings of our study may be affected to a certain extent by racial bias, given the low proportion of Asians and Africans in the enrolled samples. Furthermore, in some cases, clinical information was incomplete, such as information on M-stage for a large percentage of patients. Finally, it should be borne in mind that the immunotherapy response predicted by TIDE was determined by a bioinformatics algorithm, which exhibits a limited ability to reflect real-world situations. More robust conclusions are expected from studies analyzing the genomic data of LIHC patients treated with immunotherapy in the future.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author/s.

Ethics statement

The studies involving human participants were reviewed and approved by the ethics committee of the first hospital of Jilin University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author contributions

KL and JC contributed to the conception and design of the study. JC and HZ performed data extraction and analysis. JC and HJ wrote the first draft of this manuscript and KL completed the final version of the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This study was funded by the Jilin Science and Technology Development Program (CN) (No. 20191102031YY).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1029427/full#supplementary-material

Supplementary Figure 1 | The whole flow chart of the study.

Supplementary Figure 2 | GO enrichment analyses of DEGs.

Supplementary Figure 3 | KEGG enrichment analyses of DEGs.

Supplementary Figure 4 | (A) Expression of key immune checkpoints (PD-1, PD-L1, PD-L2, CTLA-4, etc.) in patients with a high- and low-CombinedScore in the combined ICGC cohort; (B) Correlations between the CombinedScore and expression of immune checkpoints in the combined ICGC cohort; (C) Correlations between the CombinedScore and immunotherapy predicted pathways scores in the combined ICGC cohort; (D) Submap algorithm was conducted to evaluate the sensitivity to PD-1 and CTLA-4 blockades in patients with a high- and low-CombinedScore in the combined ICGC cohort.

Supplementary Figure 5 | Correlation analysis between CombinedScore and the activities of the anticancer immunity cycles.

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ORIGINAL RESEARCH
published: 16 February 2023
doi: 10.3389/fimmu.2023.1107542

Intensity-modulated radiotherapy combined with systemic atezolizumab and bevacizumab in treatment of hepatocellular carcinoma with extrahepatic portal vein tumor thrombus: A preliminary multicenter single-arm prospective study

Kang Wang ¹†, Yan-Jun Xiang ¹†, Hong-Ming Yu ¹†, Yu-Qiang Cheng ¹†, Zong-Han Liu ¹, Jing-Ya Zhong ², Shuang Feng ³, Qian-Zhi Ni ¹,⁴, Hong-Fei Zhu ¹, Wei-Wei Pan ²,⁵, Jing-Jing Li ⁴, Chao Liang ⁶, Hong-Kun Zhou ⁷, Yan Meng ³, Wan Yee Lau ¹,⁸ and Shu-Qun Cheng ¹,²,⁶,⁷*

¹ Department of Hepatic Surgery VI, Eastern Hepatobiliary Surgery Hospital, Naval Medical University, Shanghai, China, ² Department of Cell Biology, College of Medicine, Jiaxing University, Jiaxing, China, ³ Department of Radiotherapy, Eastern Hepatobiliary Surgery Hospital, Naval Medical University Shanghai, Shanghai, China, ⁴ CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China, ⁵ G60 STI Valley Industry & Innovation Institute, Jiaxing University, Jiaxing, China, ⁶ Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China, ⁷ The First Hospital of Jiaxing Affiliated Hospital of Jiaxing University, Jiaxing University, Jiaxing, China, ⁸ Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Muhammad Khan, Guangzhou Medical University Cancer Hospital, China
Minggen Hu, Institute of Hepatobiliary Surgery of Chinese PLA, China

*Correspondence:
Shu-Qun Cheng
chengshuqun@aliyun.com

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 26 November 2022

Accepted: 08 February 2023

Published: 16 February 2023

Citation:
Wang K, Xiang Y-J, Yu H-M, Cheng Y-Q, Liu Z-H, Zhong J-Y, Feng S, Ni Q-Z, Zhu H-F, Pan W-W, Li J-J, Liang C, Zhou H-K, Meng Y, Lau WY and Cheng S-Q (2023) Intensity-modulated radiotherapy combined with systemic atezolizumab and bevacizumab in treatment of hepatocellular carcinoma with extrahepatic portal vein tumor thrombus: A preliminary multicenter single-arm prospective study. Front. Immunol. 14:1107542. doi: 10.3389/fimmu.2023.1107542

Background and aims

The efficacy and safety of systemic atezolizumab and bevacizumab (atezo/bev) in treatment of patients with unresectable hepatocellular carcinoma (HCC) have been demonstrated. However, the efficacy of this treatment in patients with HCC and extrahepatic portal vein tumor thrombus (ePVTT) is not satisfactory. This study aimed to study the efficacy and safety of combining intensity-modulated radiotherapy (IMRT) with systemic atezo/bev in treatment of these patients.

Methods

This multicenter prospective study included patients with ePVTT treated with IMRT combined with atezo/bev from March to September 2021 in three centers in China. The outcomes of this study included objective response rate (ORR), overall survival (OS), progression-free survival (PFS), time to progression (TTP), and association between response and tumor mutational burden (TMB). Treatment-related adverse events (TRAEs) were analyzed to assess safety.

Results

Of 30 patients in this study, the median follow-up was 7.4 months. Based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, the ORR was 76.6%, the median OS for the entire cohort was 9.8 months, the median PFS was 8.0 months, and the median TTP was not reached. This study failed to establish a significant correlation between TMB with any of the following outcomes, including ORR, OS, PFS or TTP. The most common TRAEs at all levels were neutropenia (46.7%), and the most common grade 3/4 TRAE was hypertension (16.7%). There was no treatment-related deaths.

Conclusions

IMRT combined with atezo/bev showed encouraging treatment efficacy with an acceptable safety profile, making this treatment to be a promising option for HCC patients with ePVTT. Further studies are required to support the findings of this preliminary study.

Clinical trial registration

http://www.chictr.org.cn, Identifier ChiCTR2200061793.

Keywords: PD-L1 inhibitor, immunotherapy, intensity-modulated radiotherapy, combination therapy, macrovascular invasion

Introduction

Hepatocellular carcinoma (HCC), a primary liver cancer, has been a hot topic in cancer research because of its high morbidity and mortality (1). Approximately 10 to 40% of HCC patients present with portal vein tumor thrombus (PVTT) at the time of diagnosis. The prognosis of these patients is poor, and the median overall survival (OS) ranges from 2.7 to 4.0 months (2). Treatment of HCC patients with PVTT remains a great challenge.

Oral administrations of sorafenib or lenvatinib are the standard first-line treatment for patients with advanced HCC, but prognosis is not satisfactory, especially in patients with PVTT (3–6). A recent clinical trial (IMbrave150) showed that combined systemic atezolizumab with bevacizumab (atezo/bev) gave better treatment survival outcomes for patients with unresectable HCC when compared with sorafenib (7–9). The astounding efficacy of atezo/bev has made it a possibility to be used as a first-line therapy for advanced HCC. Even for HCC patients with PVTT, atezo/bev therapy has been shown to improve median OS of these patients (10, 11).

Systemic therapy combined with locoregional therapy has been shown to improve prognosis of HCC patients with PVTT (12, 13). Of the locoregional therapies, intensity-modulated radiotherapy (IMRT) has shown great promise in local control of PVTT (14, 15). A previous randomized controlled trial from our group demonstrated that preoperative neoadjuvant three-dimensional conformal radiotherapy significantly improved outcomes in patients with PVTT (16). Interestingly, PVTT is more sensitive to radiotherapy as compared to the primary tumor (17). A toxicological study of atezo/bev combo in the treatment of patients with unresectable HCC using atezo/bev combined with radiotherapy to the tumor showed the combined treatment was well tolerated with a high safety profile (18). Therefore, combining IMRT can be a more effective treatment for HCC patients with PVTT.

In this prospective study, the efficacy and safety of systemic atezo/bev combined with IMRT in HCC patients with extrahepatic PVTT (ePVTT), and novel biomarkers in predicting response to treatment by genomic profiling, were studied.

Patients and methods

Patients selection

Patients with HCC and ePVTT treated with atezo/bev combined with IMRT from March to September 2021 at Eastern Hepatobiliary Hospital, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, and the First Hospital of Jiaxing Affiliated Hospital of Jiaxing University were prospectively studied. The inclusion criteria were patients with (1) HCC with ePVTT as defined in the subsequent section of this article (2); age over 18 years (3); unresectable HCC as assessed by experienced liver surgeons (4); good general status and liver function of Pugh-Child A or B. The exclusion criteria were patients with (1) contraindications to atezo/bev or IMRT (2); prior HCC treatment (3); history of other cancers (4); pregnancy. Patients with extrahepatic spread of HCC was not a contraindication of this study.

This study was carried out in compliance with the ethical standards of Declaration of Helsinki. It was approved by the Institutional Ethics Committee of each center. Informed consent was obtained from all patients prior to treatment and for their data to be used in clinical research. This study is registered with the Chinese Clinical Trial Registry, ChiCTR2200061793.

Diagnosis and treatment

In this study, HCC was diagnosed based on alpha fetoprotein levels, dynamic computed tomography, dynamic magnetic resonance imaging, or pathology findings (5, 19). PVTT was diagnosed based on the European Association for the Study of the Liver (EASL) Guidelines and the Chinese Expert Consensus on Multidisciplinary Diagnosis and Treatment of PVTT (19, 20). PVTT was staged according to the Cheng’s classification and the portal vein invasion (Vp) classification (21–23). Extrahepatic PVTT (ePVTT) was defined as PVTT which had extended extrahepatically to involve either the right or left portal vein (Rt/Lt PV), the main portal vein (MPV) and the superior mesenteric vein (SMV). High-risk status was defined as PVTT invasion of MPV and/or SMV (Vp4), and/or tumor occupancy of ≥ 50% of liver (10, 11).

After a multidisciplinary consultation to include patients who were considered to be appropriate to receive atezo/bev combined with IMRT, these patients were fully informed about the potential efficacy of the combined treatment, treatment-related adverse events (TRAEs) and costs of treatment.

IMRT was first given to these patients as previously reported (16). Each radiotherapy cycle was 28 days, with a planned total dose of 52-56 Gy on the planned target area of ePVTT at 200 cGy/dose. Systemic atezo/bev was initiated 3 ± 1 days after completion of radiotherapy, and each cycle of systemic therapy was 21 days. A fixed dose of 1200 mg of atezolizumab was administered intravenously on the first day of each systemic therapy cycle (60 min for the first session, 30 min for subsequent sessions if tolerated), and bevacizumab 15 mg/kg was administered intravenously at least 5 min apart (90 min for the first session, 30 min for subsequent sessions if tolerated). Treatment continued until unacceptable toxicity or loss of clinical benefit as assessed by investigators based on imaging findings, biochemical parameters and patient’s clinical status.

Follow-up and outcomes

All patients were followed-up in the outpatient clinic once every 6 weeks. At each follow-up visit, there were routine history taking, physical examination, laboratory blood tests, and abdominal ultrasound or enhanced CT/MRI. Assessment of tumor progression was based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and the modified RECIST (mRECIST).

The primary outcome of this study was the objective response rate (ORR) which was defined as the proportion of patients in complete response (CR) and partial response (PR). The secondary outcomes included OS, progression-free survival (PFS), and time to progression (TTP). TRAEs were assessed based on the criteria of Common Terminology Criteria for Adverse Events, version 5.0. For multiple instances of the same toxicity for each patient, the highest grade in a given category was adopted.

Definition of outcomes

Tumor responses were evaluated as previously reported (16). Briefly stated, patients were diagnosed to have progressive disease (PD) when either the primary tumor or ePVTT was evaluated as PD; stable disease (SD) when both lesions were evaluated as SD; PR when either one of these two lesions was evaluated as PR; and complete response (CR) when both lesions were evaluated as CR. PVTT response was defined according to the upstaging or downstaging in Cheng’s PVTT classification. Patients were treated with optimal treatments based on their general condition, liver function and extent of HCC after PD.

OS was defined as the time from the first study treatment to the date of death from any cause, or the date of the last follow-up. PFS was defined as the time from the first study treatment to tumor progression, death from any cause, or the last follow-up. TTP was defined as the time from the first study treatment to the appearance of any objective evidence of tumor progression. Tumor progression was assessed based on RECIST version 1.1.

Biomarker analysis

Optional baseline biopsy specimens were obtained from patients for exploratory biomarker evaluation. Specifically, genomic DNA was extracted from tumor tissues, followed by next-generation sequencing and analysis of tumor mutation burden (TMB). Differences in gene mutations between the response group (patients with CR or PR) and the non-response group (patients with SD or PD) were compared.

Statistical analysis

All clinical data were analyzed using IBM SPSS Statistics 23 and R 4.0.2 software (http://www.r-project.org/). In baseline variables considered as binary variables, the cutoff values were defined based on uniform consensus. The optimal cutoff value of TMB was calculated using the “pROC” package of R based on the maximum Youden index to distinguish high from low TMB. OS, PFS and TTP were estimated using the Kaplan-Meier method and compared with the log-rank test. The Fisher exact test was used to compare variables, and a P < 0.05 was defined as significantly different.

Results

Patients

Thirty patients were included into this study (Figure 1). The clinicopathological characteristics of these patients are summarized in Table 1. The median age of a total of 30 patients was 56.5 years (range, 37-38 years), of whom 25 patients (83.3%) were male, 26 patients (86.7%) had co-infection with HBV, and 25 patients (83.3%) with cirrhosis. Eighteen patients (60.0%) had ePVTT extending to the MPV or SMV while 12 patients (40%) had ePVTT involving just the Rt/Lt PV. Concomitant extrahepatic metastases were present in 16 patients (53.3%), and combined hepatic vein carcinoma thrombus was present in nine patients (30.0%). Twenty-nine patients were at high-risk status, 11 of them with tumor ≥50% of the liver, six with PVTT extending to MPV or SMV, and 12 with both of these risk factors. On March 16, 2022 when this study was censored, and no patient withdrawals. The median follow-up for the entire cohort was 7.4 months, and the median cycle of atezo/bev was eight.

Figure 1 | Patient Flow Diagram. Atezo/bev, atezolizumab and bevacizumab; IMRT, intensity-modulated radiotherapy.

Table 1 | Baseline characteristics of study patients.

Treatment efficacy

Table 2 summarizes the best tumor response for all patients. Based on RECIST version 1.1, the ORR was 76.6% and the disease control rate (DCR) was 96.7%. On subgroup analysis, the ORR and DCR were 75.0% and 100% for ePVTT in Rt/Lt PV, and 77.8% and 94.4% for ePVTT extending to MPV or SMV, respectively. Based on mRECIST, the ORR and DCR for the entire cohort were 90.0% and 96.7%, respectively, and the ORR and DCR for ePVTT in Rt/Lt PV were 91.7% and 100%, respectively, and 88.9% and 94.4% for ePVTT extending to MPV or SMV, respectively.

Table 2 | Best tumor response.

Based on RECIST version 1.1, 29 patients had a reduction in size of the primary tumor when compared to baseline (Figure 2). Details of the response durations and outcomes are presented in Figure 2. Of the 30 patients in this study, 13 patients had died during follow-up (suspected causes of death are detailed in Supplemental Table 1). Eleven surviving patients continued to receive atezo/bev, while three patients had discontinued atezo/bev (two due to tumor progression and one due to financial burden). Three patients underwent liver resection aiming at cure after tumor-downstaging. The imagings and pathological information of the 3 operated patients are shown in Figure 3.

Figure 2 | Characteristics of objective response in patients with intensity-modulated radiotherapy (IMRT) radiotherapy combined with atezolizumab plus bevacizumab. (A) The maximum percentage reduction from baseline in primary tumor. (B) Duration of response.

Figure 3 | Imaging and pathological information of patients who underwent hepatectomy. (A) Pre- and post-treatment MRI of patient #8, and (B) post-operative pathology showed pathological complete response. (C) Pre- and post-treatment MRI of patient #11, and (D) postoperative pathology showed partial necrosis. (E) Pre- and post-treatment MRI of patient #18, and (F) postoperative pathology showed a large area of coagulative necrosis (80%). Yellow lines and arrows for hepatocellular carcinoma, black arrows for coagulative necrosis. MRI, magnetic resonance imaging.

The median OS was 9.8 months for the entire cohort (Figure 4), not reached for ePVTT in Rt/Lt PV and 9.8 months for ePVTT extending to MPV or SMV (Figure 4). The median PFS was 8.0 months for the entire cohort (Figure 4), 10.1 and 7.1 months for ePVTT in Rt/Lt PV and ePVTT extending to MPV or SMV, respectively (Figure 4); the median TTP was not reached for the entire cohort (Figure 4), 10.5 months for ePVTT in Rt/Lt PV, and was not reached for ePVTT extending to MPV or SMV (Figure 4).

Figure 4 | Kaplan-Meier curves for overall survival (OS), progression-free survival (PFS), and time to progression (TTP) for the entire cohort of patients (A, C, E) and portal vein tumor thrombus subgroups (B, D, F). Rt/Lt, right or left portal vein; MPV, main portal vein; SMV, superior mesenteric vein.

To better compare the results with those of the IMbrave150, patients with high-risk status were analyzed. The results showed 29 high-risk patients to have a median OS of 9.8 months and a median PFS of 8.0 months (Supplemental Figure 1).

Safety

TRAEs during treatment based on their frequency and severity were evaluated using the CTCAE version 5.0. As shown in Table 3, the most common TRAEs at all levels were neutropenia (46.7%), fatigue (40.0%) and hypertension (33.3%), and the most common grade 3/4 TRAEs were hypertension (10.0%) and aspartate aminotransferase elevation (10%). No patient developed classical radiation-induced liver disease, and there was no treatment-related death.

Table 3 | Treatment emergent adverse events.

Biomarkers

Pathological tissues were obtained from 15 of 30 HCC patients, including specimens from 12 PR patients and 3 SD patients. These optional biopsy specimens were obtained for exploratory biomarker assessment. Based on tumor response to treatment, 228 mutations/megabase were used as the threshold of TMB to distinguish between high and low TMB. The results showed that ORR was 66.7% in the high TMB group and 100% in the low TMB group, and there was no significant difference between the two groups (Supplemental Figure 2, P=0.229). No association was observed between TMB with OS (HR=0.815, 95%CI= 0.215-3.082, P=0.763, Supplemental Figure 3), with PFS (HR=0.663, 95%CI= 0.200-2.202, P=0.502, Supplemental Figure 3) or with TTP (HR=0.335, 95%CI= 0.059-2.142, P=0.259, Supplemental Figure 3). Supplemental Figure 2 depicts the distributions of genetic variation which were associated with response to the combined treatment using atezo/bev and IMRT. The most frequently altered genes in the response group when compared with the non-response group were CARS1P1, CCDC146, GLI2, SART3 and SSU72P3 (P<0.05).

Discussion

The efficacy and toxicity of IMRT in combination with systemic atezo/bev in 30 HCC patients with ePVTT were studied. The ORR of entire cohort was 76.6%, with a median OS and PFS of 9.8 and 8.0 months, respectively. To our knowledge, this is the first reported study of using combined IMRT with atezo/bev to treat HCC patients with ePVTT.

HCC with PVTT is generally considered to be at an advanced stage of disease. Although there are still some controversies on whether PVTT affecting secondary and tertiary branches of the main portal vein should be treated with liver resection, there is little controversy that once the extrahepatic portal venous system is affected by PVTT (ePVTT), systemic therapy is recommended by treatment guidelines as the standard of care for these patients (19, 20, 24–26). Tumor invasion into the portal venous system not only promotes intrahepatic tumor spread of the disease, but when the extrahepatic portal venous system is involved, the portal blood supply to the liver is rapidly reduced, resulting in rapid deterioration of liver function, and increase in portal pressure with its risks of complication, which can lead to further limitations of choice of treatment options (27). Any measures that can lead to a reduction in ePVTT volume can result in increasing portal blood supply to the liver and decrease in portal hypertension, thus facilitating subsequent choice of treatment for HCC patients with ePVTT. External radiation has long been used as a local-regional treatment of HCC. A recent study reported that PVTT was more sensitive to radiotherapy than the primary HCC (17, 28–30). A recent open-label randomized clinical trial showed that TACE combined with external beam radiotherapy provided better PFS, ORR, TTP, and OS when compared with sorafenib in HCC patients with PVTT (31), and external radiotherapy has been shown to be more effective than sorafenib in treatment of HCC patients with ePVTT (5). In addition, as external radiotherapy can trigger immunogenic cell death resulting in release of cytokines and damage-associated molecular patterns (DAMPs), subsequent priming and trafficking of tumor-specific T lymphocytes into the tumor microenvironment by DAMPs can enhance recruitment of antigen-presenting cells, processing of tumor-associated antigens, and cross presentation of antigenic peptides on major histocompatibility complex class I, thereby improving the efficacy of immunotherapy (32). All these provide the theoretical basis for the combination therapy of IMRT with atezo/bev in treatment of HCC patients with ePVTT.

The extent of HCC in this study was advanced, as evidenced by almost all patients (96.7%) were at high-risk status. In this study, the median OS and PFS for such patients were 9.8 and 8.0 months, respectively, which were higher than the 7.6 and 5.4 months in the IMbrave 150 (10). A better oncological prognosis was also observed in the group of patients with PVTT extending to MPV or SMV (11). Considering the small sample size of this study, no definitive conclusions can be drawn that this treatment regimen is superior to IMbrave 150. However, the data supports that this new treatment improved prognosis of HCC patients with ePVTT. This treatment has also been shown to be safe, with the most common adverse effect being hypertension, similar to the results previously reported (7, 33). As gastrointestinal bleeding and perforation have been reported to occur on using bevacizumab, 2 patients in our study developed grade 3 or higher gastrointestinal bleeding, a rate which was higher than those previously reported on using only atezo/bev (7, 33). This may be due to the fact that most of the included patients with VP4 PVTT (34). However, no patients died from treatment-related side effects which was unexpected given the relatively advanced HCC stages of our patients who had also received IMRT (35, 36). Esophagogastroduodenoscopy should be used as a routine before patients receiving treatment using combined IMRT and atezo/bev.

This study on using biomarkers failed to establish any correlations between TMB with any one of the following treatment outcomes, including ORR, OS, PFS or TTP. These findings are consistent with the results of previously reported studies on immunotherapy (37, 38). Instead, the tumor response seemed to be associated with alterations in some specific genes. Overexpression of GLI2 has been reported to cause resistance to targeted cancer therapy in cancer cells, and another study reported that knockdown of GLI1 and GLI2 restored sensitivity to vemurafenib-resistant cells (39, 40). However, the small sample size of our study limited further analysis of these reported findings, and clinical utility of these findings remains to be further studied in the future.

There are several limitations of the study. First, this is multicenter study on a small sample size of patients. Second, the short follow-up was not sufficient to assess long-term survival outcomes in patients treated with IMRT and atezo/bev. As a consequence, ORR was used as the primary outcome. Third, this study was conducted in China where hepatitis B infection is endemic. The results of this study may not be applicable to HCC patients seen in the West where etiologies of HCC are mainly due to hepatitis C infection, alcoholism and non-alcoholic steatohepatitis.

In conclusion, combined IMRT with atezo/bev resulted in encouraging treatment efficacy and acceptable safety, making the treatment to be a promising option for patients with HCC and ePVTT. Comprehensive genomic analysis of mutation profile involving multiple biomarkers helps in providing a framework for molecularly stratified treatment of advanced HCC in future studies.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving human participants were reviewed and approved by the Institutional Ethics Committees of the Eastern Hepatobiliary Hospital. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

Conceptualization: S-QC and KW. Funding acquisition: S-QC and KW. Resources: KW, Y-JX, H-MY, Y-QC, and Z-HL. Investigation: J-YZ, SF, H-FZ, W-WP, Q-ZN, J-JL, CL, H-KZ, and YM. Formal analysis: KW, Y-JX, H-MY, Y-QC, Z-HL, and J-YZ. Writing–original draft: All authors. Writing–review & editing: WL and S-QC. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR1004A), the Key Project of the National Natural Science Foundation of China (81730097), the National Natural Science Foundation of China (82072618), and the National Key Research and Development Program of China (2022YFC2503700).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1107542/full#supplementary-material

Supplementary Figure 1 | Kaplan-Meier curves for (A) overall survival, and (B) progression-free survival for the high-risk patients. High-risk status was defined as hepatocellular carcinoma with portal vein tumor thrombosis extending to the main portal vein and/or the superior mesenteric vein, and/or tumor ≥50% of the liver.

Supplementary Figure 2 | (A) Treatment response of efficacy-evaluable patients stratified by tumor mentational burden (high v low). (B) Distribution of genetic variations associated with treatment response. In one of the responding patients, none of the above genes were altered.

Supplementary Figure 3 | Kaplan-Meier curves for (A) overall survival, (B) progression-free survival, and (C) time to progression for the tumor mutation burden subgroups.

Abbreviations

Atezo/bev, atezolizumab and bevacizumab; CR, complete response; DAMPs, damage-associated molecular patterns; EASL, European Association for the Study of the Liver; ePVTT, extrahepatic portal vein tumor thrombus; HCC, hepatocellular carcinoma; IMRT, intensity-modulated radiotherapy; MPV, main portal vein; mRECIST, modified Response Evaluation Criteria in Solid Tumors; ORR, objective response rate; OS, overall survival; PFS, progression-free survival; PR, partial response; PVTT, portal vein tumor thrombus; RECIST, Response Evaluation Criteria in Solid Tumors; Rt/Lt PV, right or left portal vein; SMV, superior mesenteric vein; TMB, tumor mutational burden; TRAEs, treatment-related adverse events; TTP, time to progression.

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ORIGINAL RESEARCH
published: 23 February 2023
doi: 10.3389/fimmu.2023.1141199

Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy

De-Zhen Guo ¹,²,³†, Shi-Yu Zhang ¹,²,³†, San-Yuan Dong ⁴, Jia-Yan Yan ¹,²,³, Yu-Peng Wang ¹,²,³, Ya Cao ⁵,⁶, Sheng-Xiang Rao ⁴, Jia Fan ¹,²,³,⁷, Xin-Rong Yang ¹,²,³*, Ao Huang ¹,²,³* and Jian Zhou ¹,²,³,⁷,⁸*

¹ Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China, ² Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Fudan University, Shanghai, China, ³ Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China, ⁴ Department of Radiology, Zhongshan Hospital, Fudan University Shanghai Institute of Medical Imaging, Shanghai, China, ⁵ Cancer Research Institute, Central South University, Changsha, China, ⁶ Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Changsha, China, ⁷ Institute of Biomedical Sciences, Fudan University, Shanghai, China, ⁸ State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Hong Zhao, Chinese Academy of Medical Sciences, China
Tao Li, Qilu Hospital, Shandong University, China
Kenta Motomura, Aso Iizuka Hospital, Japan

*Correspondence:
Xin-Rong Yang
yang.xinrong@zs-hospital.sh.cn 
Ao Huang
huang.ao@zs-hospital.sh.cn 
Jian Zhou
zhou.jian@zs-hospital.sh.cn

†These authors have contributed equally to this work and share first authorship

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 10 January 2023

Accepted: 13 February 2023

Published: 23 February 2023

Citation:
Guo D-Z, Zhang S-Y, Dong S-Y, Yan J-Y, Wang Y-P, Cao Y, Rao S-X, Fan J, Yang X-R, Huang A and Zhou J (2023) Prognostic model for predicting outcome and guiding treatment decision for unresectable hepatocellular carcinoma treated with lenvatinib monotherapy or lenvatinib plus immunotherapy. Front. Immunol. 14:1141199. doi: 10.3389/fimmu.2023.1141199

Background

Lenvatinib monotherapy and combination therapy with immune checkpoint inhibitors (ICI) were widely applied for unresectable hepatocellular carcinoma (uHCC). However, many patients failed to benefit from the treatments. A prognostic model was needed to predict the treatment outcomes and guide clinical decisions.

Methods

304 patients receiving lenvatinib monotherapy or lenvatinib plus ICI for uHCC were retrospectively included. The risk factors derived from the multivariate analysis were used to construct the predictive model. The C-index and area under the receiver-operating characteristic curve (AUC) were calculated to assess the predictive efficiency.

Results

Multivariate analysis revealed that protein induced by vitamin K absence or antagonist-II (PIVKA-II) (HR, 2.05; P=0.001) and metastasis (HR, 2.07; P<0.001) were independent risk factors of overall survival (OS) in the training cohort. Herein, we constructed a prognostic model called PIMET score and stratified patients into the PIMET-low group (without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-high group (with metastasis and PIVKA-II>600 mAU/mL). The C-index of PIMET score for the survival prediction was 0.63 and 0.67 in the training and validation cohort, respectively. In the training cohort, the AUC of 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively. The prognostic performances of the model were subsequently validated. The AUC of 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762 in the validation cohort. Subgroup analyses showed consistent predictive value for patients receiving lenvatinib monotherapy and patients receiving lenvatinib plus ICI. The PIMET score could also distinguish patients with different treatment responses. Notably, the combination of lenvatinib and ICI conferred survival benefits to patients with PIMET-int or PIMET-high, instead of patients with PIMET-low.

Conclusion

The PIMET score comprising metastasis and PIVKA-II could serve as a helpful prognostic model for uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. The PIMET score could guide the treatment decision and facilitate precision medicine for uHCC patients.

Keywords: predicting model, liver cancer, lenvatinib, immunotherapy, protein induced by vitamin K absence or antagonist-II

Introduction

Hepatocellular carcinoma (HCC) is a common malignancy worldwide and a leading cause of cancer-related death (1, 2). Patients with HCC are frequently diagnosed at an advanced stage and are not eligible for curative treatments such as surgical resection, liver transplantation, or ablation, leading to a poor prognosis (3, 4).

Recently, lenvatinib, a tyrosine kinase inhibitor, showed favorable efficacy for advanced HCC and was approved as one of the first-line therapy by the FDA (5). Furthermore, the combination of lenvatinib and immune checkpoint inhibitors (ICI) showed improved efficacy in several malignancies (6–9). However, many patients with unresectable HCC (uHCC) failed to benefit from these treatments. Several prognostic models were established to predict the efficacies and outcomes of treatments (10–13). However, majority of models were built only for patients receiving lenvatinib monotherapy or immunotherapy, which limited their application. A prognostic model to predict the outcomes of both lenvatinib monotherapy and combination therapy of lenvatinib and ICI was lacking.

In addition, the combination of lenvatinib and ICI failed to show the superior efficacy in HCC compared with lenvatinib monotherapy in the phase III clinical trial LEAP-002, indicating the necessity of patient selection for the combination therapy. A model with the ability to identify patients with uHCC who might benefit from lenvatinib plus ICI could help patient selection and guide treatment decisions.

In this study, we constructed a prognostic model for patients with uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. Importantly, the newly developed model could identify the patients who might benefit from lenvatinib plus ICI and guide the clinical treatment decisions.

Materials and methods

Patients

A total of 304 patients treated with lenvatinib monotherapy or lenvatinib plus ICI as first-line treatment for uHCC at Zhongshan Hospital between October 2018 and December 2020 were retrospectively included. The inclusion criteria were: clinically diagnosed as HCC; the Barcelona Clinic Liver Cancer (BCLC) B-C stage; lenvatinib for more than one month; at least one imaginary follow-up; complete baseline information. Tumor differentiation was assessed using the Edmondson grading system and liver function was evaluated using the Child-Pugh scoring system. The BCLC system (14) and The Guidelines of Primary Liver Cancer in China (15) were used to determine the tumor stage. To avoid potential bias, the included patients were randomly assigned to the training cohort and validation cohort at a 2:1 ratio. The research was conducted in accordance with the declaration of Helsinki and ethical approvals were obtained from the ethics committee of Zhongshan hospital (B2020-401).

Treatments and assessments

Lenvatinib (Levima®, Eisai, Tokyo, Japan) was administered orally to most patients at a dose of either 8 mg/day for patients <60 kg or 12 mg/day for patients >60kg. The withdrawal was determined when disease progression, unbearable adverse events, or personal reasons. For the patients receiving lenvatinib plus ICI, immunotherapy began on the first day of lenvatinib and was performed every three weeks afterward. The decision to treat patients with lenvatinib monotherapy or lenvatinib plus ICI was made by the multidisciplinary tumor board. The combination therapy of lenvatinib plus ICI was recommended to patients with uHCC. The patients with contraindication of ICI or refused ICI due to high cost or other personal reasons were treated with lenvatinib monotherapy.

Patients were follow-up via enhanced computed tomography or magnetic resonance imaging in the first month and then every three months after the initiation of treatment. The best tumor response was assessed in the standard of modified Response Evaluation Criteria in Solid Tumors (16). The objective response rate (ORR) was defined as the rate of patients with complete response or partial response and the disease control rate (DCR) was defined as the proportion of complete response, partial response or stable disease. Progression-free survival (PFS) was defined as the interval from initiation of treatment to progression or death from any cause. Overall survival (OS) was defined as the interval from initiation of treatment to death from any cause. The date of the last follow-up was December 1st, 2022.

Statistical analyses

The baseline was compared using the chi-square and Fisher exact test. Continuous variables were summarized as median (interquartile range) and the Wilcoxon rank sum test was applied for comparison. Kaplan-Meier was performed to calculate OS and PFS in different groups and the log-rank test was applied to compute the significance. Cox regression analysis was performed to identify the risk factor of patients with HCC, and the variables with P <0.05 were applied to develop the prognostic model in the training cohort. To evaluate the efficiency of the model, the C-statistics and the time-dependent area under the receiver-operating characteristic curve (AUC) values were calculated. All statistical tests were two-tailed and a P value <0.05 was considered statistically significant. The statistics were performed using R software 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Patient characteristics

A total of 304 patients receiving lenvatinib monotherapy or combination therapy with ICI were analyzed, including 203 and 101 in the training cohort and validation cohort, respectively (Figure 1). Baseline characteristics were shown in Table 1 and there was no significant difference between the training and validation cohort. Most of the patients were Child-Pugh A stage (280, 92.1%) and BCLC C stage (247, 81.2%). There were 183 (60.2%) patients with macrovascular invasion (MVI) and 122 (40.1%) with metastasis. There were 113 (37.2%) patients treated with lenvatinib monotherapy and 191 (62.8%) patients treated with lenvatinib plus ICI, including camrelizumab (60, 31.4%), nivolumab (8, 4.2%), pembrolizumab (29, 15.2%), sintilimab (52, 27.2%), tislelizumab (6, 3.1%), and toripalimab (36, 18.8%). The median treatment cycle of ICI was 9 (range, 1-31). The median duration of follow-up was 15.5 (interquartile range, 8.8-22.7) months. 229 (75.3%) patients died during the follow-up.

Figure 1 | Flowchart of the study. HCC, hepatocellular carcinoma.

Table 1 | Baseline characteristics.

Construction of the PIMET score

To develop a prognostic model for uHCC patients, we first performed univariate and multivariate Cox regression analyses in the training cohort (Table 2). It was identified that vitamin K absence or antagonist-II (PIVKA-II) >600 mAU/mL was an independent risk factor for uHCC patients (HR, 2.05, CI, 1.36-3.08, P=0.001). In addition, metastasis was another independent risk factor (HR, 2.07, CI, 1.49-2.87, P<0.001). Afterward, we constructed a prognostic model using PIVKA-II and metastasis. Since the coefficient values were similar between PIVKA-II (0.72) and metastasis (0.73), we developed an easy-to-use model based on those two variables and assigned 1 point for PIVKA-II>600 mAU/mL and 1 point for the presence of metastasis. The model was named the PIMET (PIVKA-II and METastasis) score. Patients were assigned into the PIMET-low group (0 points, without metastasis and PIVKA-II<600 mAU/mL), PIMET-int group (1 point, with metastasis or PIVKA-II>600 mAU/mL), or PIMET-high group (2 points, with metastasis and PIVKA-II>600 mAU/mL), respectively. In the training cohort, the C-index of the PIMET score was 0.63 and 0.56 for predicting the survival and progression of patients with uHCC. The median OS of patients in the PIMET-low group, PIMET-int group, and PIMET-high group was 24.5, 15.4, and 10.0 months, respectively. There was a significantly different OS among the three groups (P<0.001; Figure 2A). The AUC for 12-, 18-, and 24-month OS was 0.661, 0.682, and 0.744, respectively (Figure 2B). The PFS of patients in the PIMET-low group, PIMET-int group, and PIMET-high group also significantly differed (median PFS: 9.30 vs 6.67 vs 4.83 months; P=0.011; Supplementary Figure 1A). The AUC for 6-,12-, and 24-month PFS was 0.580, 0.626, and 0.730, respectively (Supplementary Figure 1B).

Table 2 | Univariate and multivariate competing risk analyses to identify independent risk factors of OS in patients with unresectable HCC treated with lenvatinib monotherapy or lenvatinib plus ICI.

Figure 2 | The prognostic efficiency of PIMET score for patients receiving lenvatinib monotherapy or lenvatinib plus ICI for unresectable HCC. (A) The Kaplan-Meier survival curves of OS according to PIMET score in the training cohort. (B) The AUCs of PIMET score for predicting 12-, 18-, and 24-month OS in the training cohort. (C) The Kaplan-Meier survival curves of OS according to PIMET score in the validation cohort. (D) The AUCs of PIMET score for predicting 12-, 18-, and 24-month OS in the validation cohort. AUC, area under the receiver-operating characteristic curve; HCC, hepatocellular carcinoma; OS, overall survival.

Validation of the PIMET score

Furthermore, the predictive efficiency of the model was verified and patients in the validation cohort were divided into the PIMET-low group, PIMET-int group, and PIMET-high group accordingly. The C-index for OS and PFS in the validation cohort was 0.67 and 0.61, respectively. The OS of patients with PIMET-low, PIMET-int, and PIMET-high were remarkably different (median OS: 25.8 vs 15.6 vs 8.7 months, P<0.001, Figure 2C) in the validation cohort. The AUC for 12-, 18-, and 24-month OS was 0.724, 0.726, and 0.762, respectively (Figure 2D). There were significantly different PFS among patients with different groups (median PFS: 10.6 vs 7.2 vs 4.9 months, P<0.001, Supplementary Figure 1C). The AUC for 6-, 12-, and 24-month PFS was 0.691, 0.674, and 0.781, respectively (Supplementary Figure 1D).

Subgroup analysis

To identify the predictive value of the model in different subgroup populations, subgroup analyses were applied to the whole cohort. As demonstrated in Figure 3, the PIMET score held predictive value regardless the gender, age, etiology, Eastern Cooperative Oncology Group (ECOG) performance status, tumor size, the presence of MVI, and alpha-fetoprotein (AFP) level.

Figure 3 | The prognostic efficiency of PIMET score in subgroups of patients receiving lenvatinib monotherapy or lenvatinib plus ICI for unresectable HCC. Median OS (Kaplan-Meier method) and hazard ratios (univariable Cox regression) for death comparing PIMET categories (0 vs. 1 and 0 vs. 2 points) in different subgroups in the whole cohort. AFP, alpha-fetoprotein; CI, confidence interval; ECOG, Eastern Cooperative Oncology Group performance status; HBV, hepatitis B virus; HCC, hepatocellular carcinoma; HR, hazard ratio; ICI, immune checkpoint inhibitor; mOS, median overall survival; MVI, macrovascular invasion.

Importantly, we investigated the prognostic efficiency of the PIMET score in patients receiving lenvatinib monotherapy and patients receiving combination therapy with ICI, respectively. In patients with lenvatinib monotherapy, median OS was not reached for PIMET-low (n=22), 13.8 months for PIMET-int (n=61, P<0.001), and 7.6 months for PIMET-high (n=30, P<0.001). In patients with lenvatinib plus ICI, the median OS was 24.0 months for PIMET-low (n=39), 17.7 months for PIMET-int (n=105, P=0.025), and 12.1 months for PIMET-high (n=47, P<0.001).

PIMET score distinguishing patients with different treatment responses

The treatment responses in patients with different PIMET scores were demonstrated in Table 3. The ORR was 49.2% in the PIMET-low group, 30.1% in PIMET-int group, and 28.6% in PIMET-high group. The patients with PIMET-low showed significantly higher ORR compared with those with PIMET-int (P=0.012) and PIMET-high (P=0.021). And the DCR in the three groups was 80.3%, 82.5%, and 62.3%, respectively. Both the patients with PIMET-low (P=0.035) and the patients with PIMET-int (P=0.001) had higher DCR than those with PIMET-high.

Table 3 | Treatment responses in HCC patients with different PIMET scores.

PIMET score identifying patients who benefited from the lenvatinib plus ICI

Furthermore, we investigated the association between the PIMET score and the prognosis of different treatment regimens. In the PIMET-low group, both the OS (P=0.150, Figures 4A) and PFS (P=0.640, Figures 4B) were similar between patients with lenvatinib monotherapy and patients with combination therapy. However, we found that in the PIMET-int group, the combination of lenvatinib with ICI significantly prolonged the OS (median OS: 17.7 vs 13.8 months, P=0.034, Figure 4C) and PFS (median PFS: 8.4 vs 5.0 months, P=0.028, Figure 4D) compared with lenvatinib monotherapy. Meanwhile, the lenvatinib plus ICI also improved outcomes of PIMET-high patients (median OS: 12.1 vs 7.6 months, P=0.031, Figure 4E; median PFS: 5.6 vs 4.0 months, P =0.007, Figure 4F).

Figure 4 | Efficacy of lenvatinib monotherapy and lenvatinib plus ICI in patients with unresectable HCC stratified by PIMET score. (A) The Kaplan-Meier survival curves of OS according to the treatment regimens in the PIMET-low group. (B) The Kaplan-Meier survival curves of PFS according to the treatment regimens in the PIMET-low group. (C) The Kaplan-Meier survival curves of OS according to the treatment regimens in the PIMET-int group. (D) The Kaplan-Meier survival curves of PFS according to the treatment regimens in the PIMET-int group. (E) The Kaplan-Meier survival curves of OS according to the treatment regimens in the PIMET-high group. (F) The Kaplan-Meier survival curves of PFS according to the treatment regimens in the PIMET-high group. HCC, hepatocellular carcinoma; ICI, immune checkpoint inhibitor; OS, overall survival; PFS, progression-free survival.

Discussion

In the present study, we constructed a prognostic model called PIMET score using PIVKA-II and metastasis status to predict the prognosis of patients with uHCC receiving lenvatinib monotherapy or lenvatinib plus ICI. Patients were classified as PIMET-high (with metastasis and PIVKA-II>600 mAU/mL), PIMET-int (with metastasis or PIVKA-II>600 mAU/mL) and PIMET-low (without metastasis and PIVKA-II <600 mAU/mL) accordingly. There were significantly distinct OS and PFS among patients in the three groups, both in the training cohort and validation cohort. The C-index of the PIMET score was 0.63 and 0.67 for survival prediction in the training cohort and validation cohort, respectively, indicating that the PIMET score could serve as a favorable prognostic biomarker for uHCC treated with lenvatinib monotherapy or lenvatinib plus ICI.

There were some established prognostic models for predicting the survival of patients who received systemic treatments for advanced HCCs, including sorafenib, lenvatinib, and immunotherapy (13, 17–20). The PROSASH and PROSASH-II model, which consisted of serum albumin, bilirubin, AFP, macrovascular invasion, extrahepatic spread, and largest tumor size, were built for survival prediction of patients with HCC treated with sorafenib. The C-index of the PROSASH and PROSASH-II models were 0.62 and 0.63 in clinical practice, respectively (17, 18). The CRAFITY score was constructed in patients with HCC undergoing immunotherapy. It comprised CRP and AFP and showed a C-index value of 0.62 in both the training and validation cohorts. However, the predicting model for both lenvatinib monotherapy and lenvatinib plus ICI was lacking. In the present study, we initially developed a general prognostic model for patients with uHCC treated with lenvatinib monotherapy or lenvatinib plus ICI, which achieved satisfactory performance. Importantly, subgroup analysis found that the PIMET score had similar predictive value in patients treated with lenvatinib monotherapy and combination therapy, indicating that the model was suitable for these two kinds of treatments.

Notably, we found that the combination therapy conferred survival benefits for patients with PIMET-int and PIMET-high, instead of those with PIMET-low. Patients with PIMET-int and PIMET-high treated with lenvatinib plus ICI showed significantly better prognoses than those treated with lenvatinib monotherapy. However, the combination therapy didn’t confer survival benefits to patients with PIMET-low. Several studies reported that lenvatinib plus ICI could exert unique immunomodulatory effects and confer better clinical benefits than monotherapy (21, 22). However, the recent LEAP-002 failed to meet the primary endpoint, indicating that not all patients could benefit from combination therapy. Consistently, we also found that compared with lenvatinib monotherapy, lenvatinib plus ICI could improve outcomes of uHCC patients with risk factors, such as PIVKA-II > 600 mAU/mL or the presence of metastasis. For those with PIVKA-II ≤ 600 mAU/mL and without metastasis, the combination of lenvatinib and ICI showed similar survival benefits with lenvatinib monotherapy. One reasonable explanation was that patients with PIMET-low showed favorable outcomes after receiving the lenvatinib monotherapy and thus the efficacy of combined immunotherapy was limited. To our knowledge, the PIMET score is the first prognostic model to guide the clinical treatment decision and facilitate precision medicine for uHCC patients.

In the present study, we identified that PIVKA-II could predict the survival of uHCC patients receiving lenvatinib monotherapy or lenvatinib plus ICI. The prognostic significance of PIVKA-II was controversial according to previous studies. Many literatures showed that the PIVKA-II could predict prognosis and serve as a biomarker for patients who received locoregional therapy (23–25). However, some studies reported that PIVKA-II could not distinguish patients with a high risk of recurrence after curative resection (26, 27). What’s more, there was no report on the application of PIVKA-II in systemic treatment. In the present study, we for the first time, found that PIVKA-II could serve as a strong indicator of prognosis for uHCC.

There were several limitations in the present study. The most prominent is the retrospective design since this is subject to unintentional biases. Thus, a prospective study was needed to validate the prognostic model. Besides, although all included patients were treated with lenvatinib, some patients also applied ICI simultaneously, potentially leading to a selection bias. To account for a potential selection bias, we performed subgroup analyses and successfully validated the PIMET score in different treatment strategies. In addition, the population characteristics of lenvatinib monotherapy and ICI combination therapy might not be equal and thus to further validate the efficacy of lenvatinib plus ICI, a randomized control clinical trial in patients with PIMET-int or PIMET-high was needed. Finally, the most common etiology in our study was the hepatitis B virus. Whether the PIMET score could achieve a similar efficacy for patients with other etiology such as hepatitis C virus needs further study.

In conclusion, we built the PIMET score, which comprised PIVKA-II and metastasis status, to predict the prognosis of patients with uHCC. The PIMET score showed prognostic value in patients receiving lenvatinib monotherapy and lenvatinib plus ICI. Patients with PIMET-int or PIMET-high could benefit from the combination of lenvatinib and ICI. The above results indicated that this model could be widely used in clinical practice and facilitated the decision-making of treatment strategies for uHCC patients.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving human participants were reviewed and approved by the ethics committee of Zhongshan hospital. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author contributions

D-ZG, S-YZ, AH and JZ designed the study and wrote the manuscript. D-ZG, S-YD and AH performed analysis of the data. X-RY, J-YY and Y-PW collected patients’ information and created database. JZ, YC, JF and S-XR interpreted the data and revised the manuscript. All authors edited the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This study was jointly supported by the National Key R&D Program of China (2019YFC1315800, 2019YFC1315802), the State Key Program of National Natural Science of China (81830102), National Natural Science Foundation of China (81772578, 81772551, 81872355 and 82072715), the Shanghai Municipal Health Commission Collaborative Innovation Cluster Project (2019CXJQ02), Shanghai “Rising Stars of Medical Talent” Youth Development Program (Outstanding Youth Medical Talents), the Projects from the Shanghai Science and Technology Commission (19441905000 and 21140900300), the Projects from Science Foundation of Zhong Shan Hospital, Fudan University (2021ZSCX28, 2020ZSLC31), and Shanghai Municipal Key Clinical Specialty.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1141199/full#supplementary-material

Abbreviations

ICI, immune checkpoint inhibitors; HCC, hepatocellular carcinoma; AFP, alpha-fetoprotein; PIVKA-II, protein induced by vitamin K absence or antagonist-II; ORR, objective response rate; DCR, disease control rate; OS, overall survival; PFS, progression-free survival; BCLC, Barcelona Clinic Liver Cancer; AUC, area under the receiver-operating characteristic curve; MVI, macrovascular invasion; ECOG, Eastern Cooperative Oncology Group.

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ORIGINAL RESEARCH
published: 02 March 2023
doi: 10.3389/fimmu.2023.1140201

A novel liver zonation phenotype-associated molecular classification of hepatocellular carcinoma

Tao Zhang ¹, Jian Gu ¹, Xinyi Wang ², Yaoyao Lu ², Kailin Cai ², Huili Li ², Yingli Nie ³*, Xiangdong Chen ¹* and Jiliang Wang ²*

¹ Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, ² Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China, ³ Department of Dermatology, Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School, United States

Reviewed by:
Shrabanti Chowdhury, Icahn School of Medicine at Mount Sinai, United States
Deyong Jia, University of Washington, United States

*Correspondence:
Yingli Nie
yingli_nie@163.com
Xiangdong Chen
xiangdongchen2013@163.com
Jiliang Wang
jiliang_wang@hust.edu.cn

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 08 January 2023

Accepted: 20 February 2023

Published: 02 March 2023

Citation:
Zhang T, Gu J, Wang X, Lu Y, Cai K, Li H, Nie Y, Chen X and Wang J (2023) A novel liver zonation phenotype-associated molecular classification of hepatocellular carcinoma. Front. Immunol. 14:1140201. doi: 10.3389/fimmu.2023.1140201

Background

Liver zonation is a unique phenomenon in which the liver exhibits distinct functions among hepatocytes along the radial axis of the lobule. This phenomenon can cause the sectionalized initiation of several liver diseases, including hepatocellular carcinoma (HCC). However, few studies have explored the zonation features of HCC.

Methods

Four single-cell RNA sequencing datasets were used to identify hepatocyte-specific zonation markers. Integrative analysis was then performed with a training RNA-seq cohort (616 HCC samples) and an external validating microarray cohort (285 HCC samples) from the International Cancer Genome Consortium, The Cancer Genome Atlas, Gene Expression Omnibus, and EMBL’s European Bioinformatics Institute for clustering using non-negative matrix factorization consensus clustering based on zonation genes. Afterward, we evaluated the prognostic value, clinical characteristics, transcriptome and mutation features, immune infiltration, and immunotherapy response of the HCC subclasses.

Results

A total of 94 human hepatocyte-specific zonation markers (39 central markers and 55 portal markers) were identified for the first time. Subsequently, three subgroups of HCC, namely Cluster1, Cluster2, and Cluster3 were identified. Cluster1 exhibited a non-zonational-like signature with the worst prognosis. Cluster2 was intensively associated with a central-like signature and exhibited low immune infiltration and sensitivity toward immune blockade therapy. Cluster3 was intensively correlated with a portal-like signature with the best prognosis. Finally, we identified candidate therapeutic targets and agents for Cluster1 HCC samples.

Conclusion

The current study established a novel HCC classification based on liver zonation signature. By classifying HCC into three clusters with non-zonational-like (Cluster1), central-like (Cluster2), and portal-like (Cluster3) features, this study provided new perspectives on the heterogeneity of HCC and shed new light on delivering precision medicine for HCC patients.

Keywords: hepatocellular carcinoma, liver zonation, immunotherapy, single-cell sequencing, classification, prognosis, drug sensitivity, precision medicine

1 Introduction

Hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide (1). Despite the rapid progression of new diagnostic methods and therapeutic strategies for HCC, its prognosis is still unfavorable due to its high heterogeneity. Therefore, uncovering the molecular mechanisms underlying HCC diversity is essential for the development of targeted and effective therapies.

The liver is a central organ that maintains physiological homeostasis. Liver lobules are the functional units of the liver and are hexagonal in shape. Hepatocytes are organized in a cord-like arrangement along the liver lobule, extending from portal nodes (PN) to the central vein (CV) (2). According to the location of hepatocytes, the liver lobule can be divided into three zones: zone 1 (periportal area) is the region near the portal triad, zone 3 (pericentral zone) is near the CV, and the region between these zones is zone 2 (mid-lobular) (3). The liver shows functional partition along the lobule radial axis, a phenomenon known as “liver zonation”. Hepatocytes in different zonations perform various functions, allowing multiple of functions to proceed in parallel. Most of the liver’s metabolic functions take place in zone 1 hepatocytes, such as β-oxidation, gluconeogenesis, and lipid metabolism. On the contrary, zone 3 hepatocytes play central roles in glycolysis, xenobiotic biotransformation reactions, and glutamine synthesis (4).

Zonation is a dynamic process that plays essential roles in regulating liver disease phenotypes and progression. For example, since fatty acid synthesis and lipid accumulation occur predominantly in zone 3, whereas fatty acid oxidation occurs in zone 1. Patients with non-alcoholic fatty liver disease (NAFLD) often (approximately 37%) exibit perivenous dominant steatosis (5). In addition, patients with HCC exhibit impaired Wnt/β-catenin signaling (6), which stands out as a major regulator of liver zonation, regulating about a third of liver zonated genes (7).

In the past decades, genome-wide analyses have been devoted to deciphering the molecular mechanisms of HCC diversity (8–12). However, few studies have explored the molecular classification of HCC associated with liver zonation characteristics (11). In the present study, we constructed a novel molecular classification associated with liver zonation phenotype, and 3 subgroups of HCC, namely Cluster1, Cluster2, and Cluster3 were identified. We then evaluated the prognostic value, clinical characteristics, transcriptome and mutation features, immune infiltration, and immunotherapy response of the HCC subclasses. Finally, we identified candidate therapeutic targets and agents for Cluster1 HCC samples, which have the worst prognosis among the three clusters.

2 Materials and methods

2.1 Patients and samples

HCC patient cohorts with survival data were retrieved from several databases, including GEO (Gene Expression Omnibus), ICGC (International Cancer Genome Consortium), TCGA (The Cancer Genome Atlas), EMBL-EBI (EMBL’s European Bioinformatics Institute), and CPTAC (Clinical Proteomic Tumor Analysis Consortium). In total, five cohorts were enrolled, including TCGA-Liver Hepatocellular Carcinoma (TCGA-LIHC), ICGC-Liver Cancer-RIKEN-Japan (LIRI-JP), GSE14520, E-TABM-36, and CPTAC-HCC cohorts.

RNA sequencing data (counts) of 374 and 242 HCC human samples with available clinical information were obtained from the TCGA-LIHC cohort (https://xenabrowser.net/datapages/) and the LIRI-JP cohort (https://dcc.icgc.org/projects/LIRI-JP), respectively. The SVA R package was utilized to merge and remove the batch effects of the two RNA-seq datasets to create one metadata cohort using the Sangerbox online tool (13). Additional microarray data of 225 and 60 HCC samples from GSE14520 (https://www.ncbi.nlm.nih.gov/geo/) and E-TABM-36 (https://www.ebi.ac.uk/arrayexpress/files/E-TABM-36) based on the HG-U133A platform, were used for external validation. A metadata cohort was also created by merging the two microarray datasets, and batch effects were removed using the combat function in SVA R package. A boxplot before and after batch effect correction is shown in Supplementary figure S1 .

We downloaded proteogenomics and clinical information from the CPTAC database (https://cptac-data-portal.georgetown.edu/) for 165 HCC tissues and 165 corresponding normal tissues.

Moreover, gene somatic mutation data (MAF files) of the TCGA-LIHC and LIRI-JP cohorts were obtained from TCGA and ICGC databases, respectively. Additionally, copy number data of GISTIC2 for the TCGA-LIHC cohort were accessed from the UCSC Xena (https://xenabrowser.net/datapages/).

Furthermore, the gene expression data and drug sensitivity data (AUC values) of hepatocellular carcinoma cell lines were downloaded from the DepMap database (https://depmap.org/portal/download/).

Additionally, 10x single-nuclei RNA-seq (snRNA-seq) data from 14 mouse and 9 human liver samples were retrieved from GSE192742 (https://www.ncbi.nlm.nih.gov/geo/).

2.2 Identification of hepatocyte-specified liver zonation markers

The procedure for identifying hepatocyte-specified liver zonation markers was shown in Figure 1 . Halpern et al. and Moshe et al. combined single-molecule fluorescence in situ hybridization (smFISH) or fluorescence-activated cell sorting (FACS) of liver zonation surface markers with scRNA-seq to reveal the zonation patterns of hepatocyte gene expression (14, 15). Genes that were overexpressed in the PN area in both Halpern et al. and Moshe et al. cohorts were defined as portal markers. Meanwhile, genes that were overexpressed in the CV area in both Halpern et al.’s and Moshe et al.’s cohorts were defined as central markers.

Figure 1 | Design overview. (A) The procedure of the identification of hepatocyte-specified zonation markers. (B) The flowchart of the study. ICGC, International Cancer Genome Consortium; TCGA, The Cancer Genome Atlas; HCC, Hepatocellular carcinoma.

To identify hepatocyte markers in both mice and humans, we utilized the Seurat R package (16) and applied standard downstream processing to mouse and human liver snRNA-seq data obtained from GSE192742 (17). Firstly, the LogNormalize method was applied for data normalization. Then, the “FindVariableFeatures” function (selection.method = “vst”, nfeatures = 3000) and the “FindIntegrationAnchors” function were using to select features and anchors for further integration. After integration, we eliminated low-quality cells with ≤ 500 genes or a mitochondrial gene ratio ≥ 5%. Finally, 25470 human liver cells and 25335 mouse liver cells that were deemed to be of good quality underwent further analysis. Following data integration and scaling, Seurat’s “RunPCA” function and “FindClusters” function were employed to perform principal component analysis and clustering. Dimensionality reduction was conducted utilizing the “RunUMAP” function, which utilized the top 30 calculated dimensions and a resolution of 0.5. Subsequently, the “FindAllMarkers” function was used to identify marker genes for each cluster (adjusted P < 0.05). Afterward, ToppGeneSuit (https://toppgene.cchmc.org/), CellMarker (http://xteam.xbio.top/CellMarker/), and PanglaoDB (https://panglaodb.se/search.html) databases were employed for the annotation of cell types. Finally, the “FindAllMarkers” function was employed to identify hepatocyte markers by comparing hepatocytes and other cell types (adjusted P < 0.05 and |logFC| > 0.25).

2.3 Identification of HCC subclasses

HCC samples of training and validating cohorts were classified using non-negative matrix factorization (NMF) clustering based on 94 liver zonation markers (18). And then, the prognosis of different subclasses in the training and validation cohorts was evaluated using the Kaplan-Meier log-rank test. Moreover, subclass mapping (SubMap) analysis (19) (Gene Pattern), a method for assessing the comparability of molecular classes between different patient cohorts based on their expression patterns, was then employed to verify whether the subclusters identified in the two cohorts were associated.

2.4 Molecular characteristics of HCC subclasses

The differentially expressed genes (DEGs) among the three clusters were identified using the “limma” package in R with cutoff criteria of |log2 fold change (FC)| > 1 and an adjusted P value < 0.05. Only genes that showed substantial differences in expression across all three comparisons were designated cluster-specific DEGs. Functional enrichment analysis of cluster-specific DEGs was conducted by the Metascape database (https://metascape.org/). P<0.05 was considered statistically significant.

Gene set variation analysis (GSVA) was then employed to estimate the score of the 50 hallmark gene sets that were achieved from the MsigDB database (http://www.gsea-msigdb.org/gsea/msigdb/index.jsp) (20). After that, we used the “ComplexHeatmap” R package to display distinct pathways among the three HCC clusters.

Moreover, Nearest template prediction (NTP) analysis (Gene Pattern modules) was used to predict the correlation between previously published HCC molecular classifications and our classification.

2.5 Estimation of immune infiltration and prediction of the immunotherapy response

To further explore the difference in immune cell infiltration among the clusters, the CIBERSORT algorithm (18) was used to estimate the fraction of 22 immune cell types in the HCC samples using Sangerbox online tool (13). In addition, single-sample GSEA (ssGSEA) was also used to estimate immune infiltration, which computed an enrichment score representing the degree to which genes in 28 immune cell gene sets were coordinately up or downregulated within a single sample (21).

To predict the response of immunotherapy in our subclasses, SubMap analysis (Gene Pattern) was employed to compare the similarity of gene expression profiles between our subclusters and a cohort of melanoma patients with programmed cell death protein-1 (PD1) inhibitor or cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) inhibitor treatment (22).

2.6 Identification of potential drug targets and therapeutic agents for Cluster1 of HCC

The cluster1-specific DEGs with overexpression (log2FC > 1 and adjusted P < 0.05) in HCC tissues and low CERES scores (< -0.5) were defined as potential drug targets. The CERES scores were acquired from the dependency map (DepMap) portal (https://depmap.org/portal/). The CERES score is utilized to evaluate the dependency of the interest gene in a certain cancer cell lines, and a lower score suggests a higher likelihood that the gene is crucial for cell growth and survival of a given cancer cell line.

The Genomics of Drug Sensitivity in Cancer (GDSC) (23), and PRISM (24) databases contain information on drug sensitivity and gene expression profiles of cancer cells, which can be employed to establish a prediction model of drug response. The oncoPredict R package is used for predicting drug response through a ridge regression model to calculate sensitivity scores (low sensitivity score indicates high drug sensitivity) (25).

2.7 Statistical analysis

All computational and statistical analyses were performed using R programming (https://www.r-project.org/) and SPSS 22.0 (IBM Corp., Armonk, NY, USA). To compare two or three groups with normally distributed variables, the unpaired Student’s t-test and one-way ANOVA were utilized, respectively. Kruskal–Wallis tests were employed to compare three groups with non-normal distribution parameters. The chi-square test or Fisher’s exact tests were used for analyzing contingency table variables. Survival analysis was performed using Kaplan–Meier methods with the log-rank test. A two-tailed P value < 0.05 was statistically significant.

3 Results

3.1 Identification of hepatocyte-specified zonation markers

A flowchart was depicted to systematically describe the processes of identifying hepatocyte-specified zonation markers ( Figure 1A ). Firstly, we identified 1467 liver zonation-associated genes that were overexpressed in the CV or PN area in Halpern et al.’s cohort ( Table S1 ). Moreover, 1034 liver zonation-associated genes were screened in Moshe et al.’s cohort ( Table S2 ). Then, 91 central markers and 87 portal markers were identified, which were overexpressed in the CV and PN areas in both Halpern et al.’s and Moshe et al.’s cohorts, respectively ( Figures 2A, B ). Furthermore, we identified 1515 and 1281 mouse and human hepatocyte markers ( Tables S3 , S4 ). Afterward, 105 mouse hepatocyte-specified zonation markers were identified ( Figure 2C ). Finally, after transferring the 105 mouse zonation markers to human homologous genes, we screened 94 human hepatocyte-specified zonation markers, including 39 central markers and 55 portal markers ( Figure 2D , Table S5 ). The expression pattern of the hepatocyte-specified zonation markers in the liver lobule was shown in Figures 2E, F . Moreover, the expression of the hepatocyte-specified zonation markers in different cell types of mouse and human liver was shown in Figures 2G–J .

Figure 2 | Identification of 94 human hepatocyte-specified zonation markers. Venn plots of central markers (A), portal markers (B), mouse hepatocyte-specified zonation markers (C), and human hepatocyte-specified zonation markers (D). Heatmap plots of the expression patterns of the 94 human hepatocyte-specified zonation markers in different areas of liver lobule in Halpern’s (E) and Moshe’s (F) cohorts. UMAP visualization of different cell types from human liver (25470 cells) by snRNA-seq (G). Heatmap plot shows the expression of 94 hepatocyte-specified zonation markers in different cell types from the human liver (H). UMAP visualization of different cell types from the mouse liver (25335 cells) by snRNA-seq (I). Heatmap plot shows the expression of 94 hepatocyte-specified zonation markers in different cell types from mouse liver (J). CV, Central venous; PN, Portal node; UMAP, Uniform Manifold Approximation and Projection; ECs, Endothelial cells; KCs, Kupffer cells; NK cells, Natural killer cells; HSCs, Hepatic stellate cells.

Consistent with previous studies, functional enrichment analysis of these zonation markers indicated that central markers were mainly significantly enriched in processes of fatty acid metabolism, bile secretion, cholestasis, xenobiotic transport, glucose metabolism, and lipid localization. However, portal markers were mainly significantly enriched in processes of amino acid metabolism, complement and coagulation cascades, the urea cycle, regulation of proteolysis, gluconeogenesis, and the response to metal ion ( Table S6 ).

3.2 NMF identifies three clusters in HCC

The procedure of our bioinformatics analyses is shown in Figure 1B . The RNA-seq cohort comprising 616 HCC samples from TCGA-LIHC and ICGC-LIRI-JP were clustered based on the expression profile of 94 liver zonation markers using NMF consensus clustering. As shown in Figure 3A , three distinct clusters were identified in the RNA-seq cohort: Cluster 1 with 123 cases, Cluster 2 with 244 cases, and Cluster 3 with 249 cases. Moreover, a significant prognostic difference was observed among the three clusters in the RNA-seq cohort. Patients in Cluster1 had the worst prognosis, while patients in Cluster3 had the best prognosis (Overall log-rank test P = 1.041×10-6, Cluster1 vs 2 P = 1.189×10-2, Cluster1 vs 3 P = 4.275×10-6, Cluster2 vs 3 P = 4.234×10-3; Figure 3B ). Subsequently, we explored the differences in the expression patterns of liver zonation markers among these clusters. As shown in Figure 3C , Cluster3 had the highest expression level of portal markers, while Cluster1 had the lowest expression level of both central and portal markers. Moreover, we performed GSVA analysis of gene sets based on central and portal markers to depict the zonation characteristics of each cluster. As shown in Figure 3D , consistent with gene expression level, Cluster3 had the highest GSVA score for portal markers, while Cluster1 had the lowest GSVA scores for both central and portal markers.

Figure 3 | Identification of zonation marker-associated HCC subclasses using NMF clustering. (A) Heatmap plot shows the consensus matrix of NMF clustering results using the gene expression data of 94 zonation markers in the RNA-seq cohort (TCGA-LIHC+ICGC-LIRI-JP), colored by three HCC subclasses (Cluster1, Cluster2, and Cluster3). (B) Kaplan-Meier survival analysis of OS of the three clusters. (C) Heatmap plot shows the expression pattern of the zonation markers of the three clusters. (D) The difference of the GSVA scores of the portal and central signatures among the three clusters. Comparison between central and portal by Student’s t-test; Comparison among three clusters by ANOVA -Tukey test. (E, F) Kaplan-Meier survival analysis of OS and RFS of the three clusters from in microarray cohort (GSE14520+E-TABM-36). OS, Overall survival; RFS, Recurrence-free survival; GSVA, Gene set variation analysis.

Subsequently, we conducted another independent analysis on a microarray cohort with 285 HCC samples from GSE14520 and E-TABM-36, the results of which also showed that there were three distinct clusters of HCC: Cluster 1 with 85 cases, Cluster 2 with 106 cases, and Cluster 3 with 94 cases ( Supplementary figure S2A ). Moreover, consistent with the RNA-seq cohort, a significant prognostic difference was observed among the three clusters in the microarray cohort, which patients in Cluster1 had the worst prognosis, and patients in Cluster2 and Cluster3 had better prognoses (Overall Survival: Overall log-rank test P = 1.394×10-4, Cluster1 vs 2 P = 1.394×10-4, Cluster1 vs 3 P = 7.750×10-4, Cluster2 vs 3 P = 5.242×10-1; Figure 3E ; Recurrence Free Survival: Overall log-rank test P = 3.616×10-4, Cluster1 vs 2 P = 2.201×10-3, Cluster1 vs 3 P = 3.367×10-3, Cluster2 vs 3 P = 9.369×10-1; Figure 3F ). Furthermore, as shown in Supplementary figure S2C , consistent with the RNA-seq cohort, Cluster3 in the microarray cohort had the highest expression level and GSVA score for portal markers, while Cluster1 had the lowest expression level and GSVA scores for both central and portal markers.

Finally, a SubMap analysis was performed to determine whether the clusters identified in the two above datasets were associated, and the result showed that Cluster1, Cluster2, and Cluster3 in the RNA-seq cohort were highly associated with corresponding clusters in the microarray cohort (P = 0.009), indicating there were three distinct molecular subclasses of HCC with different gene expression patterns ( Supplementary figure S2D ).

3.3 Correlation of the zonation-associated clusters with clinical characteristics and molecular subclasses of HCC published previously

We then explored the association between HCC-related clinicopathological variables and our classification based on the RNA-seq ( Figure 4A and Table S7 ) and microarray cohorts ( Supplementary figure S3A and Table S8 ). The chi-square and ANOVA tests indicated significant relationships between clinicopathological characteristics and HCC subtypes in the RNA-seq cohort. Favorable survival status (P < 0.001), longer survival time (P < 0.001), lower serum AFP level (P < 0.001), bigger BMI (P=0.027), lack of vascular invasion (P = 0.004), early TNM stage (P < 0.001), early histologic grade (P < 0.001), were associated with Cluster3, while worse survival status, shorter survival time (P < 0.001), smaller BMI, presence of vascular invasion, advanced histologic grade, advanced TNM stage, and high serum AFP level were associated with Cluster1. Similarly, in the microarray cohort, Cluster3 was correlated with favorable survival and recurrence status (P = 0.009, P = 0.045), longer survival and recurrence time (P < 0.001, P < 0.001), lower serum AFP level (P < 0.001), smaller tumor size (P = 0.01), and early TNM and BCLC staging (P = 0.005, P = 0.041). However, worse survival and recurrence status, shorter survival and recurrence time, smaller BMI, presence of vascular invasion, advanced histologic grade, advanced TNM stage, and high serum AFP level were associated with Cluster1.

Figure 4 | Clinical characteristics of zonation marker-associated HCC subclasses in the RNA-seq cohort. (A) Difference of clinical characteristics among the three clusters in the RNA-seq cohort by chi-square test (The comparison of survival time among the three clusters by ANOVA). (B) Correlation of the three clusters with HCC subclasses published previously in RNA-seq cohort by chi-square test.

In addition, we compared our categorization to previously identified HCC molecular subgroups, including Boyault’s classification (G1–G6) (8), Chiang’s classification (Chr 7 polysomy class, CTNNB1 class, Interferon class, Proliferation class, and Unannotated class) (9), Hoshida’s classification (S1, S2, and S3) (10), and Désert’s classification (ECM/STEM, Periportal, and Perivenous type) (11). In the RNA-seq cohort, the Cluster1 subclass was significantly associated with Boyault’s G3, Chiang’s Interferon class, Désert’s ECM/STEM type, and Hoshida’s S1. The Cluster2 subclass was linked to Désert’s Perivenous type and Hoshida’s S2. The Cluster3 subclass was associated with Chiang’s Proliferation class, Désert’s Periportal type, and Hoshida’s S3 ( Figure 4B and Table S7 ). Similarly, in the microarray cohort, Cluster1 was linked to Chiang’s Interferon class, and Désert’s ECM/STEM type. Cluster2 was linked to Boyault’s G5-6, and Désert’s Perivenous type. Cluster3 was enriched in Boyault’s G5-6, Chiang’s Proliferation class, Désert’s Periportal, and Hoshida’s S3 ( Supplementary figure S3B and Table S8 ).

3.4 Transcriptomes of the zonation-associated HCC clusters

Differential analyses were carried out to comprehend the distinctions in the molecular and biological processes among the three HCC subclasses. Significant differences in gene expression were defined as |log2FC| > 1 and the adjusted P-value < 0.05. Only genes that showed substantial differences in expression across all three comparisons were designated cluster-specific DEGs. Finally, 2682 specific DEGs (1868 upregulated and 814 downregulated) for Cluster1, 993 specific DEGs (118 upregulated and 875 downregulated) for Cluster2, and 405 specific DEGs (321 upregulated and 84 downregulated) for Cluster3 were identified ( Table S9 ).

Next, functional enrichment analysis of the cluster-specific DEGs was performed utilizing the Metascape database, and remarkably enriched biological processes or pathways are shown in Table S9 . The specific DEGs of the three clusters showed distinct enrichment of biological processes. Cluster1 was enriched in some differentiation and development-relevant processes. However, multiple metabolism-related biological processes and pathways were significantly enriched for Cluster3. For Cluster2, it was enriched in the processes associated with molecular transport and the WNT signaling pathway ( Table S10 ).

Moreover, to further investigate the molecular characteristics of the zonation marker-related subclasses, hallmark gene sets were chosen and quantified using the GSVA algorithm. Hallmark gene sets consist of eight process categories, including development, immune, signaling, cellular component, pathway, metabolic, DNA damage, and proliferation, which effectively condense the majority of the pertinent data from the initial sets and offer more precise and succinct inputs for gene set enrichment analysis (26). The result was presented in a heatmap ( Figure 5A ). Cluster1 exhibited high expression for proliferation relevant processes (e.g., G2M checkpoint, Mitotic spindle, and E2F targets), low expression for metabolic relevant processes (e.g., fatty acid metabolism, bile acid metabolism, and xenobiotic metabolism) ( Figure 5B ). Cluster2 exhibited low expression for immune (e.g., IL6-JAK-STAT3 signaling, Allograft rejection, and inflammatory response), development (e.g., Angiogenesis, Myogenesis, and Epithelial-mesenchymal transition), and signaling (e.g., IL2-STAT5 signaling, TNFA signaling via NFKB, TGFβ signaling, KRAS signaling up, Notch signaling, and Hedgehog signaling) relevant processes ( Figure 5C ). Cluster3 exhibited high expression for immune (e.g., Coagulation, Complement, Interferon-α response, and IL6-JAK-STAT3 signaling) and metabolic (e.g., Xenobiotic metabolism) relevant processes, low expression for proliferation relevant processes (e.g., G2M checkpoint, MYC targets, Mitotic spindle, and E2F targets) ( Figure 5D ).

Figure 5 | Difference of 50 hallmark gene sets among the three clusters. (A) Heatmap plot shows the GSVA score of 50 hallmark gene sets of the three clusters in RNA-seq cohort. The hyperactive and hypoactive processes or pathways in Cluster1 (B), Cluster2 (C), and Cluster3 (D). Red represents hyperactive process or pathway and blue represents hypoactive process or pathway.

3.5 The gene mutation profile of the zonation-associated HCC clusters

To further investigate the different patterns of gene mutations among HCC clusters, somatic mutation data from the RNA-seq cohort was analyzed. The top 25 genes with the highest mutation rates in all three clusters were shown in Figures 6A–C . In Cluster1, TP53 exhibited the highest somatic mutation rate (42%), followed by TTN (27%), MUC16 (17%), PCLO (14%), RYR2 (13%), and LRP1B (12%). In Cluster2, CTNNB1 exhibited the highest somatic mutation rate (49%), followed by TP53 (29%), TTN (16%), MUC16 (14%), ALB (14%), ARID1A (11%), and PCLO (11%). For Cluster3, TTN exhibited the highest somatic mutation rate (26%), followed by TP53 (23%), ALB (13%), APOB (13%), PCLO (12%), CTNNB1 (10%), and MUC16 (10%).

Figure 6 | Gene mutation profile of zonation marker associated HCC clusters. Oncoplots of the top 25 somatic mutation genes in Cluster1 (A), Cluster2 (B), and Cluster3 (C). Red represents HCC driver genes. Oncoplot of the top 20 somatic mutations of HCC driver genes (D). Difference of TMB of HCC driver genes among the three clusters by Kruskal–Wallis test (E). TMB, Tumor mutation burden.

A previous study identified 161 HCC driver genes that may play critical roles in the treatment of HCC (27). Therefore, we further explored the mutation profile of these driver genes. The top 20 driver genes with the highest mutation rates in all three clusters were shown in Figure 6D . TP53 exhibited the highest somatic mutation rate (29%), followed by CTNNB1 (25%), ALB (12%), APOB (10%), ARID1A (8%), ARID2 (8%), and AXIN1 (6%). Moreover, we compared the tumor mutation burden (TMB) of HCC driver genes among the three clusters. The results showed that Cluster2 exhibited a higher TMB level than the other two clusters ( Figure 6E ).

3.6 Correlation of the zonation-associated HCC clusters with immune infiltration and immunotherapy response

Given the significant differences in immune processes among clusters, immune infiltration was examined to characterize their immunological landscape. As shown in Figure 7A , the abundance of immune cell types was calculated using CIBERSORT and ssGSEA algorithms. As shown in Figure 7B , compared with the other two subclasses, Cluster2 presented the lowest abundance of 24 immune cell types, including B cells memory, macrophages M2, neutrophils, activated B cells, central memory CD4 T cell, central memory CD8 T cell, effector memory CD4 T cell, effector memory CD8 T cell, immature B cell, T follicular helper cell, regulatory T cell, Type 1/2/17 helper cell, activated dendritic cell, CD56dim natural killer cell, immature natural killer cell, macrophage, mast cell, MDSC, monocyte, natural killer T cell, natural killer cell, and Plasmacytoid dendritic cell. We further investigated the differences in the expression of 47 immune checkpoint genes among the three subclasses. Consistent with immune infiltration, Cluster2 exhibited the lowest expression for 44 immune checkpoint genes (except for ICOS, NRP1, and TNFSF14) compared to Cluster1 and Cluster3 ( Figure 7C ).

Figure 7 | Immune cells infiltration landscape and immune checkpoint genes expression profile of three clusters. (A) Heatmap plot shows the abundance of immune cell infiltration in three clusters by CIBERSORT and ssGSEA. (B) Difference of the abundance of immune cells infiltration among different clusters by ANOVA. (C) Difference of the level of immune checkpoint genes among different clusters by ANOVA. *P<0.05; **P<0.01; ***P<0.001. (D) The SubMap matrix shows Cluster1 may be more sensitive to the CTLA-4 inhibitor (nominal P = 0.05) and Cluster3 may be more sensitive to the PD-1 inhibitor (nominal P = 0.003).

Different immune infiltration and immune checkpoint gene expression patterns among HCC clusters indicate that the immunotherapy response among the clusters may be different. Therefore, we compared the expression profiles of the three clusters (Cluster1, Cluster2, and Cluster3) with another published melanoma cohort containing 47 patients who received PD1 inhibitor or CTLA-4 inhibitor using SubMap analysis. As shown in Figure 7D , the expression profile of Cluster2 was correlated with the CTLA4 response group (P = 0.05), while Cluster3 was associated with the PD1 response group (P = 0.003), suggesting that patients within the Cluster1 group were more likely to respond to the anti-CTLA4 therapy and Cluster3 group was more likely to respond to anti-PD1 therapy.

3.7 Identification of potential drug targets and candidate therapeutic agents for Cluster1

Since patients in Cluster1 have the worst prognosis, we further identified the potential drug targets for Cluster1. The cluster1-specific DEGs with overexpression (log2FC > 1 and adjusted P < 0.05) in HCC tissues and low CERES score (< -0.5) were defined as potential drug targets. Eventually, a total of 34 potential drug targets were identified ( Figure 8A , Table S11 ). Among them, drugs (compounds) that target AURKB, BIRC5, KIF11, PLK1, PLK4, RAD51, TOP2A, TTK, and TUBB3 were identified ( Figure 8A , Table S11 ).

Figure 8 | Identification of potential drug targets and candidate agents for Cluster1 HCC patients. (A) The 34 potential drug targets identified for Cluster1 HCC patients. (B and C) The sensitivity score, phase, and targets of candidate agents (eleven PRISM-derived compounds and seven GDSC-derived compounds) for Cluster1 HCC patients. **P<0.01; ***P<0.001.

To further identify the potential therapeutic agents for Cluster1, the oncoPredict R package was used to calculate the sensitivity score of the drugs (compounds) for potential drug targets of Cluster1 based on the drug sensitivity and gene expression profiles of HCC cell lines from GDSC and PRISM databases. As shown in Figures 8B, C , eleven PRISM-derived compounds (including Teniposide, K-858, TAK-901, Dexrazoxane, Mitoxantrone, AT-9283, Axitinib, Podophyllotoxin, Amonafide, Amsacrine, and Idarubicin) and six GDSC-derived compounds (including Axitinib, Doxorubicin, Etoposide, GSK1070916, YM−155, and Alisertib) were found to be more sensitive in Cluster1 HCC patients.

4 Discussion

Liver zonation is a unique phenomenon that exhibits a distinct division of functions among hepatocytes along the lobule radial axis, optimizing overall liver function, and playing a central role in regulating liver disease phenotypes and progression, including HCC (28). Therefore, obtaining a better understanding of the characteristics of liver zonation in HCC may help us unveil the mechanisms of HCC heterogeneity and develop more effective therapeutics for HCC. With the breakthrough of RNA- sequencing technology, scRNA-seq coupled with spatial mapping has demonstrated previously unknown molecular patterns of hepatocytes, shedding new light on the functional features of hepatocytes across different zones of the liver lobule in both humans and mice, facilitating a better understanding of the form and regulation of zonation (29).

In the present study, we identified 94 hepatocyte-specified zonation markers (39 central markers and 55 portal markers) by combining four scRNA-seq cohorts for the first time. Based on the zonation markers, we identified three HCC subclasses. Cluster1 was barely involved in zonation-related signature, with a bad prognosis, high recurrence, high AFP level, and advanced TNM stage and histologic grade. Cluster2 was associated with a central signature, with a middle prognosis, AFP level, TNM stage, and histologic grade. Cluster3 exhibited a portal-associated signature, with favorable prognosis, low recurrence, low AFP level, and early TNM stage and histologic grade. In general, this study investigated the zonation characteristics of HCC, and identified three types of HCC with non-zonational-like (Cluster1), central-like (Cluster2), and portal-like (Cluster3) features, respectively.

Moreover, the transcriptome and mutation features, immune infiltration, and immunotherapy response of the subclasses were investigated. Cluster1 (non-zonational-like type) was mainly enriched in differentiation and development-relevant processes, with a high rate of TP53 mutation (42%), a high level of immune cell infiltration, a high expression level of immune checkpoint genes, and may be more sensitive to the CTLA4 inhibitor. Moreover, Cluster2 (central-like type) was mainly enriched in the processes associated with molecular transport and WNT signaling pathway, with a high rate of CTNNB1 mutation (49%), a low level of immune cell infiltration, and a low expression level of immune checkpoint genes. Cluster3 (portal-like type) was enriched in numerous metabolism-associated biological processes and pathways, with low rates of both TP53 (26%) and CTNNB1 (10%), a high level of immune cell infiltration, a high expression level of immune checkpoint genes, and may be more sensitive to the PD1 inhibitor.

Furthermore, we compared the correlation between our clusters and previously published HCC subclasses, which provided us with abundant characteristic information related to our novel zonation-associated clusters, leading to a deeper understanding of our clusters. The results from both RNA-seq and microarray cohorts demonstrated that Cluster1 was associated with the HCC type of Chiang’s Interferon class and Désert’s ECM/STEM type. Chiang’s Interferon class is featured by the overexpression of several interferon-stimulated genes, with a low mutation rate of CTNNB1 and low expression of IGF2 and CTNNB1 target genes (9). Désert’s ECM/STEM type is the collective name for the ECM type and STEM type. ECM type is characterized by ECM modeling, integrin signaling, and epithelial-mesenchymal transition. STEM type is typified by up-regulation of cell cycle progression and p53 mutation. ECM/STEM-type HCCs share high tumor cell proliferation and a bad prognosis (11). Cluster2 was linked to Désert’s Perivenous type. Désert’s Perivenous type is defined by a high level of perivenous hepatocyte signatures, such as fatty acid and bile salt metabolism, with a high rate of CTNNB1 mutations (11). Cluster3 was associated with Chiang’s Proliferation class, Désert’s Periportal type, and Hoshida’s S3. Chiang’s Proliferation class is characterized by high proliferation, chromosomal instability, activation of IGF and Akt/mTOR signaling, and reduced frequencies of CTNNB1 mutation (9). Désert’s Periportal type is featured by the enrichment of differentiated periportal hepatocyte signatures (gluconeogenesis, amino acid catabolism, hepatocyte nuclear factor 4A (HNF4A) induced genes) and TP53 mutation rates, with good prognosis, low recurrence, early-stage tumors by BCLC, TNM staging systems, and no macrovascular invasion (11). Hoshida’s S3 is notable for a well-differentiated hepatocyte signature with a good prognosis, and preserved TP53 function (10). In general, the clusters we constructed validated the findings of previously published HCC subclasses but also preserved their own characteristics.

HCC tumors are highly heterogeneous among individuals, and finding targeted therapeutic strategies for specific groups is of vital importance to maximize the therapeutic effect. In the present study, a total of 34 potential drug targets and 16 agents for Cluster1 HCC patients were identified. Among the potential drug targets, AURKB, BIRC5, KIF11, PLK1, PLK4, RAD51, TOP2A, TTK, and TUBB3 have targeted agents. Numerous studies have demonstrated the vital oncogenic role of AURKB (30), BIRC5 (31), KIF11 (32), PLK1 (33), PLK4 (34), RAD51 (35), TOP2A (36), TTK (37), and TUBB3 (38) in various cancers, including HCC. Therefore, multiple compounds have been designed for these therapeutic targets ( Table S11 ). Among these compounds, Teniposide, K-858, TAK-901, Dexrazoxane, Mitoxantrone, AT-9283, Axitinib, Podophyllotoxin, Amonafide, Amsacrine, Idarubicin, Doxorubicin, Etoposide, GSK1070916, YM−155, and Alisertib were found to be more sensitive for Cluster1 HCC patients.

Teniposide, Dexrazoxane, Mitoxantrone, Podophyllotoxin, Amsacrine, Doxorubicin, and Etoposide are topoisomerase inhibitors that have been widely employed as chemotherapy in multiple cancer therapies. Amonafide is also a topoisomerase II inhibitor and DNA intercalator that has been found to have marked antineoplastic efficacy in preclinical models of cancer. Phase I and II studies revealed that Amonafide might be a promising drug for treating older patients, including those with multidrug-resistant, cytogenetically unfavorable secondary and treatment-associated acute myeloid leukemia (AML) (39).

TAK-901, AT-9283, GSK1070916, and Alisertib are Aurora kinase inhibitors. Human Aurora kinases, including Aurora kinase A (AURKA), B (AURKB), and C (AURKC), play critical roles in monitoring the mitotic checkpoint, bipolar mitotic spindle creation, and centrosome alignment, and also participate in regulating the separation of centrosome, bio-orientation of chromosomes, and cytokinesis. AURKB is a key regulator of mitosis and centrosome through polymerizing microfilaments and regulating chromatid segregation (40). Preclinical studies and Phase I/II/III trials have shown favorable pharmacokinetic properties and markedly antitumor effects of these agents (41–44).

K-858 is an inhibitor of the mitotic kinesin KIF11 (also known as Eg5). K-858 induces cell mitotic arrest with the formation of monopolar spindles by blocking centrosome separation, and activating the spindle checkpoint (45). K-858 has shown potent anti-proliferative and pro-apoptotic effects in multiple types of cancer cell lines (46, 47).

Axitinib is a potent and specific inhibitor of tyrosine kinase, specifically with high affinity for VEGFRs 1, 2, and 3, which was approved by the FDA for use in renal cell carcinoma in 2012 (48). In addition, Axitinib was also found to inhibit PLK4 with an IC₅₀ value of 4.2 nM (49). Clinical trials have been performed to treat HCC patients with Axitinib. In a phase II trial, second-line treatment with Axitinib combined with the best supportive care led to a remarkably longer PFS and a higher clinical benefit rate than patients with only best supportive care in advanced HCC (50). Moreover, another multicenter phase II trial in advanced HCC revealed that second-line Axitinib treatment resulted in a 62.2% disease control rate and a 6.7% response rate for advanced HCC after failing the first-line sorafenib therapy (51).

YM-155 is an imidazolium-based compound that has selective activity against BIRC5 (also known as Survivin) (52). Several phase I/II clinical trials have demonstrated that YM-155 has a safe profile and anti-tumor capacity in multiple types of tumors (53, 54). Preclinical studies found that YM155 substantially suppressed the proliferation and induced cell cycle arrest and apoptosis of HCC cell lines. Moreover, in a mouse model using patient-derived HCC xenografts with overexpressed BIRC5 and p-BIRC5, YM155 exhibited stronger anti-proliferative efficacy than sorafenib (55).

5 Conclusion

In conclusion, the current study established a novel HCC classification based on liver zonation signature. By classifying HCC into three clusters with non-zonational-like (Cluster1), central-like (Cluster2), and portal-like (Cluster3) features, this study shed new lights on the heterogeneity of HCC. Cluster3 was intensively correlated with portal-like signature with a good prognosis. Cluster2 was intensively associated with a central-like signature and exhibited low immune infiltration and sensitivity towards immune blockade therapy. Cluster1 exhibited a non-zonational-like signature with the worst prognosis. Moreover, we identified potential drug targets and agents for Cluster1 HCC, which provided new insights into delivering precision medicine for HCC patients and shed new light on improving the therapeutic effect of anti-tumor drugs by selecting potentially sensitive patients.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found within the article/ Supplementary materials .

Author contributions

TZ, and YN designed this work. JG, XW, and YL integrated and analyzed the data. HL and KC wrote this manuscript. TZ, YN, XC, and JW critical revision of the manuscript for important intellectual content, obtaining funding, and supervision. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the National Natural Science Foundation of China (NO. 82200972 to TZ, NO. 82070647 to JW, and 82071251 to XC).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1140201/full#supplementary-material

Supplementary Figure 1 | The Venn plot shows the common genes for TCGA-LIHC and ICGC-LIRI-JP cohorts (A). The box plot shows the gene expression before and after batch effect correction of the RNA-seq cohort (B). The Venn plot shows the common genes for GSE14520 and E-TABM-36 cohorts (C). The box plot shows the gene expression before and after batch effect correction of the microarray cohort (D)

Supplementary Figure 2 | Identification of zonation marker-associated HCC subclasses using NMF clustering. (A) Heatmap plot shows the consensus matrix of NMF clustering results using the gene expression data of 94 zonation markers in the microarray cohort (GSE14520+E-TABM-36), colored by three HCC subclasses (Cluster1, Cluster2, and Cluster3). (B) Heatmap plot shows the expression pattern of the zonation markers of the three clusters. (C) The difference of the GSVA scores of the portal and central signatures among the three clusters. Comparison between central and portal by Student’s t-test; Comparison among three clusters by ANOVA -Tukey test. (D) The SubMap matrix shows a significant correlation between HCC classification from RNA-seq and microarray cohorts. GSVA: Gene set variation analysis.

Supplementary Figure 3 | Clinical characteristics of zonation marker associated HCC subclasses in the microarray cohort. (A) Difference of clinical characteristics among the three clusters in microarray cohort by chi-square test (The comparison of survival time among the three clusters by ANOVA). (B) Correlation of the three clusters with HCC subclasses published previously in microarray cohort by chi-square test.

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Copyright © 2023 Zhang, Gu, Wang, Lu, Cai, Li, Nie, Chen and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

REVIEW
published: 17 March 2023
doi: 10.3389/fonc.2023.1117642

SMC4, a novel tumor prognostic marker and potential tumor therapeutic target

Zonglei Zhao ¹, Xixiu Wang ², Yan Ding ¹, Xuefeng Cao ¹* and Xingyuan Zhang ¹

¹ Department of Hepatobiliary Surgery, Binzhou Medical University Hospital, Binzhou, Shandong, China, ² Department of Cardiovascular Diseases, Binzhou Medical University Hospital, Binzhou, Shandong, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Cheng Wei, Tianjin Medical University General Hospital, China
Shangze Li, Chongqing University, China

*Correspondence:
Xuefeng Cao
byfygdwk_cxf@163.com

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Oncology

Received: 06 December 2022

Accepted: 31 January 2023

Published: 17 March 2023

Citation:
Zhao Z, Wang X, Ding Y, Cao X and Zhang X (2023) SMC4, a novel tumor prognostic marker and potential tumor therapeutic target. Front. Oncol. 13:1117642. doi: 10.3389/fonc.2023.1117642

The structural maintenance of chromosome 4 (SMC4) is a member of the ATPase family of chromosomes. The most widely reported function of SMC4, as well as the remaining subunits of whole condensin complexes, is compression and dissociation of sister chromatids, DNA damage repair, DNA recombination, and pervasive transcription of the genome. Studies have also shown that SMC4 plays an exceedingly essential role in the division cycle of embryonic cells, such as RNA splicing, DNA metabolic process, cell adhesion, and extracellular matrix. On the other hand, SMC4 is also a positive regulator of the inflammatory innate immune response, while excessive innate immune responses not only disrupt immune homeostasis and may lead to autoimmune diseases, but even cancer. To further understand the expression and prognostic value of SMC4 in tumors, we provide an in-depth review of the literature and several bioinformatic databases, for example, The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Clinical Proteomic Tumor Analysis Consortium (CPTAC), The Human Protein Atlas and Kaplan Meier plotter tools, illustrating that SMC4 plays a vital role in the occurrence and development of tumors, and high expression of SMC4 seems to consistently predict worse overall survival. In conclusion, we present this review which introduces the structure, biological function of SMC4, and its correlation with the tumor in detail; it might provide new insight into a novel tumor prognostic marker and potential tumor therapeutic target.

Keywords: SMC4, tumor, prognosis, biomarker, targeted therapy, overall survival

1 Introduction

Structural maintenance of chromosome 4 (SMC4), a member of the chromosome ATPase family, is a class of proteins evolutionarily conserved from bacteria to humans (1). It forms a chromosome pentamer complex with homologous protein structural maintenance of chromosome 2 (SMC2) and other non-structural maintenance of chromosome (SMC) subunits, called condensin. The pentamer complex plays an essential role in regulating chromatin dynamics, sister chromatid condensation, chromatin condensation, DNA replication, DNA repair, and transcription (2). Furthermore, SMC4 is also a vital eukaryotic SMC protein related to cell survival and death. Numerous pieces of evidence suggest that SMC4 can promote cell proliferation in various tumors by regulating the division cycle of embryonic cells (3). In addition, recent research has also revealed that SMC4 participates in inflammatory innate immune responses through enhancing TLR and virus-triggered activation of NF-κB and IRF3, leading to the production of proinflammatory cytokines and IFN-β. Excessive innate immune responses not only disrupt immune homeostasis and may lead to autoimmune diseases but even cancer, where the development mechanism may be strongly related to the alteration of the tumor microenvironment (TME) (4), which consists mainly of fibroblasts, adipocytes, adaptive, endothelial cells, neurons, and innate immune cells, as well as its non-cellular components, such as cytokines, growth factors, chemokines, and extracellular vesicles. Furthermore, our bioinformatics analysis has also shown that SMC4 has changed gene and protein expression levels in various tumors and plays a critical regulatory role in the occurrence, development, and prognosis of tumors. This paper review introduces the structure and biological function of SMC4 and its correlation with tumors. It is surprising to find that SMC4 may be a novel tumor prognostic marker and potential tumor therapeutic target.

2 The structure of SMC4

As a member of the SMC family, SMC4 protein is evolutionarily conserved. SMC proteins from bacteria to humans have basic structures and contain five different domains, and SMC4 is no exception. SMC4 protein is basically made up of 1000 to 1400 amino acids, with a molecular weight of 110-170 kDa. It is made up of a moderately conserved hinge region, highly conserved N-and C-terminal domains, and a coiled helix domain with N-terminal and C-terminal connections respectively (5, 6). Among them, the C-terminal domain contains about 150 amino acids, including Walker B motif, and the N-terminal domain contains about 160 amino acids, with Walker a motif. Both Walker A and Walker B motifs are a conserved sequence of NTP binding amino acids and were discovered and described first by J.E. Walker (7). In addition, SMC4 molecules usually reverse fold in the hinge domain, making its two double helix domains parallel to each other. Then the C-terminal and N-terminal are close to each other to form a rod-shaped ATPase domain, which is usually similar to the ATP binding cassette (ABC) domain ( Figure 1 ) (8).

Figure 1 | Structure diagram of SMC4, which contains mainly five different domains. The green circle represents the N-terminal, which contains Walker A the blue circle represents the C-terminal, which contains Walker B the purple circle represents the hinge; the helix on the left represents the N-terminal coiled helix domain; the helix on the right represents the C-terminal coiled helix domain. The C-terminal and N-terminal are close to each other to form the ATPase domain.

3 Biological function of SMC4

SMC4 protein is one of the members of the ATPase superfamily. It is involved in forming the core subunit of the condensin complex by binding to the SMC2 monomer in the hinge region to form a V-shaped structure (6). In addition to the SMC2 and SMC4 core subunits, condensin I complexes also contain three non-SMC subunits: non-SMC condensin I complex subunit H (NCAPH), non-SMC condensin I complex subunit D2 (NCAPD2) and non-SMC condensin I complex subunit G (NCAPG) ( Figure 2 ), while in condensin II these are NCAPH2, NCAPG2 and NCAPD3 ( Figure 3 ) (9). The condensin complex can promote the correct compression and dissociation of sister chromatids through its intramolecular bridging effect in an ATP hydrolysis-dependent manner. Meanwhile, the SMC4 protein plays an essential role in regulating the ATPase domain (8). In contrast to SMC2 protein, SMC4 protein has a high affinity for APT enzymes; therefore, it can be regarded as a “switch” of the condensin complex (9). In addition, a growing body of research showed that SMC4 plays an exceedingly essential role in the division cycle of embryonic cells, for example, RNA splicing, DNA metabolic process, cell adhesion, and extracellular matrix (10). Therefore, SMC4 protein can play a vital role in promoting cell proliferation (11). Furthermore, Wang Q et al. found that SMC4 promotes an inflammatory innate immune response, which promotes innate activation of NF-κB and IRF3 by enhancing the transcription of NEMO, and subsequently inducing the activation of proinflammatory cytokines and type I interferon genes (12).

Figure 2 | Structure diagram of condensin I, which contains mainly condensin, NCAPH, NCAPD2, and NCAPG. The green part on the left represents SCM4, and the red part on the right represents SMC2, which together form condensin; the purple protein in the middle represents NCAPH, the yellow protein in the lower left represents NCAPG, and the brown protein in the lower right represents NCAPD2.

Figure 3 | Structure diagram of condensin II, which contains mainly condensin, NCAPH2, NCAPD3, and NCAPG2. The green part on the left represents SCM4, and the red part on the right represents SMC2, which together form condensin; the yellow protein in the middle represents NCAPH2, the green protein on the lower left represents NCAPG2, and the pink protein on the lower right represents NCAPD3.

4 Correlation between SMC4 and various tumors (How to infer that it may be a novel tumor prognostic marker and potential tumor therapeutic target)

As we all know, tumors, especially malignant tumors, have biological characteristics, including proliferation and abnormal cell differentiation, uncontrolled growth, decreased apoptosis, easy invasion, and metastasis. Their occurrence and development is an enormously complex process of multi factors and multi-steps. Genomic instability refers to the increased tendency to accrue genomic alterations, which drives heterogeneity and is a hallmark of cancer. At the same time, an aberration in the DNA repair mechanisms may result in genomic and chromosomal instability and the accumulation of mutations. The activation of oncogenes or the inactivation of tumor suppressor genes is a dire consequence of genomic instability that even may bring the cells into a cancerous phenotype (13). SMC4 protein, as a sensitive switch of the condensin complex, which combines with SMC2 monomer to form a V-shaped structure, and then combines with other non-SMC subunits to form the condensin complex, plays a highly crucial role in the process, including DNA repair and correct compression and dissociation of sister chromatids; therefore SMC4 is inextricably related to the occurrence and development of tumor. Moreover, a study reported that tumors could be conceived of particular “organs” because various signaling, transcriptional and metabolic pathways are shared between organogenesis and malignant tumors (10). A growing number of studies have shown that SMC4 also plays an exceedingly crucial role in the division cycle of embryonic cells, such as RNA splicing, DNA metabolic processes, cell adhesion, and extracellular matrix. Likewise, tumor cells with high SMC4 expression may mimic the gene expression patterns of embryonic cells to enhance their competitive advantage over normal somatic cells (11, 14). On the other hand, recent research has also revealed that SMC4 participates in inflammatory innate immune responses through enhancing TLR and virus-triggered activation of NF-κB and IRF3, leading to the production of proinflammatory cytokines and IFN-β (12). An excessive innate immune response not only disrupts the immune balance, leading to autoimmune diseases but also even results in cancer. The development mechanism may be strongly related to the fact that immune cell infiltration and secretion of cytokine promote the formation of the TME, thus facilitating tumor progression, metastasis, and therapeutic resistance (1). In order to further a more comprehensive understanding of SMC4 and the potential relationship between SMC4 and tumors, from The Cancer Genome Atlas (TCGA) database, the pan-cancer analysis showed that the expression of SMC4 mRNA was highly expressed in most types of cancers, such as breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), glioblastoma multiforme (GBM), liver hepatocellular carcinoma (LIHC), pancreatic adenocarcinoma (PAAD), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and rectum adenocarcinoma (READ) ( Figure 4 ). A large number of studies also demonstrate that patients with high SMC4 expression tend to have a poor prognosis (14–47). In summary, all of these indicate that SMC4 may be enormously associated with the occurrence, development, evolution, and prognosis of tumors and even become a new tumor prognostic marker and potential therapeutic target (10, 13).

Figure 4 | Expression levels of SMC4 mRNA in different types of tumors in TCGA database. Adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS), uveal melanoma (UVM); TCGA, The Cancer Genome Atlas. ns, p > 0.05; *p < 0.05; **p < 0.01; ***p < 0.001.

4.1 SMC4 and primary liver cancer

The occurrence and development of primary liver cancer is an exceedingly complex, long-term, multi-factor, multi-step gradual evolution process. Searching for new tumor markers and exploring the intervention targets of tumor-targeted therapy has become a hot research topic at home and abroad in recent years (15). In vitro, the results of the cell invasion experiment, scratch repair experiment, and cell clone formation experiment of liver cancer cells confirmed that the migration rate of liver cancer cells transfected with SMC4 interference vector group decreased significantly, and the proliferation rate decreased from 100% to 73.4%, suggesting that SMC4 plays an exceedingly critical role in promoting the proliferation and invasion of liver cancer cells (16). Its mechanism may be related to miR-219-SMC4-JAK2/STAT3 (tyr705), a new regulatory pathway in primary liver cancer (17). Moreover, research also found that under hypoxic culture, through HIF-1-miR-219-SMC4 regulatory pathway suppression of SMC4 could inhibit the proliferation rate of hepatocellular carcinoma (HCC) cells through inducing G1 phase arrest and affecting cell migration ability by affecting the process of epithelial-to-mesenchymal transition (EMT) (18). Through real-time polymerase chain reaction (PCR), Western blot, immunohistochemistry (IHC), and other experimental results, SMC4 expression has been confirmed up-regulated in HCC tissues, and the expression level of SMC4 in HCC tissues was associated with tumor size, TNM stage, cell differentiation, and vascular invasion, indicating that the expression level of SMC4 is related to the poor prognosis of patients with hepatoma (16). Furthermore, GO analysis indicated that SMC4 had significant regulation on serine hydrolase activity, serine-type peptidase activity, serine-type endopeptidase activity, phosphatidylinositol-3-phosphatase activity, anchored component of membrane, contractile ring, photoreceptor outer segment membrane, GABA-A receptor complex, cell cortex and benzodiazepine receptor activity ( Figures 5A, B ). The correlation between SMC4 expression and the prognosis of patients with HCC was calculated using the Kaplan Meier method. HCC patients were divided into high and low expression groups based on the SMC4 expression median value. Compared with the low SMC4 expression group, both the overall survival (OS) and disease-specific survival (DSS) of the high SMC4 expression group exhibited a significantly worse prognosis (HR = 1.55 (1.09–2.20), p = 0.014) and DSS (HR = 1.85 (1.18–2.91), p = 0.008) ( Figures 5C, D ). These results indicate that SMC4 genes could significantly enrich our understanding of the development and recurrence of HCC and could be therapeutic targets for HCC treatment (19).

Figure 5 | The enrichment analysis and prognostic value of SMC4 in HCC. (A, B) Biological process enrichment related to SMC4-related genes in the TCGA database. (C, D) The prognostic value of SMC4 in OS and DSS of HCC in the TCGA database. TCGA, The Cancer Genome Atlas; OS, overall survival; DSS, disease-specific survival (DSS). CC, cellular component; MF, molecular function; HR, Hazard Ratio.

4.2 SMC4 and lung cancer

Lung cancer, which is known as the most lethal cancer, has the highest incidence and mortality among all kinds of tumors, and its incidence rate is still rising. A 2013 study on genome-scale co-expression network found that SMC4 gene plays an enormously crucial role in the progression cell cycle and is involved in the occurrence and development of lung adenocarcinoma (20). Subsequently, Zhang C et al. concluded that SMC4 plays a crucial role in lung development and tumor formation through co-expression network analysis and gene cluster penetration clustering, which is mainly reflected in cell cycle, cell adhesion, and RNA processing (14). Bioinformatics analysis manifested that the unpaired (p < 0.001) and paired (p < 0.001) differential expression analyses between normal and lung cancer groups indicated that SMC4 was expressed significantly higher in tumors compared to normal tissue ( Figures 6A, B ). From the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database, we observed that the lung cancer tumor tissues exhibited a significantly higher level of SMC4 protein than that in the normal tissues (p < 0.001) ( Figure 6C ). The receiver operating characteristic (ROC) curve indicated that SMC4 expression had good predictive power with an area under the curve (AUC) of 0.855 (95% confidence interval [CI] = 0.831–0.879) to discriminate breast cancer tissues from normal tissues ( Figure 6D ). In the Cox regression model, univariate Cox regression indicates that the T stage (p < 0.05), N stage (p < 0.001), M stage (p < 0.001), SMC4 (p = 0.049), pathologic stage (p < 0.001), residual tumor (p < 0.001) and Age (p = 0.022) were correlated with the lousy prognosis of lung cancer ( Table 1 ). All variables in univariate Cox regression were included in multivariate Cox regression. Multivariate Cox regression showed that T stage (p < 0.05), N stage (p < 0.05), Age (p = 0.016), and residual tumor (p = 0.008) were independent prognostic factors for OS ( Figure 6E ). As a result, we can easily conclude from the above bioinformatic analysis that SMC4 is highly expressed in lung cancer and is tightly linked to lung cancer prognosis. Furthermore, SMC4 gene knockout can significantly inhibit the proliferation and invasion of A549 cells (14); therefore, SMC4 may also be a potential biomarker of lung cancer and help to clarify its carcinogenic mechanism. In the study on the relationship between SMC4 and non-small cell lung cancer, Nie H also confirmed that SMC4 was highly expressed in lung cancer cells and tissues by quantitative reverse-transcription polymerase chain reaction (QRT-PCR), WB, and plasmid transfection. After the overexpression of SMC4 protein, the proliferation ability of PC9 cells and A549 cells were significantly increased, and the expression of cyclin, which is a particularly important positive regulation of the cell cycle considered to be closely related to the occurrence, development, and prognosis of the tumor was significantly up-regulated in cells (21, 22).

Figure 6 | Expression levels of SMC4 mRNA and SMC4 protein and the prognostic values of SMC4 expression in patients with lung cancer. (A) Expression of SMC4 mRNA in lung cancer and non-matched normal tissues in the TCGA databases. (B) Expression of SMC4 mRNA in lung cancer and matched normal tissues in the TCGA databases. (C) Expression of SMC4 protein in lung cancer in the CPTAC databases. (D) ROC curves for classifying lung cancer versus normal lung tissues in the TCGA database. (E) Forest map based on multivariate Cox analysis for overall survival in the TCGA databases. TCGA, The Cancer Genome Atlas; CPTAC, Clinical Proteomic Tumor Analysis Consortium; ROC, receiver operating characteristic.

Table 1 | Univariate and multivariate analyses of clinical pathological parameters in patients with lung cancer from TCGA database.

4.3 SMC4 and breast cancer

Breast cancer (BC), as one of the most common malignant tumors, is a major cause of death in women worldwide. Exploring the potential mechanism of tumor occurrence and development and finding new therapeutic targets has been a hot topic in the diagnosis and treatment of BC. As early as 2005, Wang Y et al. published their research results in Lancet, confirming that SMC4 gene plays an essential role in the cell cycle, DNA replication, recombination and repair, cell assembly, and other vital nodes of BC (23). Kulawiec M et al. later proposed in 2008 that the expression change of SMC4 can affect the chromosome stability of human breast epithelial cells through the p53 network and then produce tumorigenic transformation (24). By introducing the evaluation model of BC susceptibility genes based on multi-objective evaluation, the weighted sum method and the approximate ideal sorting method were also used to calculate and confirm that SMC4 is a potential BC susceptibility gene (25). Bioinformatics analysis demonstrated that the unpaired (p < 0.001) and paired (p < 0.001) differential expression analyses between normal and breast cancer groups indicated that SMC4 was expressed significantly higher in tumors compared to normal tissue ( Figures 7A, B ). ROC showed that the expression of SMC4 mRNA in BC was 0.848 (95% CI: 0.821–0.874) ( Figure 7C ). Based on the CPTAC database, SMC4 protein level was elevated in breast cancer compared with normal samples (p < 0.001) ( Figure 7D ). The immunohistochemistry results from The Human Protein Atlas database showed that SMC4 protein level was clearly enhanced in BC tissues ( Figure 7E ). In the Cox regression model, univariate Cox regression indicates that the Age (p < 0.001), N stage (p < 0.001), M stage (p < 0.001), SMC4 (p = 0.036), pathologic stage (p < 0.001), menopause status (p < 0.001) and radiation therapy (p = 0.004) were correlated with the lousy prognosis of lung cancer ( Table 2 ). All variables in univariate Cox regression were included in multivariate Cox regression. Multivariate Cox regression showed that Age (p = 0.017), pathologic stage (p < 0.001), and radiation therapy (p = 0.004) were independent prognostic factors for OS ( Figure 8A ). The correlation between SMC4 expression and the prognosis of patients with BC was calculated using the Kaplan Meier method. BC patients were divided into high and low expression groups based on the SMC4 expression median value. The high expression group has a strong correlation with worse OS (HR = 1.14 (0.68–1.30), p = 0.049), DSS (HR = 1.12 (0.53–1.26), p = 0.046), and progress-free interval (PFI) (HR = 1.22 (0.72–1.39), p = 0.041) ( Figures 8B–D ). Moreover, subsequent studies confirmed that interference with SMC4 mRNA expression effectively inhibited the proliferation, migration, and invasive ability of breast cancer MDA-MB-231 cells, with the mechanism possibly related to the activation of the PI3K/AKT signaling pathway (26–28). These studies and bioinformatics analysis results indicate that SMC4 mRNA level is an excellent prognostic biomarker for BC patients, and SMC4 will be a novel tumor prognostic marker and potential tumor therapeutic target.

Figure 7 | Expression levels of SMC4 mRNA and SMC4 protein in breast cancer. (A) Expression of SMC4 mRNA in breast cancer and non-matched normal tissues in the TCGA and GTEx databases. (B) Expression of SMC4 mRNA in breast cancer and matched normal tissues in the TCGA and GTEx databases. (C) ROC curves for classifying breast cancer versus normal breast tissues in the TCGA database. (D) Expression of SMC4 protein level in breast cancer in the CPTAC database. (E) SMC4 protein level in breast cancer using immunohistochemistry was analyzed using The Human Protein Atlas database. TCGA, The Cancer Genome Atlas; GTEx, Genotype Tissue Expression Project; CPTAC, Clinical Proteomic Tumor Analysis Consortium; ROC, receiver operating characteristic.

Table 2 | Univariate and multivariate analyses of clinical pathological parameters in patients with breast cancer from TCGA database.

Figure 8 | Prognostic values of SMC4 expression in patients with breast cancer. (A) Forest map based on multivariate Cox analysis for overall survival in the TCGA databases. Overall survival (B), disease-specific survival (C), and progress-free interval (D) for breast cancer patients with high versus low SMC4 in the TCGA databases. TCGA, The Cancer Genome Atlas; OS, overall survival; DSS, disease specific-survival (DSS); progress-free interval, PFI; CI, confidence interval.

4.4 SMC4 and colorectal cancer

Colorectal cancer (CRC) is another most common malignant tumor in human beings. Finding the key gene target for the occurrence, development, and prognosis of CRC is an extremely urgent event. Research has found that constantly evolving chromosome complements, or chromosome instability (CIN), is a form of genome instability implicated in the development and progression of CRC. Following SMC4 gene silencing, quantitative imaging microscopy identified increases in CIN-associated phenotypes, including micronucleus formation, changes in nuclear areas, and chromosome numbers, resulting in a significant driver effect of CRC (29). The driving effect can be revealed by LINC-ROR/miR-6833-3p/SMC4 regulatory network (30). In addition, bioinformatics analysis based on TCGA and CPTAC databases indicated that the expression of SMC4 mRNA and protein was significantly higher in CRC tissues than in normal tissues (p < 0.001) ( Figures 9A, B ). The ROC curve indicated that the expression of SMC4 mRNA in CRC was 0.894 (95% CI: 0.872–0.916), with a good predictive power to discriminate CRC tissues from normal tissues ( Figure 9C ). Kruskal-Wallis test, followed by the Bonferroni correction, proved that high expression of SMC4 was significantly associated with T stage (p < 0.001), N stage (p < 0.001), and M stage (p < 0.001) ( Figures 9D–F ). To predict the prognosis of patients with CRC, we constructed a nomogram of OS to integrate SMC4 and other prognostic factors, including T classification, N classification, M classification, and age ( Figure 9G ). On the nomogram, a higher total number of points was associated with a worse prognosis. Additionally, the calibration curve evaluated the nomogram’s performance of SMC4, and the Cindex of OS was 0.715 ( Figure 9H ). Moreover, recent research showed that the down-regulation of SMC4 expression could significantly inhibit the proliferation of colon cancer cells and reduce their malignancy. It is inferred that SMC4 may contribute to the occurrence of CRC and provide a new therapeutic target for the treatment of CRC (31). Furthermore, low expression levels of microRNA-124-5p can lead to a poor prognosis of CRC via targeting of SMC4, which further confirms the novel tumor prognostic marker and potential tumor therapeutic target role of SMC4 in CRC (32).

Figure 9 | Expression levels of SMC4 and the prognostic values of SMC4 expression in CRC patients, as well as the associations between SMC4 expression and clinicopathological characteristics. (A) Expression of SMC4 mRNA in CRC and non-matched normal tissues in the TCGA databases. (B) Expression of SMC4 protein in CRC in the CPTAC databases. (C) ROC curves for classifying colorectal cancer versus normal colorectal tissues in the TCGA database. Association between the SMC4 expression and the T stage (D), N stage (E), and M stage (F). (G) A nomogram that integrates SMC4 and other prognostic factors in SMC4 from TCGA data. (H) The calibration curve of the nomogram. TCGA, The Cancer Genome Atlas; CPTAC, Clinical Proteomic Tumor Analysis Consortium.

4.5 SMC4 and pancreatic cancer

The main reasons for the poor prognoses of pancreatic adenocarcinoma (PA) patients are rapid early-stage progression, chemotherapy resistance, and advanced-stage metastasis. Identification of novel diagnostic and prognostic biomarkers of PA is therefore urgently needed. Recently, Zuo D et al. first observed that SMC4 was associated with immune cell infiltration in the tumor microenvironment (TME) of PA using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, where its mechanism may be related to that immune cell infiltration, and secretion of cytokine promote the formation of the TME in PA, thus facilitating tumor progression, metastasis, and therapeutic resistance (33). In addition, bioinformatics analysis manifested that the expression of SMC4 mRNA and protein was significantly higher in PA tissues than in normal tissues (p < 0.001) ( Figures 10A, B ). The ROC curve also suggested that SMC4 expression had good predictive power with an area under the curve (AUC) of 0.944 (95% CI: 0.917–0.972) to discriminate PA tissues from normal tissues ( Figure 10C ). PA patients were divided into high and low expression groups based on the SMC4 expression median value. The high expression group has a strong correlation with worse OS (HR = 1.80 (1.18–2.76), p = 0.007) and DSS (HR = 1.86 (1.15–3.01), p = 0.011) ( Figures 10D, E ). Furthermore, the prognosis of patients with high SMC4 expression was notably more unfavorable in several subgroups, including alcohol history (HR = 1.77 (1.00–3.12), p = 0.05), gender (HR = 1.99 (1.09–3.64), p = 0.026), radiation therapy (HR = 1.93 (1.16–3.19), p = 0.011) and age (HR = 2.82 (1.45–5.48), p = 0.002) ( Figures 10F–I ). Additionally, a study by Fukuhisa H et al. demonstrated that SMC4 gene was identified as a possible oncogenic target by miR-130b-5p regulation in PA cells and then affected the proliferation, migration, and invasion of PA (34). These results indicate that SMC4 can be reported as a novel diagnostic or even therapeutic marker for future studies in PA (35).

Figure 10 | Expression levels of SMC4 and the prognostic values of SMC4 expression in PA patients. (A) Expression of SMC4 mRNA in PA and non-matched normal tissues in the TCGA databases. (B) Expression of SMC4 protein in PA in the CPTAC databases. (C) ROC curves for classifying PA versus normal pancreatic tissues in the TCGA database. (D, E) The prognostic value of SMC4 in OS and DSS of PA in the TCGA database. (F–I) OS survival curves of alcohol history, gender, radiation therapy, and age between high and low SMC4 patients with PA. TCGA, The Cancer Genome Atlas; OS, overall survival; DSS, disease-specific survival.

4.6 SMC4 and cervical cancer

Cervical cancer is one of the most common malignancies associated with mortality in females. Its onset and prognosis are primarily concerned with persistent infection with high-risk types of human papillomavirus (HPV). However, the definite molecular mechanisms of cervical cancer remain unclear. High-throughput sequencing to identify differentially expressed miRNAs and three series were selected from the Gene Expression Omnibus database to identify differentially expressed genes. Then the miRNA-TF-gene regulatory network was constructed using bioinformatic methods. Genes in the network were performed functional enrichment analysis and protein-protein interaction network analysis. The results show that SMC4 may be recognized as one hub gene with the highest connectivity degrees and be suggested to play crucial roles in the development of HPV-positive cervical cancer (36). In the Cox regression model, univariate Cox regression indicates that the T stage (p = 0.025), N stage (p = 0.002), M stage (p = 0.023), and SMC4 (p = 0.043) were correlated with the bad prognosis of cervical cancer. All variables in univariate Cox regression were included in multivariate Cox regression. Multivariate Cox regression showed that N stage (p = 0.047) was an independent prognostic factor for OS ( Table 3 ). Moreover, further research by protein-protein interaction network analysis showed that SMC4 was closely associated with MELK and TTK, which were promising candidate markers for cervical cancer prognosis and also emerged as potential therapeutic drug targets (37). In vitro, cytological studies also demonstrated that SMC4 overexpression facilitated the development of cervical cancer cells by activating NF-κB pathway, which provides a new therapeutic target for patients with cervical cancer (38).

Table 3 | Univariate and multivariate analyses of clinical pathological parameters in patients with cervical cancer from TCGA database.

4.7 SMC4 and hematological tumors

Research on SMC4 in hematological tumors is scarce. At present, it can be reviewed only in areas of acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Researchers established an AML mouse model induced by MLL-AF9 and found that down-regulating the expression of SMC4 can prolong the survival time of AML mice in the study. Knockout of SMC4 expression can reduce the proportion of leukemia stem cell (LSC) and then affect its ability to start leukemia. Cell cycle analysis also showed that SMC4 gene knockout could inhibit more LSCs in G0 phase of cell differentiation (39). It further revealed that SMC4 played a vital role in the progression of AML and provided new insights into the maintenance mechanism of leukemia stem cells. Clinical research also demonstrated that the expression of SMC4 was associated with various clinical outcomes in pediatric patients with ALL; the high SMC4 expression group had a worse disease-specific survival rate and poorer overall survival rate (40).

4.8 SMC4 and glioma

Glioma is a common primary malignant brain tumor characterized by high mortality and poor prognosis. Bioinformatics analysis based on TCGA and CPTAC databases indicated that the expression of SMC4 mRNA and protein was significantly higher in glioma than in normal tissues (p < 0.001) ( Figures 11A, B ). The immunohistochemistry results from The Human Protein Atlas database also showed that SMC4 protein level was clearly enhanced in glioma ( Figure 11C ). The correlation between SMC4 expression and the prognosis of patients with glioma was calculated using the Kaplan Meier method. The median value of SMC4 expression was used as a cut-off score, and the patients were divided into high and low SMC4 expression groups. Compared with the low SMC4 expression group, both the OS (HR = 4.86 (3.65–6.48, p < 0.001) and DSS (HR = 5.12 (3.78–6.94, p < 0.001) of the high SMC4 expression group exhibited a significantly worse prognosis ( Figures 11D, E ). ROC showed that the expression of SMC4 mRNA in glioma was 0.881 (95% CI: 0.866–0.896) ( Figure 11F ). Overexpression of SMC4 can significantly improve the proliferation rate, migration rate, and invasion ability of glioma cells in vivo and in vitro, while the down-regulation of SMC4 is just the opposite. Depth studies have shown that SMC4 overexpression through the activation of TGF β/Smad signaling promotes the invasive phenotype of glioma cells. This study provides a new insight into the cofunction of SMC4 and the mechanism by which the TGF β/Smad pathway is hyperactivated in gliomas, indicating that SMC4 is a valuable prognostic factor and a potential therapeutic target in gliomas (41). Through integrated bioinformatic analysis, SMC4 was also identified as a potential prognostic biomarker for low-grade glioma patients, which functions to promote cell proliferation by repairing replication damage, and the expression of SMC4 could be transcriptionally regulated by MYB or directly by miR-433-3p (42, 43).

Figure 11 | Expression levels of SMC4 mRNA and SMC4 protein in glioma and the prognostic value of SMC4 in glioma. (A) Expression of SMC4 mRNA level in glioma and non-matched normal tissues in the TCGA database. (B) Expression of SMC4 protein level in glioma in the CPTAC database. (C) SMC4 protein level in glioma using immunohistochemistry was analyzed using The Human Protein Atlas database. (D) Overall survival (OS) and (E) disease-specific survival (DSS) for glioma patients with high versus low SMC4. (F) ROC curves for classifying glioma versus normal tissues in the TCGA database. TCGA, The Cancer Genome Atlas; CPTAC, Clinical Proteomic Tumor Analysis Consortium; ROC, receiver operating characteristic.

4.9 SMC4 and other tumors

Nasopharyngeal carcinoma (NPC) is a malignant tumor of the nasopharyngeal epithelium. Despite the therapeutic enhancements in the treatment of NPC, late diagnosis and metastasis influence survival and poor prognosis. Liu K et al. proposed that SMC4 may be involved in the tumorigenesis of NPC. Furthermore, it may be utilized as a molecular biomarker in the early diagnosis of NPC (44). Similar results also exist in the research field of prostate cancer, and bladder Carcinoma; SMC4 is demonstrated to be the potential biomarker and therapeutic target (45, 46).

5 Summary and prospect

Malignant tumors remain a significant cause of mortality worldwide and are the second leading cause of death in the United States (47). However, an invariable factor is that early diagnosis is crucial for the successful treatment of malignant tumors; therefore, it is necessary to explore and identify a new reliable tumor biomarker actively. As an ATP-sensitive switch, SMC4 protein, together with SMC2 monomer and other non-SMC subunits, forms a condensin. It plays a critical role in promoting the compression and dissociation of sister chromatids, DNA recombination, and repair after DNA damage. Along with RNA splicing, DNA metabolism, cell adhesion, and extracellular matrix, SMC4 is also extremely important for the division cycle of embryonic cells. Similar to how normal somatic cells can replicate the gene expression patterns of embryonic cells to gain an edge over them, tumor cells with high SMC4 expression may do the same (14, 48). Moreover, SMC4 is also a positive regulator of the inflammatory innate immune response, while An excessive innate immune response not only disrupts the immune balance, leading to autoimmune diseases but also even results in cancer. Combined with relevant kinds of literature and bioinformatics analysis, we found that SMC4 protein and its microRNA levels are abnormally expressed in a variety of tumor cells and tissues, and are involved in the occurrence and development of tumors, which is closely related to the proliferation, migration, and invasion of tumors. The high expression of SMC4 also predicts the poor prognosis of tumors. The correlation between SMC4 and tumors is realized through a variety of signal pathways ( Table 4 ), which indicates that SMC4 can be used as a clinical treatment target for a variety of tumors, contribute to the individualized treatment of tumors, and improve the treatment results of tumors.

Table 4 | Tumor effect of SMC4 and its possible mechanism or pathway.

In conclusion, we revealed that compared with the normal tissues, the expression of SMC4 was significantly up-regulated in many malignant tumor tissues. It plays an essential role in the occurrence and development of tumors, which is closely related to the proliferation, migration, and invasion of tumors. Understanding the function of SMC4 in tumor progression will not only advance our knowledge of the mechanisms underlying tumor aggressiveness, but also establish SMC4 as a novel tumor prognostic marker or a potential therapeutic target for treating tumors.

Ethics approval

The study was approved by the Institutional Ethics Committee of Binzhou Medical University Hospital, Shandong Province, China. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Author contributions

ZZ and XC are the guarantors of the manuscript and contributed to conception and design of the study, acquisition and analysis of data, and writing and revision of the manuscript. XW and YD collected data and analyzed statistics, XC and XZ provided guidance and funding. All authors contributed to the article and approved the submitted version.

Funding

This research was supported by the Project of Medical and Health Technology Development Program in Shandong Province (No. 202104081018).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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CASE REPORT
published: 20 March 2023
doi: 10.3389/fimmu.2023.1144371

Case report: Preliminary response to tislelizumab plus S-1 in patients with metastatic gallbladder carcinoma: A report of five cases and a literature review

Yuzhu Zhang ¹†‡, Yuchen Liu ¹†‡, Jing Liu ²‡, Tiande Liu ¹, Hu Xiong ¹, Wen Li ¹, Xiaowei Fu ¹, Fan Zhou ¹, Shousheng Liao ²*, Lu Fang ¹* and Bo Liang ¹*

¹ Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China, ² Department of Pathology, The Second Affiliated Hospital of Nanchang University, Nanchang, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Ignacio Juarez, (UCM), Spain
Lovenish Bains, University of Delhi, India
Marko Jakopovic, University of Zagreb, Croatia

*Correspondence:
Bo Liang
lb2087@163.com 
Lu Fang
fanglu@medmail.com.cn 
Shousheng Liao
36748456@qq.com

†Present address:
Yuzhu Zhang, Second Clinical Medical College, School of Medicine, Nanchang University, Nanchang, China;
Yuchen Liu, Queen Mary School, School of Medicine, Nanchang University, Nanchang, China

‡These authors have contributed equally to this work and share first authorship

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 14 January 2023

Accepted: 06 March 2023

Published: 20 March 2023

Citation:
Zhang Y, Liu Y, Liu J, Liu T, Xiong H, Li W, Fu X, Zhou F, Liao S, Fang L and Liang B (2023) Case report: Preliminary response to tislelizumab plus S-1 in patients with metastatic gallbladder carcinoma: A report of five cases and a literature review. Front. Immunol. 14:1144371. doi: 10.3389/fimmu.2023.1144371

Gallbladder cancer (GBC) and cholangiocarcinoma are common cancers of the biliary system and are associated with a poor prognosis. Surgery and chemotherapy provide limited benefit to patients with advanced biliary tract carcinoma. Novel immunotherapies and molecularly targeted therapies are more effective options; however, few patients benefit and drug resistance is a concern. Here, we report five cases of advanced GBC with either high programmed death-ligand 1 (PD-L1) expression or a high tumor mutation burden (TMB-H). The patients were treated with a combination therapy of tislelizumab and S-1. The tumors were effectively controlled in most patients. One patient developed immune-related pneumonia (irP) during treatment, which resolved after hormone therapy, and the patient underwent surgery. Tislelizumab and S-1 were administered again after surgery; however, recurrent irP required discontinuation, and the tumor progressed after drug withdrawal. These cases demonstrate that combined therapy of anti-programmed cell death protein-1 (PD-1) antibodies and S-1 is a safe and effective regimen with few side effects for GBC patients, especially for sensitive populations (patients with TMB-H, microsatellite instability, deficient mismatch repair, or high expression of PD-L1). To our knowledge, this is the first time that tislelizumab in combination with S-1 has been used to treat patients with advanced GBC.

Keywords: immunotharapy, PD-L1, tislelizumab, S-1, GBC

1 Introduction

Gallbladder cancer (GBC) and cholangiocarcinoma (CHOL) are common cancers of the biliary system, with GBC accounting for 80–95% of all biliary tract carcinomas (BTCs) (1, 2). The 5-year overall survival (OS) of patients with BTC is below 5% (3). Surgical resection is the only curative treatment for BTC (4, 5), but only 10–15% of patients can undergo surgery because most patients are diagnosed with advanced disease or distant metastasis (6, 7). Chemotherapy is the most common adjuvant treatment for cancers of the biliary system. However, the objective response rate (ORR) of standard first-line chemotherapy (cisplatin plus gemcitabine [GC]) is only approximately 20% (8).

Immunotherapy and targeted therapy have shown good therapeutic promise for cancer in recent years (9, 10). The FDA (Food and Drug Administration) has approved targeted inhibitors of mutated isocitrate dehydrogenase 1 (IDH1), fibroblast growth factor receptor 2 (FGFR2), and casein kinase (CK2) for advanced or metastatic CHOL and durvalumab for locally advanced or metastatic BTC. However, few patients benefit from targeted therapy, and drug resistance is a concern (11–14). Therefore, the treatment of BTC remains challenging.

Here, we report our experience treating five GBC patients with high PD-L1 expression or high tumor mutation burden (TMB-H) who received a combination of tislelizumab and S-1. We present the comprehensive clinical evaluations and relevant histories of the patients and highlight the possible association between immunotherapy markers and efficacy. All patients achieved good therapeutic results and showed good treatment tolerance.

2 Case descriptions

2.1 Case 1

A 64-year-old man sought medical attention in September 2020 for marasmus. The physical examination showed no yellow pigmentation of the patient’s skin or sclera and no abdominal tenderness. Contrast-enhanced computed tomography (CECT) and positron emission tomography-computed tomography (PET-CT) of the abdomen showed a significant mass in the gallbladder and left hepatic region. Further, there were multiple metastatic nodules in the omentum and multiple metastases in the hilar and retroperitoneal lymph nodes (Figures 1A, B). CT-guided biopsy of the liver mass demonstrated poorly differentiated adenocarcinoma, and GBC with multiple intrahepatic and abdominal metastases were considered. The patient’s carbohydrate antigen 19-9 (CA19-9) was 188.49 U/mL (normal range, 0–37 U/mL), and his cancer antigen 125 (CA125) was 86 U/mL (normal range, 0–35 U/mL). The rest of the preoperative liver functional parameters were normal. We also performed next-generation sequencing (NGS) and the test report indicated a low tumor mutation burden (TMB-L) (1.76 Muts/Mb) and microsatellite stability (MSS). Immunohistochemistry demonstrated high expression of PD-L1 (Dako 22-C3, tumor proportion score (TPS) 90%, combined positive score (CPS) 90) in the tumor tissues (Figure 1C).

Figure 1 | CECT and Immunohistochemistry for Case 1. (A, B) Tumor condition at first CECT scan. (C) Immunohistochemistry of PD-L1 expression. (D, E) Tumor conditions after seven cycles of treatment of tislelizumab + S-1. (F) irP after treatment. (G, H) The tumor shrank after continued immunotherapy, but the patient developed irP. (I) New lymph node metastasis. (J) irP developed during postoperative tislelizumab administration. (K) Lung metastasis. (L) irP after administration of nivolumab. CECT, contrast-enhanced computed tomography; PD-L1, Programmed Cell Death-Ligand 1; irP, immune-related pneumonia.

This patient had a complex disease course. He received seven cycles of tislelizumab + S-1 between October 2020 and February 2021. CECT indicated that the patient’s tumor shrank 89.37% (186.25 cm³ to 19.80 cm³, partial remission [PR]) and the abdominal metastases disappeared (Figures 1D, E). However, the patient developed immune-related pneumonia (irP) (Figure 1F) and had to discontinue immunotherapy but he continued S-1 treatment. After more than two months of meprednisone therapy, the patient’s pneumonia was in remission. Therefore, he restarted tislelizumab and S-1 (Figures 1G, H).

However, his CA19-9 levels continued to rise. After two cycles of treatment, CECT indicated that the primary tumor continued to shrink and the abdominal metastases had disappeared, but there were newly enlarged lymph nodes in the hilar area (Figure 1I). We did not identify any distant metastases on PET-CT. On May 2021, the patient underwent a laparoscopic cholecystectomy plus partial resection of liver S4b + 5 and regional lymph node dissection. The postoperative histopathological examination (HPE) report showed massive necrosis of the intrahepatic tumor tissue, no active tumor cells, and acute and chronic inflammatory cell infiltration. Individual atypical cells were observed in the liver tissue near the gallbladder. There was one positive lymph node in group 12, and no cancer cells were observed in the lymph nodes in groups 8, 9, 13, and 16 (tumour-node-metastasis(T4N1M1) stage: IVB).

Because of the lymph node metastases, the patient received tislelizumab + S-1 for two months after surgery. However, he again developed irP, so immunotherapy was discontinued, and he received S-1 monotherapy (Figure 1J). Six months after monotherapy, the patient began to develop lung metastases (Figure 1K). To avoid the development of irP, he switched to gemcitabine plus S-1 for treatment, but the tumor in the lung region continued to progress. Therefore, we administered three cycles of nivolumab. However, the tumor continued to enlarge and irP recurred (Figure 1L). As of February 2023, the patient was alive but more than two years after diagnosis, the tumor had not been controlled.

2.2 Case 2

A 69-year-old woman sought medical attention in June 2021 for abdominal pain. The physical examination showed no yellow pigmentation of the patient’s skin or sclera and no abdominal tenderness. CECT showed gallbladder and intrahepatic region lesions, suggesting GBC with intrahepatic metastasis. In addition, the tumor had invaded the hilar vessels, and the patient was unable to undergo surgery (T4NxM1, IVB) (Figures 2A-C). Ultrasonography-guided liver biopsy confirmed liver invasive or metastatic poorly differentiated carcinoma (Figures 2D, E). The patient’s CA19-9 was 29.2 U/mL and her CA125 was 55 U/mL. The rest of the liver functional parameters were normal. NGS testing indicated TMB-H (16.48 Muts/Mb) and MSS. Immunohistochemistry indicated low expression of PD-L1 (Dako 22-C3, TPS 10%) in the tumor tissues (Figure 2F).

Figure 2 | CECT, HE, and Immunohistochemistry for Case 2. (A) Intrahepatic mass at diagnosis. (B) A second intrahepatic mass at diagnosis. (C) Peri-gallbladder condition at diagnosis. (D) Hematoxylin and eosin staining of biopsy tissue (original magnification ×200). (E) Hematoxylin and eosin staining of biopsy tissue (original magnification ×400). (F) PD-L1 staining of biopsy tissue (original magnification ×400). (G) The reduction of the intrahepatic mass after 5 months of treatment. (H) Significant reduction in a second intrahepatic mass after 5 months of treatment. (I) Significant reduction in the peri-gallbladder mass after 5 months of treatment. (J) Persistent reduction of the intrahepatic mass after 18 months of treatment. (K) Persistent reduction in a second intrahepatic mass after 18 months of treatment. (L) Persistent reduction in the peri-gallbladder mass after 18 months of treatment. CECT, contrast-enhanced computed tomography; HE, hematoxylin and eosin; PD-L1, Programmed Cell Death-Ligand 1.

The patient received 26 cycles of tislelizumab + S-1 regularly between July 2021 and February 2023. The patient was offered curative surgery but she refused and chose to continue medication. As of November 2022, CECT indicated that the patient’s tumors shrank 98.28% (from 66.33 cm³ to 1.14 cm³, PR) (Figures 2G-L). Moreover, the patient’s CA19-9 and CA125 levels returned to normal.

2.3 Case 3

A 55-year-old man sought medical attention in June 2021 for upper abdominal pain. The physical examination showed no yellow pigmentation of the patient’s skin or sclera and no abdominal tenderness. CECT showed space-occupying lesions in the gallbladder region, invasion of the right liver and right colon suggesting GBC with intrahepatic invasion, and invasion of the hepatic region of the colon (Figure 3A). The patient’s CA 19-9 was 274.81 U/mL, and CA 12-5 was 108.6 U/mL. The rest of the preoperative liver functional parameters were normal. The patient decided to undergo a cholecystectomy plus partial resection of liver S4b + 5 and regional lymph node dissection, a right hemicolectomy, a bile duct-jejunal ROUX-Y anastomosis, a right hemicolectomy and an ileum-colon anastomosis on June 9, 2021. The postoperative HPE report showed medium-low differentiated adenocarcinoma of the gallbladder and suggested that tumor-free margins (R0) was achieved (T4N1M0, IVA). NGS testing indicated TMB-H (17.93 Muts/Mb) and MSS. Immunohistochemistry indicated low expression of PD-L1 (Dako 28-8, TPS <1%) in the tumor tissues.

Figure 3 | CECT and Macroscopy for Case 3-5. (A) Preoperative tumor conditions of case 3. (B) Postoperative 6 months reexamination of case 3. (C) Postoperative 18 months reexamination of case 3. (D) Preoperative tumor conditions of case 4. (E) Postoperative progression and bile duct dilatation of case 4. (F) Postoperative progression and hilar soft tissue effects of case 4. (G) Postoperative neck metastasis of case 4. (H) Hilar soft tissue shadow slightly reduced after immunotherapy in case 4. (I) Neck tumor disappeared after immunotherapy in case 4. (J) Preoperative tumor conditions of case 5. (K) Postoperative intrahepatic metastasis of case 5. (L) Intrahepatic metastases reduced after immunotherapy in case 5. CECT, contrast-enhanced computed tomography; HE, hematoxylin and eosin; PD-L1, Programmed Cell Death-Ligand 1.

Due to the presence of liver and colon invasion and lymph node metastases in this GBC patient, the risk of postoperative recurrence was very high. The patient received 23 cycles of tislelizumab + S-1 regularly between June 2021 and October 2022. As of December 31, 2022, CECT showed no significant tumor recurrence (complete response (CR)) (Figures 3B, C), and the patient’s CA 19-9 and CA 12-5 levels returned to normal.

2.4 Case 4

A 53-year-old woman sought medical attention in August 2021 for a diagnosis of pathology suggestive of GBC after laparoscopic cholecystectomy one week earlier. The physical examination showed no yellow pigmentation of the patient’s skin or sclera and no abdominal tenderness. Healing surgical scars were visible on the patient’s abdomen. Pathology revealed a gallbladder tumor invading the serosa. CECT showed postoperative changes in the gallbladder region, and no metastases were observed in the liver. (Figure 3D) The patient’s CA 19-9 was 21.37 U/mL and CA 12-5 was 29.80 U/mL. The rest of the preoperative liver functional parameters were normal. The patient received a partial resection of liver S4b + 5 and regional lymph node dissection on August 9, 2021, after confirmation that there were no distant metastases. The HPE report showed medium-low differentiated adenocarcinoma of the liver tissue adjacent to the gallbladder. Cancer tissue metastasis was observed in 2 of 2 lymph nodes in group 8, 1 of 3 lymph nodes in group 12, and 1 of 6 lymph nodes in groups 13 and 16. We also observed a poking hole 2.0 cm below the xiphoid (T3N2M1, IVB).

Under the doctor’s care, the patient began receiving GS regimen chemotherapy after surgery. On January 14, 2022, the patient developed a hilar soft tissue shadow with intrahepatic bile duct dilatation (Figure 3E), and percutaneous transhepatic cholangial drainage was performed. On May 5, 2022, tumor progression developed, accompanied by an abdominal CECT revealing multiple enlarged lymph nodes in the hilar region and abdominal, and portal vein tumor thrombosis. (Figures 3F, G) An abnormal mass was palpable in the neck, and cervical metastasis of the tumor was considered. We performed NGS, and the report indicated TMB-H (7.1 Muts/Mb) and MSS. Immunohistochemistry indicated no expression of PD-L1 (Dako 28-8, TPS 0%) in the tumor tissues. The patient began receiving tislelizumab and S-1 treatment on June 3, 2022. As of December 31, 2022, CECT showed that the patient’s tumor shrank 76.45% (from 34.89 cm³ to 8.21 cm³, PR) and the patient’s neck mass disappeared. (Figures 3H, I).

2.5 Case 5

A 64-year-old male sought medical attention on May 12, 2022, for upper abdominal pain persisting for more than one month. The physical examination showed no yellow pigmentation of the patient’s skin or sclera and no abdominal tenderness. CECT revealed GBC with local intrahepatic invasion (Figure 3J). The patient’s CA 19-9 level was more than 700 U/mL, and CA 12-5 was 35.9 U/mL. The rest of the preoperative liver functional parameters were normal. The patient received a laparoscopy, a cholecystectomy plus partial resection of liver S4b + 5 and regional lymph node dissection, and a biliary-intestinal anastomosis after on May 19, 2022, after confirming there were no distant metastases. The postoperative HPE report suggested that R1 resection was achieved. The report also showed a gallbladder adenocarcinoma invading the gallbladder wall’s full thickness and surrounding liver tissue. In addition, nerve and hepatic arterial sheath invasions were found. No metastases were found in any of the 12 groups of lymph nodes (T4N0M0, IVA). The patient did not receive chemotherapy after surgery. However, the tumor recurred and was found on reexamination two months after surgery. CT showed intrahepatic metastasis (Figure 3K). Immunohistochemistry indicated a high expression of PD-L1 (Dako 28-8, TPS >60%) in the tumor tissues.

The patient began receiving tislelizumab and S-1 treatment on July 30, 2022. As of February 2023, he had received 10 cycles of treatment and CECT showed that the tumor shrank 97.55% (from 7.75 cm³ to 0.19 cm³, PR) (Figure 3L).

The timelines of Cases 1–5 are shown in Figure 4.

Figure 4 | Timelines of Case 1-5. (A) The timeline of case 1. (B) The timeline of case 2. (C) The timeline of case 3. (D) The timeline of case 4. (E) The timeline of case 5.

3 Discussion

The FDA recently approved durvalumab combined with GC for adult patients with locally advanced or metastatic BTC based on the pivotal results of the TOPAZ-1 study, which showed that the combination of durvalumab plus GC prolonged OS (median OS 12.8 months vs. 11.5 months) and progression-free survival (PFS) (median PFS 7.2 months vs. 5.7 months) compared with chemotherapy alone (15). Unfortunately, few patients can benefit from this treatment (9, 16).

In the decade since immunotherapy became available for BTC, some case reports have shown good treatment outcomes. Lenvatinib plus anti-PD-1 treatment tends to improve survival in patients with advanced BTC after failure with GC (17); the median PFS and OS in patients receiving combination therapy were 4.0 months (95% confidence interval: 3.5–5.0) and 9.50 months (95% confidence interval: 9.0–11.0), respectively. However, 98.64% of patients reported adverse events (15). A phase I study based on 30 Japanese patients with unresected or recurrent biliary tract cancer showed anti-tumor activity for nivolumab as a monotherapy (median OS 5.2 months; median PFS 1.4 months) and in combination with GC (median OS 15.4 months; median PFS 4.2 months) (18). Another report showed that pembrolizumab monotherapy in BTC patients with PD-L1 expression in 1% or more of tumor cells or with tumor-infiltrating lymphocytes achieved responses in 4 out of 24 (17%) patients (19). Hepatic artery infusion chemotherapy (HAIC) in combination with immunotherapy also has high anti-tumor activity (20–22). One retrospective study pooled over 100 BTC patients treated via HAIC with the 3cir-OFF regimen, which is comprised of oxaliplatin, 5-fluorouracil, and folinic acid. The median PFS and OS of patients receiving this regimen were 9.8 months and 14.2 months, respectively (22). A study from China showed that tislelizumab was also observed to have anti-tumor activities in other solid tumors with an ORR ≥15% including nasopharyngeal carcinoma (43%), MSI-H/deficient mismatch repair (dMMR) solid tumors (19%), non-small cell lung cancer (18%), gastric cancer (17%), hepatocellular carcinoma (17%), and melanoma (15%) (23).

Some reports have shown that S-1 alone is effective and well-tolerated in patients with advanced GBC (24–26). Three patients achieved partial or complete remission after S-1 monotherapy, suggesting that S-1 could be used as an alternative therapy if standard first-line chemotherapy drugs were not tolerated. Results from a recent large trial of Japanese patients undergoing BTC resection showed a significant increase in both three-year OS (77.1% vs. 67.6%) and three-year relapse-free survival (62.4% vs. 50.9%) for patients treated with S-1 compared to those who did not receive it (27). Results from another trial investigating the efficacy of combined chemotherapy with S-1 and cisplatin in the treatment of metastatic or recurrent BTC showed an overall ORR of 30% and a median OS of 8.7 months (28). In addition, the synergistic effect of nab-paclitaxel and GC has shown good antitumor efficacy and controllable safety (29, 30). A phase II clinical trial (ClinicalTrials.gov identifier: NCT02392637) showed that nab-paclitaxel combined with GC prolonged OS (19.2 months vs 11.7 months) compared with GC alone (31).

Several studies suggest that high PD-L1 expression, TMB-H, MSI, and deficient mismatch repair (dMMR) are potential biomarkers for immunotherapy (32–35) and that tumors with these biomarkers may show higher ORR (36–38). However, stereotactic body radiotherapy combined with nivolumab or pembrolizumab showed good survival benefits and acceptable toxicity in patients with intrahepatic cholangiocarcinoma with TMB-L, MSS, mismatch repair-proficient (pMMR), and no PD-L1 expression (expression level < 1%) (39, 40). Further, some patients without high PD-L1 expression respond to immune and targeted combination therapy. Some BTC patients with mutated sites have low PD-L1 expression (TPS ≤10%) but still achieve significant clinical benefits from targeted combined anti-PD-L1 therapy (41–43). Of note, none of these previously published cases mention other biomarkers such as TMB, MMR, or MSS.

Given the superiority of immune and targeted combination therapy shown by published studies, several clinical trials on immunotherapy and targeted therapy in patients with GBC are ongoing (ClinicalTrials.gov identifiers: NCT04003636, NCT05239169, NCT04333927, NCT04308174, NCT04466891, NCT04183712, and NCT04211168).

The TOPAZ-1 study showed that combination therapy did not have significantly different survival benefits based on PD-L1 expression (15). However, other biomarkers were not considered when performing grouping. Another clinical trial administered nivolumab alone for patients with refractory BTC and found that patients with high PD-L1 expression had a significantly longer PFS than those with low PD-L1 expression, and all patients who responded to treatment had dMMR (44). Therefore, we believe that although the overall benefit is limited, immunotherapy combined with chemotherapy will achieve better clinical outcomes in sensitive populations (patients with TMB-H, MSI, dMMR, or high expression of PD-L1). However, it is still unclear whether combination therapy benefits these sensitive patients more than immunotherapy alone.

Because the patient in Case 1 developed irP, we changed the treatment from tislelizumab to nivolumab. However, his irP recurred, and his tumor continued to progress. Therefore, it appears that switching medications after the patient develops immune-related adverse events does not decrease the severity of the side effects. In addition, while waiting to start nivolumab, the patient developed drug resistance. In Case 2, after the medication was effective and the surgical criteria were met, the patient chose to continue medication instead of surgery. Although the patient showed good clinical results, we still cannot determine whether it is feasible to forgo surgery after response to combination therapy. Further, if the patient opts to continue treatment, determining how to judge whether there is tumor activity by CECT and how long to discontinue the drug after the development of adverse events remains a problem. In Case 3, considering the patient’s high risk of postoperative recurrence, we administered the combination of tislelizumab and S-1 to the patient postoperatively. Currently, the patient has not experienced a relapse for 19 months. The patients in cases 4 and 5 developed tumor progression after surgery. After which they received tislelizumab + S-1 and the tumor shrank. Combining the results from the three patients (Cases 3, 4, and 5), we suggest that timely postoperative drug administration for patients with medication indication may reduce tumor progression and the recurrence rate.

Only the patient in case 1 developed irP; the rest of the patients had no immune-related adverse events. Four of the five patients profiled in this study underwent surgery, and all of them recovered smoothly with no significant postoperative complications. In our study of immunotherapy for GBC, we found only one case of irP, but our team also found immune-related liver damage and immune-related myocarditis in immunotherapy for liver cancer. Deaths occurred in these two patients. This study has several limitations. First, the samples size of our study is too small to draw general conclusions. Thus, further studies are required to establish the effectiveness of the combined treatment with tislelizumab plus S-1. Second, we did not recheck the PD-L1 and TMB status of patients which go on to form metastases in continued immunotherapy. We think it may be beneficial to test the PD-L1 and TMB status of patients’ specimens if patients show resistance to anti-PD-1 immunotherapy.

4 Conclusions

The five cases we report show that the combination of anti-PD-1 and S-1 therapy is a safe and effective treatment option for patients with advanced GBC and may be more useful for those with a high expression of immunotherapeutic markers (PD-L1, TMB, etc.).

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.

Ethics statement

Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

SL, LF, and BL contributed to the conception and design of the study. SL, LF, and BL contributed to the administrative support of the study. FZ, JL, SL, LF, and BL contributed to the provision of study materials for patients. YL contributed to the collection and assembly of data for the study. YZ and YL contributed to the data analysis and interpretation of the study. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the National Natural Science Foundation of China (grant No. 82160578); the Natural Science Foundation of Jiangxi Province (No. 20212BCJ23024, No. 20202BAB206049, No. 20212ACB206025); the Health Department of Jiangxi Province (grant No. 20198020).

Acknowledgments

We are thankful to all the laboratory members of the Department of General Surgery and Pathology, the Second Affiliated Hospital of Nanchang University for their helpful discussion.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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ORIGINAL RESEARCH
published: 24 March 2023
doi: 10.3389/fimmu.2023.1141148

Immune-related adverse events correlate with the efficacy of PD-1 inhibitors combination therapy in advanced cholangiocarcinoma patients: A retrospective cohort study

Yanfang Zhang ¹†, Xiaoting Wang ²†, Yinyan Li ¹, Yun Hong ¹, Qingwei Zhao ¹,³* and Ziqi Ye ¹,³*

¹ Department of Clinical Pharmacy, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China, ² Department of Medical Oncology, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China, ³ Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Elena Tassi, San Raffaele Hospital (IRCCS), Italy
Susanna Ulahannan, University of Oklahoma, United States

*Correspondence:
Qingwei Zhao
qwzhao@zju.edu.cn
Ziqi Ye
ziqiye@zju.edu.cn

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 10 January 2023

Accepted: 16 March 2023

Published: 24 March 2023

Citation:
Zhang Y, Wang X, Li Y, Hong Y, Zhao Q and Ye Z (2023) Immune-related adverse events correlate with the efficacy of PD-1 inhibitors combination therapy in advanced cholangiocarcinoma patients: A retrospective cohort study. Front. Immunol. 14:1141148. doi: 10.3389/fimmu.2023.1141148

Background

Whether irAEs can predict the efficacy of PD-1 inhibitors in cholangiocarcinoma (CCA) has not been assessed. Therefore, this study aims to investigate the correlation between irAEs and the therapeutic effect of PD-1 inhibitors combination therapy in patients with advanced CCA.

Methods

All patients with CCA who were consecutively admitted to the inpatient unit of our hospital and received PD-1 inhibitors combination therapy between September 2020 and April 2022 were screened. In total, 106 patients with CCA were screened out. We then followed up these patients until October 2022. Due to perioperative use (n=28), less than 2 cycles of PD-1 inhibitor therapy (n=9), incomplete data (n=8) and no pathological report (n=2), 59 patients were included in the final analysis. The patients were divided into the irAEs cohort and the non-irAEs cohort according to whether they experienced irAEs or not. The Log-Rank test was performed to compare the difference in survival time between these two cohorts. We then applied multivariate COX regression analysis to investigate whether irAEs were independent prognostic factors for survival in patients with advanced CCA.

Results

Finally, 32 patients were included in the irAEs cohort and 27 patients in the non-irAEs cohort. A total of 32 patients (54.2%) had any-grade irAEs, of which 4 patients (6.8%) had grade 3-4 irAEs. The most common irAEs were thyroid toxicity (30.5%) and dermatologic toxicity (30.5%). There were no notable differences in demographics and clinical characteristics between the irAEs and non-irAEs cohorts, except for total bilirubin level (P=0.026) and relapse (P=0.016). The disease control rate (DCR) in the irAEs cohort was higher than in the non-irAEs cohort (90.6% vs 70.4%, P=0.047). Median overall survival (OS) and median progression-free survival (PFS) were better in the irAEs cohort than in the non-irAEs cohort (OS: 21.2 vs 10.0 months, P<0.001; PFS: 9.0 vs 4.4 months, P=0.003). Multivariate COX regression analysis showed that irAEs were independent prognostic factors for OS and PFS (OS: HR=0.133, 95% CI: 0.039-0.452, P=0.001; PFS: HR=0.435, 95% CI: 0.202-0.934, P=0.033).

Conclusion

IrAEs correlated with improved DCR, OS, and PFS in advanced CCA patients receiving PD-1 inhibitors combination therapy.

Keywords: advanced cholangiocarcinoma, PD-1 inhibitors combination therapy, immune-related adverse events (irAEs), efficacy prediction, clinical marker

1 Introduction

Cholangiocarcinoma (CCA) is major type of biliary tract cancer (BTC) that originates from the bile duct epithelium (1). It is the second most common hepatobiliary cancer with high malignancy and poor prognosis (1). Depending on its anatomical location, CCA is divided into extrahepatic cholangiocarcinoma (ECC) and intrahepatic cholangiocarcinoma (ICC). CCA has few treatment options and is insensitive to both radiotherapy and chemotherapy. The onset of CCA is latent and is often at a later stage of the tumor when it is diagnosed (2). Surgery is currently the most effective treatment for CCA. However, only about 35% of patients with early-stage CCA can be treated with radical surgery. Even with radical surgery, patients still have a postoperative relapse rate of up to 70-75% (3, 4). Gemcitabine in combination with cisplatin has been the standard first-line treatment for patients with unresectable or metastatic CCA since 2010, based on the phase 3 ABC-02 trial (5). However, the overall survival (OS) of patients with advanced CCA treated with chemotherapy remains unsatisfactory.

With the ongoing development of precision medicine, immunotherapeutic drugs, particularly immune checkpoint inhibitors (ICIs), have made breakthroughs in cancer treatment. Inhibitors of the programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) play an important therapeutic role in a wide range of tumors (6–9). In the phase 3 TOPAZ-1 trial in 685 patients with advanced BTC, durvalumab (a PD-L1 inhibitor) in combination with gemcitabine and cisplatin improved OS compared to placebo plus chemotherapy (median OS: 12.8 months vs 11.5 months, P=0.021) (10). Therefore, durvalumab plus gemcitabine and cisplatin is recommended as one of the preferred first-line regimens for advanced CCA. The efficacy of PD-1 inhibitors in CCA has also been extensively studied, but objective response rates (ORR) vary widely between studies, ranging from 17.5% to 40.9% (11–14). Therefore, there is a need to identify biomarkers that predict response to PD-1 inhibitors in CCA. Currently, deficient mismatch repair/microsatellite instability-high (dMMR/MSI-H), tumor mutation burden-high (TMB-H) and PD-L1 expression have been widely studied as biomarkers related to response to immunotherapy in solid tumors (15–17), but the evidence in CCA is insufficient (11, 12, 14, 18, 19). Recently, several biomarkers have been identified in ICC to predict the efficacy of PD-1inhibitors, including pre-treatment serum lipid levels, inflammation-based scores and immune-related RNA signatures (20–22). However, there is still a lack of clinical markers during treatment to identify patients who may benefit from PD-1 inhibitors in CCA. Interestingly, PD-1 inhibitors not only activated effector T cells, but also reduced regulatory T cells, which may play a key role in maintaining immune tolerance (23). As a result, the immune system can also attack normal cells, leading to immune-related adverse events (irAEs) (24). Several studies have shown that irAEs can serve as an effective clinical biomarker for PD-1 inhibitors in several cancers, such as lung, liver, gastric cancers, and melanoma (24–28). However, there is a lack of studies on whether irAEs may play a similar role in CCA.

Therefore, this retrospective cohort study aims to investigate the correlation between irAEs and the efficacy of PD-1 inhibitors combination therapy in CCA patients. It will provide a theoretical basis for whether irAEs can be considered as a clinical marker for predicting the efficacy of PD-1 inhibitors combination therapy, and provide evidence for the precise screening of CCA patients who may benefit from PD-1 inhibitors combination therapy.

2 Materials and methods

2.1 Participants

All CCA patients consecutively admitted to the inpatient unit of our hospital and received PD-1 inhibitors combination therapy between September 2020 and April 2022 were screened. A total of 106 patients with CCA were screened out. A pathology report was required for each patient. All patients were then followed up until October 2022.

2.2 Inclusion and exclusion criteria

Inclusion criteria; (1) pathologically diagnosed with CCA; (2) ≥18 years of age, no gender restrictions; (3) received PD-1 inhibitors in combination with chemotherapy or targeted therapy [including multi-target tyrosine kinase inhibitors (TKIs)]; (4) received at least two cycles of PD-1 inhibitors combination therapy; (5) Eastern Cooperative Oncology Group (ECOG) performance status (PS) score was 0-1. Exclusion criteria: (1) incomplete medical records; (2) received PD-1 inhibitor combinations therapy for perioperative treatment.

2.3 Measures

We reviewed electronic medical records and extracted demographic information and laboratory parameters of eligible patients, including gender, age, ECOG PS score, TNM stage, total bilirubin, alpha-fetoprotein (AFP), and carcinoembryonic antigen (CEA) before PD-1 inhibitors combination therapy. A fixed dose of 200mg for pembrolizumab, sintilimab, camrelizumab, and tislelizumab and 3 mg/kg or 240 mg for toripalimab was given intravenously every 3 weeks. The choice of PD-1 inhibitor for treatment does not follow strict guidelines, mainly because of the accessibility and price of the drugs. In addition, to date, no studies have shown which PD-1 inhibitor is more effective than others. According to National Comprehensive Cancer Network (NCCN) guidelines, CCA patients who developed irAEs require a decision-making team consisting of oncology, pharmacy, endocrinology, dermatology and pulmonology specialists. This team determined whether the patient needed to discontinue PD-1 inhibitors and received appropriate observation, levothyroxine supplementation, anti-allergy medication, and topical or systemic hormone therapy. Multi-target TKIs combined with PD-1 inhibitors were lenvatinib (8 mg orally per day) or anlotinib (10 mg orally once daily d1-14, every 3 weeks for one cycle), which are recommended by the China Society of Clinical Oncology (CSCO) guidelines for advanced BTC patients. The specific combination regimens for each patient are shown in Supplementary Table 1.

Response to treatment was assessed according to the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1). ORR, defined as the proportion of patients who achieved a partial response (PR) or complete response (CR). The disease control rate (DCR) was defined as the proportion of patients who achieved not only PR and CR but also stable disease (SD). Progression-free survival (PFS) was defined as the time from the first cycle of PD-1 inhibitors combination therapy until death or disease progression, whichever came first. In addition, OS was defined as the time from the first cycle of PD-1 inhibitors combination treatment to death from any cause. The irAEs were assessed and graded for severity according to the Common Terminology Criteria for Adverse Events 5.0 (CTCAE 5.0). Patients were then divided into an irAEs cohort and a non-irAEs cohort based on whether they had irAEs or not.

2.4 Statistical analysis

Data were calculated as mean ± standard deviation (SD) for continuous variables with normal distribution, otherwise as median (interquartile range). Categorical variables were presented as frequencies and percentages. Student’s t-test or Mann-Whitney U test was used to compare differences between the irAEs cohort and the non-irAEs cohort for continuous variables. Fisher’s exact test or Chi-square test was used for categorical variables. Kaplan-Meier survival curves were plotted. Differences in OS and PFS between the irAEs and non-irAEs cohorts were compared using the Log-Rank test. We also performed univariate and multivariate COX regression analyses to investigate whether irAEs were independent prognostic factors for OS and PFS in CCA patients. All statistical analyses were performed using the IBM Statistical Package for Social Sciences (SPSS, version 26). A two-sided P<0.05 was considered a significant difference.

3 Results

3.1 Clinical and demographic characteristics of all included CCA patients

A total of 106 CCA patients receiving PD-1 inhibitors combination therapy at our hospital from September 2020 to April 2022 were screened out. After reviewing the electronic medical records system, 28 patients with perioperative treatments, nine patients with less than two cycles of PD-1 inhibitor treatments, eight patients with incomplete data, and two patients without pathological diagnosis were excluded. Patients were divided into the irAEs cohort and non-irAEs cohort according to whether they had irAEs or not. Finally, 32 patients were included in the irAEs cohort and 27 patients were enrolled in the non-irAEs cohort. The flow chart is shown in Figure 1.

Figure 1 | The flow chart of this study. irAEs, immune-related adverse events.

The clinical and demographic characteristics of all CCA patients in the irAEs and non-irAEs cohorts are shown in Table 1. Of these, 35 (59.3%) patients were treated with sintilimab, 7 (11.9%) with camrelizumab, 5 (8.5%) with pembrolizumab, 4 (6.8%) with toripalimab, and 8 (13.6%) with tislelizumab. In two cohorts, the mean age was 63.3 and 63.0 years, respectively. Fifteen patients (46.9%) in the irAEs cohort and 15 patients (55.6%) in the non-irAEs cohort were male. The mean body mass index (BMI) in two cohorts was 22.1 and 21.8 kg/m², respectively. No pre-existing autoimmune disease (96.9% vs 96.3%), ECOG PS score of 1 (84.4% vs 92.6%), ICC (81.3% vs 63.0%), HBsAg negative (84.4% vs 77.8%), Child-Pugh A (87.5% vs 88.9%), no history of cirrhosis (78.1% vs 81.5%), AFP ≤20 ng/mL (96.9% vs 100.0%), CEA ≤5 ng/mL (62.5% vs 55.6%), CA125 >35 U/mL (56.3% vs 66.7%), CA19-9 >37 U/mL (78.1% vs 74.1%), multiple tumors (75.0% vs 77.8%), lymph node metastasis (81.3% vs 81.5%), AJCC stage IV (62.5% vs 63.0%), PD-1 inhibitors combination therapy as first-line treatment (87.5% vs 96.3%), and PD-1 inhibitors in combination with chemotherapy (62.5% vs 81.5%) were the majority of CCA patients enrolled. There were no statistical differences in most clinical and demographic characteristics between the two cohorts (P>0.05), except for total bilirubin level (P=0.026) and relapse (P=0.016).

Table 1 | Clinical characteristics of CCA patients with and without irAEs.

3.2 Description of irAEs

In total, 32 (54.2%) patients experienced any- grade irAEs and 4 (6.8%) patients experienced severe (grade 3-4) irAEs. The most common irAEs were thyroid dysfunction (30.5%) and dermatologic adverse events (30.5%), manifesting as hypothyroidism (25.4%), hyperthyroidism (5.1%), rash (22.0%) and pruritus (8.5%). Other irAEs were mainly pneumonia (5.1%), colitis (1.7%), hyperglycaemia (1.7%), pancreatitis (1.7%) and infusion-related reactions (1.7%). The incidence of irAEs is shown in Table 2.

Table 2 | Description of irAEs in CCA patients treated with PD-1 inhibitors in combination with chemotherapy and/or multi-target TKIs.

3.3 Comparison of the efficacy between irAEs and non-irAEs cohorts

The median follow-up time was 11.4 (95%CI: 9.1-13.7) months. The ORR was similar in the irAEs and non-irAEs cohorts (21.9% vs 14.8%, P=0.488). The DCR was higher in the irAEs cohort (90.6% vs 70.4%, P=0.047) (Table 3). Median PFS was 9.0 (95% CI: 4.9-13.1) months in the irAEs cohort and 4.4 (95% CI: 3.2-5.6) months in the non-irAEs cohorts. Median OS was 21.2 (95% CI: 10.9-31.5) months and 10.0 (95% CI: 5.2-14.8) months in these two cohorts, respectively. Both PFS and OS were better in the irAEs cohort than in the non-irAEs cohort (P=0.003 and P<0.001, respectively) (Figure 2).

Table 3 | Treatment responses of CCA patients with and without irAEs.

Figure 2 | PFS and OS in patients with CCA with or without irAEs.(A) Progression-free survival (PFS) and (B) overall survival (OS) in CCA patients with or without irAEs after receiving programmed cell death protein 1 (PD-1) inhibitors combination therapy. irAEs, immune-related adverse events; CCA, cholangiocarcinoma.

3.4 Correlation of irAEs with PFS and OS

To investigate whether the differences in patients’ clinical and demographic characteristics affected the results of this study, we performed univariate COX regression analysis, the results showed that total bilirubin >21 μmol/L (HR=2.621, 95% CI: 1.194-5.752, P=0.016), Child-Pugh B (HR=3.316, 95% CI: 1.317-8.353, P=0.011), CEA>5 ng/mL (HR=2.410, 95% CI: 1.256-4.626, P=0.008) and irAEs (HR=0.388, 95% CI: 0.200-0.750, P=0.005) were prognostic factors for PFS. In addition, we also constructed a multivariate COX regression model using the above variables as well as relapse and treatment strategy (P=0.060 in univariate analysis), and the results demonstrated that irAEs were an independent protective factor for PFS (HR=0.435, 95% CI: 0.202-0.934, P=0.033) Table 4).

Table 4 | Univariate and multivariate analyses of prognostic factors for PFS in CCA patients.

In addition, univariate COX regression analysis indicated that CEA>5 ng/mL (HR=3.764, 95% CI: 1.481-9.565, P=0.005) and irAEs (HR=0.149, 95% CI: 0.053-0.423, P<0.001) were prognostic factors for OS. We constructed a multivariate COX regression model with the above 2 variables as well as total bilirubin, relapse and treatment strategy. The results showed that irAEs were an independent protective factor for OS (HR=0.133, 95% CI: 0.039-0.452, P=0.001), whereas CEA>5 ng/mL was an independent risk factor for OS (HR=5.718, 95% CI: 1.826-17.902, P=0.003) (Table 5).

Table 5 | Univariate and multivariate analyses of prognostic factors for OS in CCA patients.

4 Discussion

PD-1 inhibitors are promising anti-tumor drugs that have led to several breakthroughs in the treatment of tumors such as lung cancer and melanoma. A number of clinical trials of PD-1 inhibitors for the treatment of CCA are ongoing, and several case reports and clinical findings suggest that PD-1 inhibitors have therapeutic effects in CCA (11, 12, 29–31). Data from KEYNOTE-158 (11) showed that among 22 patients with advanced CCA with dMMR/MSI-H who received pembrolizumab monotherapy, the ORR was 40.9% (2 CR and 7 PR), and the median PFS and OS were 4.2 (95% CI: 2.1-NR) months and 24.3 (95% CI: 6.5-NR) months, respectively. In another phase II study in 46 patients with advanced BTC (12), 5 patients (11%) responded to nivolumab, all mismatch repair protein-positive tumors. These studies suggest that dMMR/MSI-H may be associated with improved outcomes in CCA patients treated with PD-1 inhibitors, but the evidence is insufficient. Zhang J (31) et al. reported a patient with metastatic ICC who failed first-line chemotherapy and achieved a CR after three cycles of sintilimab monotherapy. However, this patient had a low TMB and was microsatellite stable (MSS). Therefore, there is still a need to explore new biomarkers to predict the effect of PD-1 inhibitors on CCA.

Interestingly, PD-1 inhibitors have the potential to augment normal immune responses while enhancing antitumor immune responses, leading to dysregulation of immune homeostasis and ultimately to irAEs (23). Weber JS et al (32) were the first to report that irAEs of any grade were associated with higher ORR in patients with advanced melanoma receiving nivolumab. Since then, several studies have shown that the occurrence of irAEs is associated with improved ORR, PFS or OS in patients with non-small cell lung cancer (NSCLC) (25, 33–37), gastric cancer (25, 26, 37), renal cell carcinoma (25, 37), urothelial cell carcinoma (38) and hepatocellular carcinoma (28) treated with immunotherapy. However, whether irAEs have a predictive effect in CCA has not been reported. To our knowledge, this is the first evidence that irAEs are associated with better clinical outcomes in patients with advanced CCA receiving PD-1 inhibitors combination therapy. The results of this study showed that among CCA patients receiving PD-1 inhibitors combination therapy, the irAEs cohort had a higher DCR (90.6% vs 70.4%, P=0.047), longer OS (median OS: 21.2 vs 10.0 months, P<0.001) and PFS (median PFS: 9.0 vs 4.4 months, P=0.003) compared to the non-irAEs cohort.

There were statistically differences in total bilirubin levels and relapse between the irAEs and non-irAEs cohorts in the clinical and demographic characteristics of the CCA patients. To assess whether differences in total bilirubin levels and relapse between these two cohorts would affect outcomes, we further performed multivariate COX regression analysis. The results showed that total bilirubin levels did not affect OS and PFS in CCA patients (P=0.322 and 0.899, respectively). Relapse also had no effect on OS and PFS in CCA patients (P=0.783 and 0.804, respectively). Furthermore, irAEs were an independent prognostic factor for PFS in CCA patients (P=0.033), while irAEs and CEA levels were independent prognostic factors for OS (irAEs: P=0.001; CEA levels: P=0.003). The above results suggest that the occurrence of irAEs may predict better outcomes in patients with advanced CCA receiving PD-1 inhibitors combination therapy. Recently, a multicentre retrospective study (39) showed that a higher grade of initial checkpoint inhibitor-related pneumonitis (CIP) was associated with a higher relapse rate of CIP after rechallenge with ICIs in patients with advanced lung cancer. Therefore, we infer that advanced CCA patients with grade 1-2 irAEs may benefit from rechallenge with PD-1 inhibitors, and appropriate studies are needed to verify this inference.

However, the specific mechanisms to explain why irAEs are positively associated with immunotherapy are limited. Studies have shown that a higher BMI associated with improved PFS and OS and a higher incidence of irAEs in cancer patients receiving PD-1/PD-L1 inhibitors (40–42), suggesting that adipose tissue may play a key role in immunotherapy and irAEs. Taken together, the available evidence suggests that this may be related to an increased inflammatory environment due to obesity (42–46). Furthermore, a prospective study of dermatologic adverse events in NSCLC patients receiving PD-1 inhibitors found that tumor and skin tissues shared nine T-cell antigens, and it has been speculated that T cells targeting cancer cells may also target normal tissues with shared antigens (47), which may partially explain the correlation between irAEs and PD-1/PD-L1 inhibitor efficacy. It has also been shown that intestinal flora can modulate innate and acquired immunity in cancer patients in a variety of ways, further influencing the efficacy of PD-1/PD-L1 inhibitors (48–53). While irAEs may also be associated with intestinal flora, studies to date have been limited to CTLA-4 inhibitors. It was found that OS and PFS were improved in patients with Faecal Bacterium and other Firmicutes, but these patients were more likely to develop colitis (54). The specific roles of different microbial species are not yet known and further relevant prospective clinical trials are needed. Finally, high drug clearance (Clearance, CL) has also been suggested as a possible factor to explain the correlation between immunotherapy efficacy and irAEs. One study indicated that high CL was negatively associated with OS and PFS in cancer patients receiving pembrolizumab (P<0.001 and 0.003, respectively) (55). It is possible that high CL promotes the clearance of PD-1 inhibitors, leading to lower blood levels and possibly reduced efficacy. However, this study found no statistical difference in CL between patients with severe irAEs and those with mild irAEs (P = 0.70) (55). Although the CL of PD-1/PD-L1 inhibitors may help explain the correlation between irAEs and the therapeutic effect of PD-1/PD-L1 inhibitors, further evidence is needed.

This study also has some limitations. First, it is a retrospective cohort study, so there may be an inevitable bias for irAEs. Secondly, due to non-randomization, baseline imbalances in patients resulted in the presence of confounding variables. Although we corrected for this using multivariate analysis, validation is needed in prospective, randomized clinical trials. Thirdly, due to the low prevalence of CCA, the number of advanced CCA patients receiving PD-1 inhibitors combination therapy is limited. Therefore, the sample size in this study is small. Finally, the PD-L1 expression and MSI status of most of the patients enrolled were unknown, so we cannot draw any conclusions on the correlation between MSI status, PD-L1 expression and response/survival in advanced CCA patients. In the future, larger prospective studies are needed to validate this finding and identify molecular biomarkers.

5 Conclusion

Our study suggests for the first time that irAEs may predict higher DCR and longer OS and PFS in patients with advanced CCA receiving PD-1 inhibitors combination therapy. IrAEs may serve as an effective clinical marker to predict the efficacy of PD-1 inhibitors combination therapy in patients with advanced CCA.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Ethics statement

The studies involving human participants were reviewed and approved by the First Affiliated Hospital, Zhejiang University School of Medicine (Approval number: IIT20210916A). The patients/participants provided their written informed consent to participate in this study.

Author contributions

YZ and ZY designed this study. XW and YL collected the data of the participants. YZ, YH contributed to the data analysis. YZ, QZ, and ZY participated in the writing and revision of the manuscript. All authors approved the final version of the article and agreed to be responsible for all aspects of this study.

Funding

This study was supported by the Fund of Zhejiang Pharmaceutical Association (Grant number 2022ZYYL01) and the Fund of Zhejiang Medical Doctors Association (Grant number YS2022-3-010).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1141148/full#supplementary-material

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ORIGINAL RESEARCH
published: 27 March 2023
doi: 10.3389/fimmu.2023.1113455

Molecular subtype identification and signature construction based on Golgi apparatus-related genes for better prediction prognosis and immunotherapy response in hepatocellular carcinoma

Liang Sun ¹†, Zitao Liu ¹†, Zhengyi Wu ¹†, Ke Ning ², Junwen Hu ¹, Zhendong Chen ¹, Zhipeng Wu ¹ and Xiangbao Yin ¹*

¹ Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China, ² Department of Emergency, The First Affiliated Hospital of Nanchang University, Nanchang, China

Edited by:
Yunfei Xu, Shandong University, China

Reviewed by:
Anutosh Ganguly, University of Michigan, United States
Wenhao Ouyang, Sun Yat-Sen Memorial Hospital, China

*Correspondence:
Xiangbao Yin
294252736@qq.com

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 01 December 2022

Accepted: 15 March 2023

Published: 27 March 2023

Citation:
Sun L, Liu Z, Wu Z, Ning K, Hu J, Chen Z, Wu Z and Yin X (2023) Molecular subtype identification and signature construction based on Golgi apparatus-related genes for better prediction prognosis and immunotherapy response in hepatocellular carcinoma. Front. Immunol. 14:1113455. doi: 10.3389/fimmu.2023.1113455

Introduction

The Golgi apparatus (GA) is the center of protein and lipid synthesis and modification in normal cells and is involved in regulating various cellular process as a signaling hub, the dysfunction of which can lead to the development of various pathological conditions, including tumors. Mutations in Golgi apparatus-related genes (GARGs) are prevalent in most tumors, and their mutations can make them pro-tumor metastatic. The aim of this study was to analyze the predictive role of GARGs in the prognosis and immunotherapeutic outcome of hepatocellular carcinoma.

Methods

We used TCGA, GEO and ICGC databases to classify hepatocellular carcinoma samples into two molecular subtypes based on the expression of GARGs. Signature construction was then performed using GARGs, and signature genes were selected for expression validation and tumor phenotype experiments to determine the role of GARGs in the prognosis of hepatocellular carcinoma.

Results

Using the TCGA, GEO and ICGC databases, two major subtypes of molecular heterogeneity among hepatocellular carcinoma tumors were identified based on the expression of GARGs, C1 as a high-risk subtype (low survival) and C2 as a low-risk subtype (high survival). The high-risk subtype had lower StromalScore, ImmuneScore, ESTIMATEScore and higher TumorPurity, indicating poorer treatment outcome for ICI. Meanwhile, we constructed a new risk assessment profile for hepatocellular carcinoma based on GARGs, and we found that the high-risk group had a worse prognosis, a higher risk of immune escape, and a higher TP53 mutation rate. Meanwhile, TME analysis showed higher tumor purity TumorPurity and lower ESTIMATEScore, ImmuneScore and StromalScore in the high-risk group. We also found that the high-risk group responded more strongly to a variety of anticancer drugs, which is useful for guiding clinical drug use. Meanwhile, the expression of BSG was experimentally found to be associated with poor prognosis of HCC. After interfering with the expression of BSG in HCC cells SMMC-7721, the proliferation and migration ability of HCC cells were significantly restricted.

Discussion

The signature we constructed using GARGs can well predict the prognosis and immunotherapy effect of hepatocellular carcinoma, providing new ideas and strategies for the treatment of hepatocellular carcinoma.

Keywords: golgi apparatus, hepatocellular carcinoma, signature, prognosis, immunotherapy

1 Introduction

The treatment of hepatocellular carcinoma (HCC) is currently a major challenge worldwide, and despite the availability of multiple treatment options, treatment outcomes continue to fall short of expectations. Therefore, new breakthroughs and tools are needed to address the current treatment bottlenecks.

The Golgi apparatus (GA) has long served as a platform for sorting, modification, production and transport of proteins and lipids. In addition to these classical functions, studies have confirmed that the GA plays an important role in the regulation of many cellular processes, such as cell migration, apoptosis, inflammation, autophagy, and stress responses (1). The mechanism of action of intracellular GA is a finely regulated process, and dysregulation can lead to a variety of pathological conditions. Increasing evidence suggests that GA functions are closely related to cancer development and progression, including the regulation of cell proliferation, motility, metabolism and immune evasion (2, 3). The GA in cancer cells frequently exhibits functional and structural disorders associated with cancer development and progression (4, 5). In addition, the GA can also influence cellular metabolism and participate in processes that control the phenotype of cancer cells, including innate immune response, angiogenesis, tumor migration and invasion (6, 7). Moreover, Golgi apparatus-related genes (GARGs) have been found to be mutated at a high frequency in tumors, and mutated GARGs tends to enhance tumor metastasis and bring a poorer prognosis (8). It has also been shown that the chromosome 1q21-43 region (containing many Golgi-related functional genes) is frequently amplified in multiple cancer types (8).

The role of GARGs in tumors has been confirmed. Among them, GOLM1 can regulate EGFR/RTK and thus promote metastasis of HCC (9). In addition, GOLM1 can control colitis and colon tumorigenesis by regulating the balance of Notch signaling in the intestine (10). Golgi phosphoprotein 3 (GOLPH3) is involved in cell migration, GA morphology and orientation, and protein glycosylation, and the increased expression of GOLPH3 observed in a variety of tumors enhances cis-transport of GA and leads to increased cytosolic ejection of pro-metastatic factors such as cytokines, growth factors, and Wnt molecules (11, 12). In addition, GA can enhance the secretion of immune factors and promote the formation of an immunosuppressive tumor microenvironment that promotes tumor progression and metastasis. For example, PI4KIIβ promotes the migration of myeloid-derived suppressor cells, a key immunosuppressive cell type associated with metastasis, by mediating the cytokine of immune modifying molecules (CXCL1, IL-1α, IL-8 and VEGF) in lung adenocarcinoma cells (13). GOLPH3 interacts with cytoskeleton-associated protein 4 localized in the extracellular body and is able to enhance the secretion of Wnt3a, thereby limiting T cell differentiation (14).

Therefore, GARGs play an important role in tumor development and immune effects, which is a new direction to be explored. In this study, a prognostic and immune response system based on GARGs for hepatocellular carcinoma was constructed by bioinformatics analysis using public databases to provide guidance for clinical treatment of hepatocellular carcinoma.

2 Materials and methods

2.1 Data processing

Search and download the gene set “GOCC_GOLGI_APPARATUS” through Gene Set Enrichment Analysis (GSEA) MSigDB database (http://www.gsea-msigdb.org/gsea/msigdb/index.jsp).

The mRNA expression data and clinical information of samples from patients with HCC were downloaded from the TCGA, GEO and ICGC databases, of which the TCGA database included 374 HCC samples and 50 normal liver samples, and 365 HCC samples were retained after screening (excluding HCC samples with survival time of 0). the GEO database (GSE76427 and GSE14520) obtained data for 115 and 219 HCC samples, respectively. Thus, TCGA-LIHC, GSE76427 and GSE14520 had a total of 699 HCC samples. the ICGC database (ICGC-LIRI-JP) excluded blood samples and samples with metastatic tumors, and finally retained data for 208 HCC samples.

2.2 Gene screening

Valuable GARGs were screened for further analysis using the TCGA database. First, we used the weighted gene co-expression network analysis (WGCNA) package to screen out the most relevant modules for HCC, and extracted the genes from the modules for subsequent analysis. Second, differential expression analysis was performed for normal and tumor samples of TCGA. We used the “limma” R package and Wilcoxon test to screen Golgi apparatus-related differentially expressed genes between normal and HCC samples in the TCGA cohort (FDR< 0.001, |logFC|> 1). Third, COX analysis of GARGs was performed to obtain prognosis-related genes. Finally, the overlapping genes of the three were screened for the next step of analysis.

2.3 Identification of GARGs molecular subtypes by consensus clustering

First, we merged the HCC sample data from the three datasets TCGA-LIHC, GSE76427 and GSE14520. TCGA-LIHC data (FPKM normalized) were converted to Transcripts Per Kilobase per Million mapped reads (TPM). For the microarray cohort, the normalized matrix files of expression data and clinical information were downloaded directly and log-transformed, GSE76427 and GSE14520 using the “sva” R package to eliminate batch effects (15). Then batch correct and normalize the three data sets (Figures S1A, B).

Then, we used the R package “ConsensusClusterPlus” to perform consensus clustering analysis on the screened overlapping gene-related molecular subtypes. Next, we used the R packages “survival” and “survminer” to investigate the relationship between subtypes and OS. Principal component analysis was used to identify whether the two subtypes were able to distinguish well between HCC samples. The R package “pheatmap” was used to show the relationship between molecular subtypes of GARGs and clinicopathological characteristics (sex, age and Stage).

2.4 Gene set variation analysis and single sample gene set enrichment analysis

Gene set variation analysis (GSVA) was performed using the KEGG gene set (c2.cp.kegg.v7.5.1.symbols) to assess pathway differences across molecular isoforms. Biosignature gene sets were obtained from the Hallmarker gene set. Single sample gene set enrichment analysis (ssGSEA) was used to assess the relative activity of biological pathways between different isoforms.

2.5 Analysis of tumor microenvironment among molecular subtypes

TME is a dynamic system that undergoes specific modifications during the development of cancer and eventually leads to metastasis and diffusion, which is still the main cause of death of cancer patients (16). Tumorigenesis is a complex and dynamic process. TME includes intercellular compartments composed of stromal fibroblasts, infiltrating immune cells, blood and lymphatic networks, and non-cellular components including ECM (17). We analyzed the characteristics of the two molecular subtypes by comparing the content of non-tumor cell components and tumor purity between the two molecular subtypes. Understanding the interactions between different immune cells and between immune cells and stromal cells (such as hematopoietic stem cells or CAFs) will be crucial to the therapeutic development of tumor microenvironment.

2.6 Construct and verification of GARGs signature

To further represent the prognostic value of GARGs in HCC, we constructed a risk score system constructed from GARGs using the TCGA database and GEO database to predict prognosis and immunotherapeutic response in hepatocytes. We constructed a signature using Cox regression analysis and lasso penalty method using previous overlapping genes to obtain a risk scoring system based on GARGs: , The samples were divided into two groups (high-risk and low-risk) based on the median value of risk scores, and then survival analysis, principal component analysis (PCA), and ROC curve plotting were performed, followed by nomograms to further determine the prognosis of patients. Also, to verify the reliability of the signature, we validated the signature using an external dataset ICGC database to determine its accuracy and stability.

2.7 Quantitative real-time PCR

After obtaining informed consent from patients, we collected 20 pairs of HCC tissues and paraneoplastic tissues (from the Second Affiliated Hospital of Nanchang University), then cultured one normal hepatocyte line (HL-7702) and four HCC cell lines (HCCLM3, MHCC97H, HepG2 and SMMC-7721), and after extracting RNA from the tissues and cells, q-PCR experiments were performed to verify the signature genes.

2.8 Immunohistochemistry experiments

Immunohistochemistry (IHC) experiments was used to detect protein expression differences: HCC tissues were paraffin-embedded, sectioned, dewaxed and hydrated, incubated with anti-trait gene antibodies overnight at room temperature, then labeled with secondary antibodies for 30 minutes, stained and photographed.

2.9 Immunofluorescence

We inoculated 2000 SMMC-7721 cells onto a 24-well plate. After 24-36 hours, wash them with PBS three times, and then fix them with 4% paraformaldehyde for 20 minutes. At room temperature, permeate the plate with 0.5% Triton X-100 for 20 minutes, and then seal it with goat serum. We used BSG antibody (1:100, Abbart, T59564), KIF20A antibody (1:200, Abbart, PU602304), RNF24 antibody (1:1000, Abbart, PH9881) and GM130 antibody (1:2000, Protentech, 11308-1-AP) to incubate overnight. The labeled antibodies of different colors were incubated at room temperature for 1 hour, and then re-stained with DAPI. Finally, fluorescence microscope is used for imaging.

2.10 Cell proliferation assay

EdU assay: Inoculate SMMC-7721 cells with 1*10⁴/well in 96-well plate, and after 24 hours, Each well was incubated with 100 ul of 50 uM EdU (YF ® 594 Click-iT EdU imaging kit, UELANDY) for 2 hours, and then fixed with 4% paraformaldehyde for 30 minutes. Use 2 mg/ml glycine for another 5 minutes. After washing with PBS twice, the cells were mixed with 1 × The Apollo staining solution was incubated for 30 minutes. Discard the dye solution and wash twice with PBS containing 0.5% TritonX-100. Join 1 × Hoechest 33342 was incubated at room temperature for 30 minutes. After washing with PBS, observe the positive cells by fluorescence microscope.

2.11 Wound healing assay

Inoculate cells into six orifice plates and use 200 μl disposable gun head forms wound line. When the cell grows to about 80% - 90%, draw lines on the cell surface. Wash the plate twice with PBS to remove suspended cells. The wound closure images were taken at 0, 24 and 48 hours.

2.12 Transwell assay

Cells were inoculated into the upper cavity of 200ul serum-free medium (1.5*10⁴/well). Add 600ul of medium containing 10% serum into the lower chamber. After 24 hours, the cells were fixed and stained with crystal violet, and then the migrated cells were counted under the inverted optical microscope.

2.13 Immune infiltration analysis and immune function analysis

Correlations between signature genes and 22 immune cells were analyzed using the CIBERSORT algorithm, while differences in the abundance of immune cells between high and low risk groups were analyzed to distinguish features between the two risk groups.

2.14 Immune escape and drug sensitivity analysis

We evaluated the Tumor Immune Dysfunction and Exclusion (TIDE) scores between the risk groups to determine whether there were differences in the effects of immunotherapy between the risk groups. The IC50 values of chemotherapeutic drugs were also calculated by the “PRROPHIC” R package to assess the sensitivity of the different risk groups to chemotherapeutic drugs.

2.15 Statistical analysis

R language (Version 4.1.2) and GraphPad Prism 8.0 are used for statistical analysis. The t-test was used for two groups of analysis. P<0.05 means statistically significant.

3 Results

3.1 WGCNA

We extracted 1659 GARGs from the GSEA database (GOCC_GOLGI_APPARATUS). we used TCGA-LIHC data for differential module analysis by WGCNA algorithm to screen out key genes closely related to HCC, and finally obtained four modules, among which the Meblue module was the most correlated and significantly different, so GARGs in the Meblue module was selected for the subsequent analysis (Figure 1).

Figure 1 | Weighted gene co-expression network analysis. (A) Clustering dendrogram of genes, with dissimilarity based on topological overlap, together with assigned module colors. (B) Visual representation of the weight relationship of modular genes in an eigengene network. (C) Module-trait associations: Each row corresponds to a module eigengene and the column to the tumor and normal. Each cell contains the corresponding correlation and p-value. (D) Correlation graph of genes and tumors in the blue module.

3.2 Differential expression and prognostic of GARGs in TCGA-LIHC

First, using TCGA-LIHC data, we screened 50 normal liver tissues and 374 HCC tissues for differentially expressed genes (DEGs) (|logFC|>1, FDR<0.001), and obtained a total of 242 differentially expressed genes (62 down-regulated and 180 up-regulated) (Table S1; Figures 2A, B). GO and KEGG enrichment analyses were performed on these DEGs. the GO enrichment results showed that GARGs were associated with Golgi apparatus subcompartment, trans-Golgi network, Golgi lumen, Golgi stack and cis-Golgi network. KEGG showed that DEGs were mainly associated with Glycosphingolipid biosynthesis-lacto and neolacto series, Glycosphingolipid biosynthesis-globo and isoglobo series, Glycosaminoglycan biosynthesis-keratan sulfate and Sphingolipid metabolism (Figures 2C, D). Then, 672 GARGs associated with prognosis were screened using a one-way COX analysis (Table S2).

Figure 2 | Identification of differentially expressed GARGs in HCC. (A) The heatmap. (B) The volcano map. (C) Gene ontology (GO) analysis of differentially expressed GARGs. (D) Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed GARGs.

3.3 Screening for core genes

Based on the WGCNA algorithm, differential expression analysis and prognostic analysis, three groups of genes were obtained for screening, respectively, and we overlapped the three to screen 30 GARGs for subsequent analysis (Figure 3A). We extracted the expression data of these 30 genes from the combined mRNA expression data of TCGA-LIHC, GSE76427 and GSE14520, and finally 21 genes were extracted (some genes were not included in all three datasets at the same time) (Table S3). Then, the mutation status and copy number changes of these 21 genes were analyzed using TCGA database information (Figures 3B–D).

Figure 3 | Screening of major GARGs and their Copy Number Variation and Mutations. (A) DEGs, prognostic genes and Meblue module genes were intersected by a Venn diagram. (B) The CNV variation frequency of GARGs in TCGA-LIHC. (C) The location of CNV alteration of GARGs on 23 chromosomes in TCGA-LIHC. (D) Mutation frequency of GARGs in TCGA-LIHC.

3.4 Identification of GARGs molecular subtypes

After combining the information of three data sets, based on the expression amount of these 21 genes, we determined two different HCC subtypes (C1 and C2) through consensus cluster analysis, and the two subtypes can be well distinguished (Figures 4A, B). The survival analysis shows that the survival rate of patients with C1 is worse (Figure 4C), so we call cluster C1 “high-risk type” and cluster C2 “low-risk type”. The expression of 21 GARGs was significantly different in the two subtypes, and there were significant differences in tumor stage, sex and age between the two subtypes (P<0.05) (Figure 4D).

Figure 4 | Identification of tumor subtypes based on the GARGs. (A) Two clusters were identified according to the best consensus matrix (k=2). (B) Principal Component Analysis (PCA) demonstrates the degree of differentiation between the two subtypes. (C) The overall survival between the two subtypes. (D) Comparison of GARGs expression and clinical characteristics between the two subtypes. (E) Gene Set Variation Analysis (GSVA) was performed to analyze the differences between the two subtypes. (F) The immune cell infiltration between the two subtypes was analyzed by the ssGSEA. (G) TumorPurity between the two subtypes. (H) ESTIMATEScore between the two subtypes. (I) ImmuneScore between the two subtypes. (J) StromalScore between the two subtypes. *P<0.05, **P<0.01, ***P<0.001.

To further investigate the biological behavior between these two isoforms, the differences between the two isoforms were analyzed using Gene Set Variation Analysis (GSVA) and the “limma” package in 20 pathways, where the low-risk group was mainly expressed in KEGG_ HEMATOPOIETIC_ CELL_ LINEAG, KEGG_ COMPLEMENT_ AND_ COAGULATION_ CASCADES and KEGG_ NEUROACTIVE_ LIGAND_ RECEPTOR_ INTERACTION (Figure 4E). Meanwhile, the immune cell infiltration between the two subtypes was analyzed by the ssGSEA, and the results showed that cluster C2 had significantly higher immune infiltration in Activated CD8 T cell, Eosinophilna, Gamma delta T cell, MDSCna, Neutrophila, Plasmacytoid dendritic cell, Regulatory T cell, T follicular helper cell and Type 2 T helper cell. In contrast, immune infiltration was higher in Activated dendritic cellna, CD56bright natural killer cellna, Natural killer T cellna and Type 1 T helper cellna in cluster C1 (Figure 4F).

To further typify the characteristics and tumor microenvironment between the two subtypes, we evaluated ImmuneScore, StromalScore, ESTIMATEScore and TumorPurity between the two subtypes using the ESTIMATE method, which showed that the high-risk group had higher tumor purity and lower immune and stromal cell content (Figures 4G–J).

3.5 Construction of a prognostic signature based on GARGs

To further analyze the value of GARGs in the prognosis and immune response of HCC, we constructed a prognostic signature of HCC based on these 21 genes. First, 699 HCC samples were randomly divided into two groups (training set (set1) and test set (set2)) by 1:1, and the total samples were used as test set (set3). A prognostic signature consisting of three GARGs was built by LASSO-COX analysis in the training set (Figures 5A–C).

Figure 5 | LASSO-COX regression to identify signature genes in the TCGA-Train set. (A) Forest plot of prognostic GARGs in the train set. (B) Cross-validation of the LASSO regression. (C) Coefficient value of prognostic genes. (D) Survival analysis between the high and low risk groups. (E) Receiver operating characteristic curve (ROC) of risk score. (F) Distribution of risk scores and survival outcomes.

The risk score was calculated for each patient according to the formula. The median was used to divide the sample into high-risk and low-risk groups, and in the training set, survival was significantly lower in the high-risk group (p<0.001) (Figure 5D). the ROC curve assessed the accuracy of the signature, with AUC values of 0.702, 0.662, and 0.642 at 1, 2, and 3 years, respectively (Figure 5E). And as the risk score increased, the survival time gradually decreased and the number of deaths gradually increased (Figure 5F).

3.6 Validation of prognostic signature

We use the test set (set2) and the total data set (set3) to test the accuracy of the model. Similarly, after calculating the risk score of each sample, we can divide it into high and low risk groups according to the median value. The results of KM survival analysis showed that the survival rates of the high-risk groups in set2 and set3 were lower (P<0.05) (Figures 6A, D). AUC values assessed by ROC curve in set2 are 0.673, 0.636 and 0.627 respectively in 1, 2 and 3 years (Figure 6B). AUC values assessed by ROC curve in set3 are 0.689, 0.648 and 0.629 respectively in 1, 2 and 3 years (Figure 6E). In addition, the number of deaths gradually increased with the increase of risk score (Figures 6C, F).

Figure 6 | Distribution of survival analysis, ROC and risk scores and survival outcomes for the validation set. (A) Survival analysis between the high and low risk groups in TCGA-test. (B) Receiver operating characteristic curve (ROC) of risk score in TCGA-test. (C) Distribution of risk scores and survival outcomes in TCGA-test. (D) Survival analysis between the high and low risk groups in TCGA-sum. (E) Receiver operating characteristic curve (ROC) of risk score in TCGA-sum. (F) Distribution of risk scores and survival outcomes in TCGA-sum. (G) Survival analysis between the high and low risk groups in ICGC. (H) Receiver operating characteristic curve (ROC) of risk score in ICGC. (I) Distribution of risk scores and survival outcomes in ICGC.

To further verify the accuracy of the signature, we use the data of ICGC-LIRI-JP queue as external verification data to verify the risk characteristics. Similarly, the cohort was divided into high-risk group and low-risk group according to the median risk score. The results showed that patients with higher risk also had worse survival outcomes (P<0.05) (Figure 6G). AUC values assessed by ROC curve in set3 are 0.776, 0.713 and 0.717 respectively in 1, 2 and 3 years (Figure 6H). Similarly, as the risk score went up, the number of deaths gradually increased (Figure 6I). Meanwhile, we used IMvigor database to verify that there was a significant difference between the immunotherapy response group and the non-response group (Figure S2).

Also, the results of the t-distributed Stochastic Neighbor Embedding (t-SNE) and Principal Component Analysis (PCA) analyses demonstrate the discriminative power of the signature (Figure S3).

3.7 Expression validation and epigenetic analysis of signature genes

At the RNA level, we verified the expression of the signature genes in normal hepatocytes (7702) and hepatocellular carcinoma cells (HCCLM3, MHCC97H, HepG2 and SMMC-7721) by q-PCR assay (Figures 7A–C). In addition, we collected 20 pairs of HCC tissues and their paraneoplastic tissues to detect the mRNA expression of the signature genes (Figures 7D–F), and the primer sequences are shown in Table 1. At the protein level, we detected the difference in the expression of the signature genes in three pairs of HCC tissues and paraneoplastic tissues by immunohistochemical assay, and the results were consistent with the results at the RNA level, and the signature genes were expressed in HCC tissues higher (Figure 8).

Figure 7 | Validation of differential expression of 3 signature genes in cells and tissues by q-PCR. (A–C) Differential mRNA expression of 3 signature genes in HL-7702, HCCLM3, MHCC-97H, HepG2 and SMMC-7721 cells. (D–F) Differential mRNA expression of 3 signature genes in HCC tissue and paraneoplastic tissue (20 pairs). **<0.01, *** <0.001.

Table 1 | Primer sequences used for RT-qPCR.

Figure 8 | Immunohistochemistry of 3 signature genes in three pairs of HCC tissues and paraneoplastic tissues.

Since the signature genes are closely linked to the function of the Golgi apparatus, to further verify the localization of these genes, we performed immunofluorescence experiments. We used the Golgi apparatus marker gene GM130 as a control, and the results showed that GM130 was localized in the cytoplasm and the localization of the three signature genes were also predominantly present in the cytoplasm, consistent with the results of our analysis (Figure 9).

Figure 9 | Golgi apparatus marker gene GM130 was used as a reference. Immunofluorescence experiments determined the localization of the signature genes in SMMC-7721 cell.

We analyzed the methylation levels and copy number variation of the signature genes in LIHC using the GSCA database. The results showed that the higher the expression of the three genes, the lower the level of methylation. Meanwhile, the copy number variation and survival of the signature genes were presented (Figure S4).

3.8 Independent prognostic analysis and construction of nomograms

We performed univariate and multivariate analyses of risk scores and clinical data, which showed that clinical stage and risk scores were significantly associated with patient prognosis (P<0.05) in both univariate and multivariate analyses (Figures 10A, B). Then, we presented the signature genes with clinical characteristics as heat maps and found significant differences in Stage staging and gender between high- and low-risk groups (P<0.05) (Figure 10C). To further evaluate individual patients, we simplified the statistical prediction signature with Nomograms (Figure 10D). calibration plots of survival probabilities at 1, 2, and 3 years also showed good agreement between bar graph predictions and actual observations (Figure 10E).

Figure 10 | Independent prognostic analysis and Construction and validation of the Nomograms. (A) Univariate cox regression analysis for the TCGA cohort. (B) Multivariate cox regression analysis for the TCGA cohort. (C) The correlations between the GARGs and clinicopathologic characters of the high-risk group and low-risk group were shown as a heatmap. (D) Construction of the Nomograms. (E) The calibration curves displayed the accuracy of the nomogram in the 1-, 2- and 3 years.

3.9 BSG expression was associated with poor prognosis of HCC

We selected the signature gene BSG for the prognosis experiment of HCC. First, we evaluated the expression level of BSG in HCC cell lines. The results showed that BSG was highly expressed in HCCLM3, SMMC-7721 and Huh7 than HL-7702 (normal liver cell) (Figures 11A, B). Next, we used SMMC-7721, which has the highest level of BSG mRNA expression. Then, We used Western blot to verify the knockdown efficiency (Figures 11C, D).

Figure 11 | Evaluate the expression level of BSG in HCC cell line, and then verify the intervention efficiency. (A) Compared with HL-7702(normal liver cell), BSG is generally highly expressed in tumor cells and has the highest expression in SMMC7721. (B) Differential protein expression of BSG between hepatocytes and hepatoma cells. (C) Verification of intervention efficiency through western blot. (D) The expression of BSG protein was significantly reduced in SMMC-7721 cells after the intervention of BSG expression. **P value<0.01; ***P value<0.001; ns, Not Significant.

To assess the effect of BSG on proliferation in HCC, we used EdU assays in SMMC-7721. The results showed that after interfering with the expression of BSG in SMMC7721 cell, the cell proliferation rate in the si-BSG group was significantly lower than that of si-NC group (Figures 12A, B). At the same time, the effect of inhibiting BSG expression on the migration of HCC cells was further analyzed. Transwell assay and wound healing assay showed that inhibition the expression of BSG could significantly inhibit the migration ability of HCC cells (Figures 12C–F).

Figure 12 | The expression level of BSG is related to the poor prognosis of HCC. (A, B) EdU assay showed that the cell proliferation rate of si-BSG group was significantly lower than that of si-NC group. (C, D) Transwell assay showed that inhibiting the expression of BSG could significantly inhibit the migration ability of HCC cells. (E, F) The wound healing assay showed that the migration ability of HCC cells were significantly inhibited after the intervention of BSG expression. **<0.01, *** <0.001.

3.10 Immune cell infiltration analysis

The CIBERSORT algorithm was used to calculate the immune cell infiltration between the high and low risk groups, and the results showed that Plasma cells, T cells CD4 memory activated, Macrophages M1 and Mast cells activated were significantly higher in the high-risk group, while T cells CD4 memory resting, T cells regulatory (Tregs) Dendritic cells activated, Mast cells resting and Eosinophils were higher in the low-risk group (Table S4) (Figure 13A). Also, we analyzed the correlation between the signature genes and immune cells (Figure 13B).

Figure 13 | Immune infiltration and tumor microenvironment analysis between high-risk and low-risk groups. (A) The CIBERSORT algorithm was used to calculate the immune cell infiltration between the high and low risk groups. (B) The correlation between the signature genes and immune cells. (C) TumorPurity between the two risk groups. (D) ESTIMATEScore between the two risk groups. (E) ImmuneScore between the two risk groups. (F) StromalScore between the two risk groups. (G) Comparison of the scores of TIDE between the high and low risk group. *<0.05; ns, Not Significant.

3.11 Tumor microenvironment and immunotherapy analysis

Similarly, we assessed the ImmuneScore, StromalScore, ESTIMATEScore and TumorPurity levels between the two risk groups using the ESTIMATE method, and the results showed that the tumor purity was higher and the non-tumor components were lower in the high-risk group (Figures 13C–F). To compare the differences in immunotherapy between the two risk groups, we analyzed the risk of immune escape between the two groups, and the results showed that the high-risk group had less risk of immune escape and might respond better to immunotherapy (Figure 13G). We also performed an analysis of risk score versus drug sensitivity, and the results showed that the high-risk group was more sensitive to Gemcitabine, Sorafenib and Sunitinib and may have better treatment outcomes. Correlation analysis showed that the higher the risk score, the higher the sensitivity to these three drugs (Figures 14A–F).

Figure 14 | Drug sensitivity analysis. (A–C) Comparison of the Gemcitabine, Sorafenib and Sunitinib (IC50) between the two groups. (D–F) The relationship between risk score and Gemcitabine, Sorafenib and Sunitinib (IC50) sensitivity.

4 Discussion

HCC is one of the most common malignancies worldwide and its morbidity and mortality are on the rise (18, 19). Surgery remains the most successful treatment modality available, but most HCC usually occurs in the setting of cirrhosis, where the postoperative residual liver has poor hepatic function while still maintaining a tumor-prone microenvironmental state. The possibility of cure exists through radiofrequency ablation and liver transplantation, but requires diagnosis and intervention at a sufficiently early stage. In this context, the advent of immune checkpoint inhibitors (ICIs), tyrosine kinase inhibitors (TKIs) and monoclonal antibodies has expanded the therapeutic field for HCC. Immunotherapy is currently a major hotspot in the treatment of HCC, and understanding the interplay between oncogenic pathways and immune responses is essential to improve current and future therapeutic outcomes.

The pathogenic role of organelles in tumors has received increasing attention, and the Golgi apparatus complex is a highly dynamic organelle that is considered the “heart” of intracellular transport. In the last decade, a growing number of studies have reported structural alterations of the Golgi apparatus associated with diseases, dysregulation and infection. These include stress responses (20), cancer (21) and various infections. The mammalian GA is the center of glycosylation and changes in GA structure will lead to reordering of glycosyltransferases, resulting in specific glycosylation epitopes that lead to tumorigenesis and development (22). In addition, GA membrane dynamics are triggered by the actin cytoskeleton and associated unconventional myosin (23). In many cases, upregulation of GA associated myosin motility is associated with aggressive cancers (24, 25). Meanwhile, myosin 18 is able to coordinate directly with GA morphology by interacting with Golgi phosphorylated protein 3 (GOLPH3), and their linkage triggers Golgi proliferation (26). In turn, the synergistic GOLPH3-myosin 18a relationship is necessary for DNA damage to induce Golgi fragmentation, which itself is a prerequisite for most mutations and cancers (27).

Due to the important role of the GA in tumor development, this study utilized bioinformatics analysis techniques based on the study of GARGs to identify two distinct molecular subtypes that exhibit significantly different immune cell infiltration and survival outcomes by comprehensive mining of publicly available HCC transcriptional data. Meanwhile, a risk score signature consisting of three GARGs was constructed using these key GARGs to predict the prognosis and immunotherapy response in HCC patients. The C2 cluster and low risk score groups in this study exhibited abundant immune cell infiltration and more non-tumor cell components, along with lower tumor purity, characteristics that corresponded to better survival outcomes. Meanwhile, the high-risk group exhibited a lower risk of immune escape and higher sensitivity to immunotherapeutic agents, findings that provide new perspectives for improving patient prognosis and risk-stratified treatment strategies by considering TME characteristics and transcriptomics.

Among the three genes that constitute the prognostic signature, RNF24 is considered as one of the risk factors for human oral squamous cell carcinoma (28). Moreover, RNF24 expression is more than 2-fold upregulated in esophageal adenocarcinoma patients compared to normal subjects (29). BSG (CD147) is a multifunctional protein involved in cancer cell survival and controls lactate transport mainly through interaction with monocarboxylate transporter proteins (MCT) such as MCT1 (30). It was shown that CD147 receptor is essential for TFF3-mediated signaling that regulates colorectal cancer progression (31). In addition, CD147 is able to promote collective invasion of hepatocellular carcinoma cells through upregulation of histone B expression (32). kIF20A (kinesin family member 20A), was shown to promote proliferation and metastasis of bladder cancer cells, and bladder cancer patients with high expression of KIF20A had poorer tumor differentiation and poorer prognosis (33). Moreover, overexpression of KIF20A can promote tumor proliferation and invasion in renal clear cell carcinoma and is associated with poor clinical outcome (34). In our study, a nomogram was constructed to calculate a score representing the OS of HCC patients through a risk scoring system composed of these three genes. The calibration plots showed that the signature had a better fitting curve, better clinical application than the traditional staging system, and was able to predict the prognosis of HCC patients more individually. These three GARGs have been studied and identified to have regulatory roles in tumors, thus providing higher value for our signature to predict prognosis and immunotherapeutic response in hepatocellular carcinoma.

5 Conclusion

In summary, we have systematically explored for the first time the value of GARGs in the prognosis and immunotherapy of patients with HCC, providing a new direction and therapeutic target for the treatment of HCC.

Data availability statement

Publicly available datasets were analyzed in this study, the names of the repositories/accession numbers are listed within the article/Supplementary Material.

Ethics statement

The studies involving human participants were reviewed and approved by the Ethics Committee of the Second Affiliated Hospital of Nanchang University. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by the Ethics Committee of the Second Affiliated Hospital of Nanchang University.

Author contributions

LS, ZL and ZheW designed the study and performed the experiments and wrote the manuscript. KN, JH, ZC and ZhiW assisted with specimen collection and bioinformatics analysis. XY reviewed the article and made revisions. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the National Natural Science Foundation of China (81760439).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1113455/full#supplementary-material

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ORIGINAL RESEARCH
published: 28 March 2023
doi: 10.3389/fimmu.2023.1116057

Chromosome 11q13 amplification correlates with poor response and prognosis to PD-1 blockade in unresectable hepatocellular carcinoma

Kai Yan ¹†, Ding Zhang ²†, Yanan Chen ²†, Wenfeng Lu ¹, Mengli Huang ², Jinping Cai ², Shiqing Chen ², Ting Bei ², Yuezong Bai ², Jian Lv ³, Yong Fu ¹* and Haibin Zhang ¹*

¹ Department of Hepatic Surgery (V), The Third Affiliated Hospital of Naval Medical University, Shanghai, China, ² The Medical Department, 3D Medicines Inc., Shanghai, China, ³ Department of Thoracic Surgery, Changzheng Hospital, Shanghai, China

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Hao Cai, Shanghai Jiao Tong University, China
Deshui Jia, Shanghai General Hospital, China
Yusuke Yamamoto, National Cancer Centre, Japan

*Correspondence:
Haibin Zhang
drzhanghb@163.com
Yong Fu
fuyg1982@163.com

†These authors have contributed equally to this work

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 05 December 2022

Accepted: 13 March 2023

Published: 28 March 2023

Citation:
Yan K, Zhang D, Chen Y, Lu W, Huang M, Cai J, Chen S, Bei T, Bai Y, Lv J, Fu Y and Zhang H (2023) Chromosome 11q13 amplification correlates with poor response and prognosis to PD-1 blockade in unresectable hepatocellular carcinoma. Front. Immunol. 14:1116057. doi: 10.3389/fimmu.2023.1116057

Background & aims

Little is known about molecular biomarkers that predict the response and prognosis in unresectable hepatocellular carcinoma (HCC) treated with programmed death (PD)-1 inhibitors.

Methods

A total of 62 HCC patients who underwent next-generation sequencing were retrospectively included in our department for this study. Patients with unresectable disease were subjected to systemic therapy. PD-1 inhibitors intervention (PD-1Ab) group and nonPD-1Ab group included 20 and 13 patients, respectively. Primary resistance was defined as initial on-treatment progression or progression with an initial stable disease of less than 6 months.

Results

Chromosome 11q13 amplification (Amp11q13) was the most common copy number variation in our cohort. Fifteen (24.2%) patients harbored Amp11q13 in our dataset. Patients with Amp11q13 showed higher level of Des-γ-carboxy-prothrombin (DCP), tumor number and were more prone to be combined with portal vein tumor thrombosis (PVTT). In the PD-1Ab group, the proportion of progressive disease (PD) in patients with Amp11q13 was significantly higher than that in patients with nonAmp11q13 (100% vs 33.3%, P=0.03). In the nonPD-1Ab group, the proportion of PD in patients with Amp11q13 and nonAmp11q13 had no significant difference (0% vs 11.1%, P>0.99). In the PD-1Ab group, the median progression-free survival (PFS) was 1.5 months in Amp11q13 patients vs 16.2 months in non-Amp11q13 patients (HR, 0.05; 95% CI 0.01-0.45; P = 0.0003). No significant difference was observed in the nonPD-1Ab group. Notably, we found that hyperprogressive disease (HPD) might be associated with Amp11q13. The increased density of Foxp3+ Treg cells in HCC patients with Amp11q13 might be one of potential mechanisms.

Conclusion

HCC patients with Amp11q13 are less likely to benefit from PD-1 blockade therapies. These findings may help guide the use of immunotherapy for HCC in routine clinical practice.

Keywords: hyperprogressive disease, hepatocellular carcinoma, programmed cell death protein-1, chromosome 11q13 amplification, next-generation sequencing, multiplex immunofluorescence

Introduction

Hepatocellular carcinoma (HCC) is one of the most common malignancies with a poor prognosis, which ranks as the fourth leading cause of cancer-related mortality worldwide (1). Chronic alcohol consumption, diabetes, non-alcoholic steatohepatitis related to obesity, and hepatitis virus infections are among the primary risk factors associated with the development of HCC (2). Overall, the prognosis of HCC is generally poor, with a five-year survival rate of less than 20% (3). For patients with early-stage HCC, hepatic resection and transplantation have become the mainstay curative treatments (4) However, most patients with HCC are diagnosed with advanced-stage or unresectable diseases (5). Recently, systemic therapies, including immune checkpoint inhibitors (ICIs), tyrosine kinase inhibitors (TKIs), and vascular endothelial growth factor/vascular endothelial growth factor receptor (VEGF/VEGFR) monoclonal antibodies, have challenged the treatment landscape of unresectable HCC (uHCC) with remarkable survival benefits. Specifically, ICIs represented by programmed cell death protein 1 (PD-1)/programmed cell death protein ligand 1 (PD-L1) blockade were developed to reinvigorate exhausted T cells in the tumor microenvironment (6). The objective response rate (ORR) of PD-1 antibody monotherapy was reported to be in the range of 18-20% (7, 8). The combination of ICIs with TKIs or VEGF antibodies further improved the treatment efficacy (9, 10). For instance, the combinational usage of atezolizumab (a PD-L1 inhibitor) and bevacizumab has become the standard of care with a median overall survival (OS) of 19.2 months (11).

Compared to other treatment approaches, immunotherapies have demonstrated the ability to generate responses that are rapid and durable in some patients (12). Currently, there is no reliable biomarker for predicting the efficacy of immunotherapy for HCC, despite the widespread use of PD-L1 expression as a marker in several tumor types (13). In HCC, there is no consistent conclusion regarding the predictive value of PD-L1 expression. CheckMate-459, KEYNOTE-224, and IMbrave150 studies showed higher response rates for PD-L1 positive advanced HCC patients treated with immunotherapy, while the CheckMate-040 study showed no significant difference between PD-L1 positive and negative patients treated with nivolumab (11, 14). The potential predictive value of tumor mutational burden (TMB), microsatellite instability (MSI), and gut microbiota in the immunotherapy of HCC needs further investigation (15, 16).

Although PD-1 monotherapy has significantly improved the landscape of hepatocellular carcinoma treatment, a large proportion of patients do not respond to treatment, or develop progression after a variable period of benefit. Resistance to ICIs stems from both cancer cell intrinsic and extrinsic factors, including impaired antigen presentation, overexpression of inhibitory immune checkpoints, abrogation of the interferon-gamma signaling pathway, and recruitment of immunosuppressive cells (17). Somatic genetic factors also play vital roles in the response of ICIs. In patients with non-cutaneous melanoma, genetic aberrations in the cyclin-dependent kinase 4 (CDK4) pathway were shown to be associated with innate resistance to PD-1 blockade (18). Cyclin D1 (CCND1) amplification was also associated with a poor prognosis in patients receiving ICIs, even those with a high TMB (19). For patients with HCC, genetic aberrations such as Wnt/β-Catenin mutations and lower immune scores may contribute to the resistance in immunotherapy (20, 21). Nevertheless, the molecular profiles of HCC patients with the resistance to PD-1 blockade have not been fully characterized.

Hyperprogressive disease (HPD), a special form of primary resistance characterized by paradoxically accelerated progression after immunotherapy, has been highlighted by several publications (22, 23). It is assessed by calculating the change in tumor growth dynamics. Depending on the criteria used to define HPD, the incidence of HPD ranges from 4% to 29% (24). Due to the rapid deterioration of clinical status, HPD patients often have limited opportunities to receive other therapies. Several biomarkers have been proposed to predict HPD following immunotherapy, but large-scale validation is needed. However, some debates that HPD may reflect merely the natural course of disease in a subset of patients (25).

In this study, based on genetic profiling, we sought to identify genetic biomarkers to predict the response to PD-1 blockade. Our data suggested that the amplification of the chromosome 11q13 locus, including CCND1, fibroblast growth factor 3 (FGF3), FGF4 and FGF19, might predict poor response and prognosis to PD-1 blockade in uHCC.

Patients and methods

Patients and samples

To explore the correlation between genetic phenotype and response to PD-1 inhibitors, we reviewed the NGS data from patients with uHCC who underwent systemic therapy in our center. The process of case inclusion is shown in Figure 1. A total of 62 patients underwent genetic testing from January 2019 to March 2022, of which 46 patients received surgical resection and 16 patients were diagnosed with uHCC. In clinical practice, the indications of NGS testing for patients who underwent surgery are tumors with high risk factors for recurrence, such as tumor size >5 cm, positive MVI, multiple tumors, or tumors with portal vein tumor thrombosis (PVTT). Of the patients who underwent surgical resection, 17 developed recurrence and were diagnosed with uHCC. Therefore, a total of 33 patients with uHCC (17 recurred and 16 uHCC) with available sequencing data were retrospectively reviewed.

Figure 1 | Patient flow diagram.

According to the NCCN guidelines (Version 5.2022), uHCC is defined as HCC with insufficient hepatic functional reserve (Child-Pugh B or C); insufficient residual liver volume (<40% for patients with Child Pugh grade A cirrhosis); complicated tumor location which was not suitable for surgical resection; or tumors invading large blood vessels or with distant metastases.

For genetic sequencing, next-generation sequencing (NGS) was performed on formalin-fixed paraffin-embedded (FFPE) tumor specimens or blood samples by 3D Medicines Inc. (Shanghai, China), which is certified and accredited by Clinical Laboratory Improvement Amendments (CLIA) and College of American Pathologist (CAP). The panel covered 733 cancer-related genes. Specifically, for patients who developed recurrence, NGS was performed on the resected specimen, which was collected during the initial operation; for patients with uHCC, NGS was performed on tissue or blood samples.

The treatment strategy for each patient with uHCC was determined by a multidisciplinary team. The options included locoregional therapies, TKIs, anti-VEGF antibodies, and PD-1 inhibitors. According to whether or not they had received PD-1 inhibitors, patients were divided into two groups. Namely, those who received PD-1 inhibitors as first- or second-line therapies were classified into the PD-1Ab group, and the others into the nonPD-1Ab group, respectively. The response to treatments was measured regularly, and the treatment strategy was replaced upon progressive disease (PD). All procedures were conducted in accordance with the Helsinki Declaration and with approval from the Ethics Committee of the Third Affiliated Hospital of Naval Medical University (ID: ChiECRCT20220048). Written informed consent was obtained from all participants. Clinicopathological information was extracted from the hospital information management system (HIMS). Prognostic information was obtained by telephone calls. All patients were followed for >6 months. Primary resistance was defined as initial on-treatment progression or progression with an initial stable disease of less than 6 months.

DNA extractions, library preparation, targeted capture, targeted sequencing and data processing

The assay methodology of DNA extraction and sequencing followed the methods published in a previous paper with some modifications (26). Namely, tumor genomic DNA in the FFPE specimens and cell-free DNA (cfDNA) in the plasma were extracted using QIAamp DNA FFPE Tissue kit (Qiagen GmbH, Hilden, Germany), and normal genomic DNA was extracted from peripheral blood mononuclear cells using QIAamp DNA Blood Mini kit (Qiagen GmbH, Hilden, Germany), respectively, following the manufacturer’s protocols. Libraries were prepared with the KAPA Hyper Prep Kit (KAPA Biosystems, Japan) following the guidelines of the manufacturer. Barcoded with unique molecular identifiers (UMIs) for cfDNA library preparation were individually adopted. For targeted capture, indexed libraries were subjected to probe-based hybridization with a customized NGS panel targeting 733 cancer-related genes. NGS sequencing was performed on the NovaSeq 6000 platform (Illumina, USA) for 100 bp paired-end sequencing with an average coverage depth of 2000× for tumor specimens and 35000× for cfDNA.

Raw data of paired samples were mapped to the reference human genome hg19 using the Burrows-Wheeler Aligner (v0.7.12) (27). PCR duplicate reads were removed and sequence metrics were collected using Picard (v1.130) and SAMtools (v1.1.19) for tissue-based testing, and an in-house developed software was used to generate duplex consensus sequences based on dual UMI integrated at the end of the DNA fragments for ctDNA-based testing. Variant calling was performed only in the targeted regions. Somatic single nucleotide variants (SNVs) were detected using an in-house developed R package to execute a variant detection model based on a binomial test. Local realignment was performed to detect indels. Variants were then filtered by their unique supporting read depth, strand bias, and base quality as previously described (28). Single-nucleotide polymorphism (SNPs) and indels were annotated by ANNOVAR against the following databases: dbSNP (v138), 1000Genome and ESP6500 (population frequency > 0.015). Only missense, stopgain, frameshift and non-frameshift indel mutations were kept. Copy number variations (CNVs) and gene rearrangements were detected as described previously (28).

Efficacy evaluation

Patients receiving locoregional therapies (LRTs) or systemic therapies were monitored for response evaluation every 6 weeks. If any symptoms or signs suggesting a progressive disease were observed at any time, one extra assessment was performed. Tumor size measurement using radiologic imaging was conducted by radiologists from the Third Affiliated Hospital of Naval Medical University. Assessment of objective response was confirmed by clinicians per Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and the date of disease progression was documented (29). The definition of primary resistance was initial on-treatment progression or progression with an initial stable disease of less than 6 months (30). Progression-free survival (PFS) was defined as the time from the onset of treatment to disease progression or death by any cause.

Assessment of tumor growth dynamics and definition of HPD

Tumor growth dynamics were evaluated based on tumor growth rate (TGR) and tumor growth kinetics (TGK), as described before (31, 32). Briefly, TGR was calculated as the log-scale calibrated change in the sum of the volumes of the target lesions per month according to RECIST 1.1 criteria (33). TGK was calculated as the change in the sum of the longest diameters of the target lesions according to RECIST 1.1 criteria per month (32). Changes in the tumour growth dynamics were assessed by calculating fold changes in TGK and TGR (34).

The defined criteria for HPD in our study were as follows: (1) time from the beginning therapy to treatment failure (TTF) < 2 months; (2) at least 50% increase in tumor burden for the first-line treatment or 2-fold increases in both TGK and tumor TGR ratios for the second-line treatment.

Multiplex immunofluorescence

Thirty FFPE samples were subjected to assessment of mIHC using the Akoya OPAL Polaris 7-Color Automation IHC kit (NEL871001KT). FFPE tissue samples were deparaffinized in a BOND RX system (Leica Biosystems) and then incubated sequentially with primary antibodies targeting CD68 (Abcam, ab213363, 1:1000), PD-L1 (CST, E1L3N, 13684S, 1:400), PD-1 (CST, D4W2J, 86163S, 1:200), CD163 (Abcam, ab182422, 1:500), CD3 (Dako, A0452), CD8 (Abcam, ab178089, 1:100), CD4 (Abcam, ab133616, 1:100), CD20 (Dako, L26, IR604), CD56 (Abcam, ab75813, 1:100), FOXP3 (Abcam, ab20034, 1:100) and pan-CK (Abcam, ab7753, 1:100) (Akoya Biosciences). Next, secondary antibodies and reactive Opal fluorophores were incubated. DAPI was used to stain the nucleic acids. Multiplex stained slides were scanned at 20 nm wavelength intervals from 440 nm to 780 nm with a fixed exposure time and an absolute magnification of 200 utilizing an Akoya Biosciences Vectra Polaris Quantitative Pathology Imaging System. All scans for each slide were then superimposed to create a single image. Multilayer images were imported into APTIME software (3D Medicines Inc.) for quantitative image analysis. Pan-CK staining was used to distinguish the tumor parenchyma and stroma. The numbers of stained cells per square millimeter in all nucleated cells were used to express the amounts of distinct cell types.

Statistical analysis

The demographic characteristics of patients were compared via the Chi-Square (χ2) test. PFS was analyzed using the Kaplan-Meier method with the log-rank test and drawn with R (version 4.0.2, R Development Core Team). Cox regression was implemented to calculate the HR for PFS, in both univariable and multivariable analyses. Interaction tests were performed to explore the interaction effect between Amp11q13 and treatment choice (PD-1Ab vs. nonPD-1Ab). Differences in immune cell subsets between the Amp11q13 group and the non-Amp11q13 group were analyzed using the Mann–Whitney U test. All statistical tests were double-sided; p ≤0.05 was considered significant.

Results

Patient characteristics and treatment groups

Between January 2019 and March 2022, 62 patients with HCC were enrolled in this study, including 46 with initially resected HCC and 16 with uHCC. Tissue samples accounted for 87.1% of all analyzed samples and liquid biopsy specimens were the rest. During the follow-up period, 17 patients who underwent previous resection showed recurrence, that was not suitable for operation and were classified into the uHCC group (Figure 1). Among all patients, the median age was 55.5 years (range, 30–76) and 16.1% (10/62) were women. Most patients did not have portal vein tumor thrombosis (PVTT) (49, 79.0%) and extrahepatic metastases (58, 93.5%) (Table 1). 33 patients with uHCC status were divided into PD-1Ab group (n=20) and nonPD-1Ab group (n=13). The baseline characteristics of these 33 patients for survival analysis are summarized in Supplementary Table 2.

Table 1 | Baseline characteristics of the enrolled 62 patients.

Genomic characteristics

The mutation profiles of all 62 patients are depicted. 54 (87.1%) individuals had at least one pathogenic or likely pathogenic alteration in DNA. The most frequently altered gene was TP53, which was observed in 54.8% of the patients (34/62), followed by TERT, CCND1, FGF19, and CTNNB1. The common gene with copy number amplification were CCND1, FGF19, FGF3 and FGF4 (Figures 2, S1, S2). These four genes were located in the chromosome 11q13 locus, also known as Amp11q13. In our dataset, 15 (24.2%) patients harbored Amp11q13. The clinical characteristics of the Amp11q13 and non-Amp11q13 groups are presented in Table 1. Patients with Amp11q13 showed higher level of Des-γ-carboxy-prothrombin (DCP), tumor number and were more prone to be combined with portal vein tumor thrombosis (PVTT, all P < 0.05). There was no significant difference in the molecular profiling distribution between Amp11q13 and non-Amp11q13 patients (Figure S3).

Figure 2 | Summary of frequently (Top 20) genomic characterized alterations among 62 patients with HCC.

Association of Amp11q13 with the clinical outcome

Thirty-three patients with recurrent HCC and initially diagnosed uHCC had a median follow-up period of 11 months (interquartile range 5.9‐21.5). For 20 patients in the PD-1Ab group, the median PFS was 10.5 months (95% CI 2.37–NE). The median PFS was 16.0 months (95% CI 5.10–NE) in the nonPD-1Ab group. All patients with nonPD-1Ab in our study received first-line therapy (Figure S4). Tumor shrinkage was observed in 18 (54.5%) of 33 patients with baseline and post-baseline assessments (Figure 3A). Of the 20 patients who were evaluable for response in the PD-1Ab group, 6 (30%) patients had an objective response. Response was not determined in three patients because of withdrawal for clinical deterioration before an initial response assessment. The Amp11q13 rate was more common in the PD-1Ab group with PD (50%) and the nonPD-1Ab group with stable disease (SD) (75%) (Figure 3B). In the PD-1Ab group, the proportion of patients with PD as BOR in patients with Amp11q13 was significantly higher than that in patients with nonAmp11q13 (100% vs 33.3%, P=0.03, Table S4). In the nonPD-1Ab group, the proportion of PD in patients with Amp11q13 and nonAmp11q13 had no significant difference (0% vs 11.1%, p>0.99, Table S4). Notably, all patients with Amp11q13 who received PD-1Ab showed primary resistance (Figure 3C).

Figure 3 | (A) Waterfall plot of the maximum reduction in target lesion diameter in 33 patients with recurrent HCC and initially diagnosed uHCC. Lesions caused by the death were not evaluable in 3 patients. (B) Histogram analysis of the proportion of the Amp11q13 and tumor response in PD-1Ab and nonPD-L1Ab groups. (C) The proportion of primary resistance in patients with Amp 11q13 among total patients, patients with the PD-1Ab and patients with nonPD-1Ab. * refer to patients with Amp11q13. (D) Kaplan-Meier estimates of PFS in Amp11q13 and nonAmp11q13 patients in PD-1Ab and Non-PD-1Ab treatment group.

By repeating Fisher’s exact test, the association of each gene with the response to systemic therapy was analyzed in detail. Notably, all the patients who harbored amplification of the either one gene (FGF3, FGF4, FGF19, or CCND1) in the 11q13 amplicon developed progressive disease (PD) upon ICI treatment. However, only CCND1 was significantly associated with poor response to ICI treatment (P = 0.033). In the nonPD-1Ab group, no such correlation was found.

Kaplan‐Meier curves showed that Amp11q13 was associated with inferior PFS, regardless of the treatment strategy. Notably, patients who received immunotherapy with Amp 11q13 had the shortest PFS compared with other groups (log rank P < 0.001) (Figure 3D).

In the PD-1Ab group, patients with Amp11q13 presented significantly shorter PFS than those without Amp11q13 (adjusted hazard ratio [HR], 0.09; 95% CI, 0.02-0.38; P<0.0001), while no such correlation was observed in the nonPD-1Ab group (adjusted hazard ratio [HR], 0.28; 95% CI, 0.05-1.76; P=0.15). However, there was no significant interaction in PFS between these two groups (P for interaction =0.0794, Figure 4A). No association was found between Amp11q13 and the prognosis in The Cancer Genome Atlas (TCGA) database (Figure S5).

Figure 4 | Kaplan‐Meier estimates of progression‐free survival. (A) Kaplan‐Meier survival curves of progression-free survival comparing PD-1Ab therapy and nonPD-1Ab therapy. (B) Kaplan‐Meier survival curves of progression‐free survival comparing first-line PD-1Ab therapy and nonPD-1Ab therapy.

We also analyzed the difference in the Amp11q13 effect on PFS between patients receiving first-line PD-1Ab and patients with nonPD-1Ab (Figure 4B). In the PD-1Ab group, the median PFS was 1.5 months in Amp 11q13 patients vs 16.2 months in nonAmp 11q13 patients (HR, 0.05; 95% CI 0.01-0.45; P = 0.0003). In the nonPD-1Ab group, the median PFS was 7.0months in Amp 11q13 patients vs 16.0 months in nonAmp 11q13 patients (HR, 0.28; 95% CI 0.05-1.76; P = 0.15). The interaction between Amp 11q13 status and treatment was significant (P for interaction = 0.0389). In the multivariable model, Amp11q13 status was the only independent predictor for PFS in the PD-1Ab group (HR, 17.83; 95% CI 2.93-108.4; P = 0.002) (Table 2).

Table 2 | Univariable and multivariable analysis of PFS in PD-1Ab group.

We also conducted repeated survival analyses to determine the contribution of each gene in predicting the prognosis. We discovered that the amplification of the either one gene (FGF3, FGF4, FGF19, or CCND1) predicted inferior PFS on PD-1Ab treatment separately (all P values < 0.05), but this association was not observed in patients treated with nonPD-1Ab.

Tumor microenvironment characteristics in patients with and without Amp11q13

To investigate the difference in the tumor immune microenvironment between HCC patients with and without Amp11q13, we assessed 30 FFPE samples that were subjected to both NGS and mIHC. Six of these harbored Amp11q13. Compared with samples without Amp11q13, samples with Amp11q13 were associated with higher densities of PD-1⁺ cells in the tumor (P=0.024) (Figure 5A). Similar results were also observed for densities of FoxP3⁺ cells in the stroma, despite that the significance of the difference was limited by the small sample size (P=0.072) (Figure 5B). No remarkable differences were observed in the density of other immune cell subsets in the tumor microenvironment (Figure S6).

Figure 5 | Relationship between immune cell subsets in tumor immune microenvironment and Amp11q13 status. Images of representative mIHC results (top, scale: 500 μm) were showed in two patients with and without Amp11q13. PD-1+ cells and FoxP3+ cells (bottom) in patients with Amp11q13 compared with nonAmp11q13 patients were also displayed. (A) Panel 1 referred to multiple stained slides and representative merged images show the distribution of individual markers [CD8, CD163, CD68, PD-1, PD-L1, pan-CK, DAPI for nucleic acids (blue)] and the separable vision of immunohistochemical staining with PD-1 antibody (green). (B) Panel 2 referred to multiple stained slides and representative merged images show the distribution of individual markers [CD3, CD20, FoxP3, CD56, CD4, pan-CK, DAPI for nucleic acids (blue)] and the separable vision of immunohistochemical staining with FoxP3 antibody (yellow).

To validate the findings, data from the TCGA (283 patients with HCC, include 24 with Amp11q13 and 259 with nonAmp11q13) were analyzed. The results showed that the expression of PD-1 and the infiltration of Treg cells was slightly higher in the Amp11q13 group compared to the nonAmp11q13 group, which is consistent with the findings of our study.

Hyper-progressive disease case series

HPD was observed in 3 of 20 patients (15.0%) (Table 3). All three cases were 11q13 amplified, less than 75 years old, without extra-hepatic metastases, and had a good ECOG performance status (0-1). Most importantly, all three cases were with Amp11q13. The proportion of HPD in patients with Amp11q13 was significantly higher than patients with nonAmp11q13 in the PD-1Ab group (3/5, 60% vs 0/15, 0%, P=0.0075). No HPD was found in the nonPD-1 group.

Table 3 | HPD evaluation of 5 patients with Amp11q13 receiving immunotherapy.

Case 1

This 61-year-old man was first diagnosed with HCC in segment 6 and underwent local resection in 2017. He had a local recurrence of disease in segment 7 and the left lateral lobe and underwent trans-arterial chemoembolization (TACE) in December 2017. Apatinib (a selective VEGFR-2 TKI, Hengrui, Jiangsu, China) was administered subsequently. However, MR showed 6 new lesions, with a maximum diameter of 2.8 cm in May 2020. Ablation therapy were performed thereafter. Unfortunately, further progression led to the development of 3 new lesions 5 months later. He was referred to TACE plus the PD-1 monoclonal antibody toripalimab in late October 2019. MR scan performed 1.8 months after the initiation of ICI showed significant radiological progression of disease with the occurrence of new lesions (totally >= 15). His TGK ratio was 11.95, and the TGR fold was 11.90 (Figure 6A). CtDNA test showed amplification of 11q13. Toripalimab was ceased thereafter and a therapy strategy containing hepatic artery infusion chemotherapy (HAIC) and regorafenib was given. Notably, the lesions were reduced following the cessation of ICI therapy. In March 2022, regorafenib was suspended for acute myocardial infarction. In June 2022, a dramatic progression of intra-hepatic lesions was observed by MR scan. Regorafenib was then continued. This patient was alive until the cut-off date.

Figure 6 | MR images and the timeline of therapy in three cases with HPD. (A) Case 1. Pre-baseline MR was obtained before ablation. After confirmed progression 5 months later, the patient adopted immunotherapy. Evaluation scan after 1.8 months showed significant radiological progression of disease with occurrence of new lesions. The TGR fold was calculated to be 11.90. (B) Case 2. After 2 cycles of immunotherapy, MR showed significant progression of intra-hepatic lesions. TTF was 1.6 months. TBI was 579.5%. (C) Case 3. After 2 cycles of treatment, MR scan showed significant progression of disease with increase in size of hepatic lesions. TTF was 1.3 months. TBI was 53.5%.

Case 2

A 56-year-old woman was first diagnosed with HCC in January 2019. She underwent resection as an initial treatment. During the operation, a mass of 4.0 cm in diameter was found in in segment 7. Two years after surgery (February 2021), an abdominal CT scan showed a recurrence of HCC in the left lateral lobe, with a diameter of 5.2 cm. TACE plus PD-1 monoclonal antibody sintilimab (Innovent, Suzhou, China) was given. Imaging performed after two cycles of treatment (April 2021) demonstrated significant radiological progression of disease with an increase in the number and size of hepatic lesions, accompanied with the occurrence of pulmonary lesions (Figure 6B). TTF was 1.6 months. HPD was diagnosed for a TBI of 579.5%. The NGS test based on the resected specimen showed an amplification of 11q13. PD-1 immunotherapy was ceased, and HAIC was given subsequently. However, she continued to deteriorate and passed away in June 2021.

Case 3

The 41-year-old man went to the local clinic for abdominal pain in December 2021. MR scan showed a large infiltrative mass involving almost the entire left hepatic lobe, with several nodules of 0.6 cm to 2.8 cm in the remaining left medial lobe, the right anterior S8 lobe and the S1-caudate lobe. Tumor thrombosis was seen in the left and main portal vein. He was diagnosed with locally extensive infiltrative and multifocal HCCs. Genetic sequencing of tumor biopsy showed the amplification of CCND1, FGF3 and MYC and somatic mutation of TP53 and TERT. Stereotactic body radiotherapy focusing on PVTT was performed 6 times from Month 2022, combined with oral lenvatinib and intravenous pembrolizumab. Two doses of pembrolizumab were administered when he was referred to our hospital. Unfortunately, as shown by the MR scan, the tumor progressed with a significant increase in the size of the multifocal hepatic lesions (Figure 6C). TTF was 1.3 months. HPD was diagnosed for a TBI of 53.5%. PD-1 immunotherapy was ceased and TACE was given. He passed away 4 months later.

Discussion

In this study, we retrospectively analyzed the clinical features and genomic profiles of 62 HCC patients in our institution. The total prevalence rate of Amp11q13 was 24.2%, which appeared more frequently in patients with poor clinical indicators. In patients with the primary resistance to systemic therapy, the proportion of Amp11q13 was 43.8%. Furthermore, three of 13 patients with primary resistance to PD-1 blockade therapy showed HPD, as proved by assessing tumor growth dynamics. All HPD patients showed Amp11q13.

Immunotherapy, represented by ICIs, has entered the therapeutic arsenal for many advanced-stage malignancies (35). ICIs reactivate exhausted T cells by targeting their immunological synapse. Besides, ICIs are associated with novel patterns of anti-tumor efficacy, such as longer duration of response, pseudoprogression (an initial disease progression followed by objective response), and even HPD (12). However, a notable number of individuals remain resistant to PD-1 blockade. In phase II and randomized phase III trials, PD-1 monoclonal antibodies showed a consistent 15−20% response rate in patients with HCC(7, 8). Approximately 30~40% of patients showed primary resistance to PD-1 blockade therapy.

The following pathways may contribute to PD-1 blockade therapeutic resistance: the absence of antigenic proteins, impaired antigen presentation, lack of T cells with tumor antigen-specific T-cell receptors, insensitivity to T cells (mutations in the interferon gamma pathway), overexpression of other inhibitory immune checkpoints and immunosuppressive cell recruitment (17, 36–38). Resistance to ICIs may also be caused by genetic T cell exclusion. For instance, oncogenic signaling through the MAPK pathway results in the production of VEGF and IL-8, which have known inhibitory effects on T cell recruitment and function (39). In a murine model, tumors with elevated beta-catenin lacked a subset of dendritic cells (DCs) known as CD103+ DCs, due to decreased expression of CCL4, a chemokine that attracts CD103+ DCs (40). Activation of the CDK4/6 pathway has also been found to be associated with resistance to PD-1 blockade. Whole-exome sequencing in patients with metastatic melanoma treated with anti-PD-1 antibodies revealed CDK4 amplification in patients with no clinical improvement. Defective TNF-alpha signaling, inflammatory response, and IFN-gamma response were found in tumors with CDK4 amplification (18). A bioinformatic analysis of the TCGA and MSKCC datasets revealed that CCND1 amplification was associated with shorter overall survival and inferior outcomes with ICIs (19). Furthermore, CCND1 amplification was associated with immune cell exclusion and a variety of aggressive, immunosuppressive oncogenic pathways, including KRAS and mTOR signaling (19). By using single-cell RNA sequencing for 33 melanoma tumor specimens, Jerby-Arnon et al. developed a cancer cell program to predict T cell exclusion and resistance to checkpoint blockade (41). Interestingly, CDK appears to be the master regulators that governs the expression of the genes in the program. In our study, we sought to determine the genetic factors that conferred responsiveness to PD-1 blockade in HCC. Our results confirmed the role of CCND1 amplification as a pivotal regulator of PD-1 resistance in HCC.

CCND1 is a downstream effector in the Wnt2/β-catenin pathway and activates the cyclin-dependent kinases (CDKs) CDK4 and CDK6 to promote cancer proliferation (42). FGF signaling promotes cancer proliferation, migration and angiogenesis by downstream activation of the RAS–RAF–MAPK, PI3K–AKT, signal transducer and activator of transcription and phospholipase Cγ pathways. The locus CCND1-ORAOV1-FGF19-FGF4-FGF3-TMEM16A-FADD-PPFIA1-CTTN was located on chromosome 11q13. The amplifications of CCND1, FGF3, FGF4 and FGF19 were characterized as Amp11q13 (43–45). Indeed, Amp11q13 is present in many cancers, including esophageal cancer, head and neck tumors, sarcoma, breast cancer, bladder tumors and hepatobiliary malignancies (43). Amp11q13 was most prevalent in head and neck squamous cell carcinoma, with an incidence of 30-36% (46, 47). In individuals with head and neck squamous cell carcinoma, Amp11q13 was also associated with decreased PFS (48). In HCC, Amp 11q13 was found in approximately 10% of the population (49–51). In the current study, we found that Amp11q13 was associated with a worse prognosis in the PD-1Ab group. Especially for patients with first-line treatment, there was a significant interaction between Amp11q13 status and treatment. In the multivariable analysis, Amp11q13 was an independent predictor of PFS in the PD-1Ab group.

Several immune cell subtypes in the tumor microenvironment have recently been found to be correlated with combination treatment with ICIs and antiangiogenic drugs in HCC (9, 52). Patients with a high infiltration of M1 macrophages had a significantly longer PFS and OS, suggesting that the high infiltration of M1 macrophages in HCC might be a potential positive indicator for the efficacy of immunotherapy (53). In our study, increased densities of PD-1+ cells in the tumor and FoxP3+ cells in the stroma were observed in patients with Amp11q13 compared with patients without Amp11q13. It was reported that effector Treg cells were activated and thus mediated suppression of antitumor immunity in HPD patients with gastric cancer (54). HPD might be triggered by activating Tregs in 11q13 amplified patients. However, the mechanisms through which tumor cell with 11q13 amplification induce the infiltration of Tregs are still needed to be elucidated. These findings showed that PD-1+ cells and FoxP3+ cells might be associated with hampered immunological response of HCC patients.

Patients with Amp11q13 may be sensitive to therapies other than immunotherapy. It was reported that FGF19, a biomarker of proliferation propensity, is an important driver gene in HCC (55, 56). HCC harboring FGF19 amplification may represent a subset of cancers that are strongly addicted to the FGFR pathway. The interaction of FGF19 and FGFR4 has been exploited for drug development. In FGF19-positive patients as measured by Immunohistochemistry, the overall response rate of FGFR4 antibody fisogatinib was 17% with a median duration of response of 5.3 months (57), while 0% in FGF19-negative patients. In addition, FGF3/FGF4 amplification was found to predict an increased response to sorafenib in patients with HCC (58). In addition, CCND1 could be targeted by palbociclib (59). These findings support future exploration of individualized combined targeted therapy based upon genetic sequencing in clinical setting.

Defined by its signal growth kinetics, HPD has raised many concerns regarding its incidence, mechanism, and potential biomarkers. Some debates that HPD represents the natural accelerating growth pace of the disease course (25). HPD after ICIs treatment in patients with Amp11q13 has been reported in non-small cell lung cancer, esophageal adenocarcinoma and lung cancer with neuroendocrine features. The incidence of hyperprogression was 43% in patients with 11q13 amplification (60). Previously, MDM2/4 amplification, EGFR alterations, and loss of CDKN2A/B have also been reported as potential biomarkers for HPD (61, 62). It was revealed that 12.7% of nivolumab-treated patients with HCC had HPD. The potential risk factors for HPD in HCC include previous radiotherapy (63), elevated neutrophil-to-lymphocyte ratio and hemoglobin level, PVTT and Child-Pugh score (64–66). However, little is known about whether HPD is associated with somatic mutations in HCC. In our study, the incidence of HPD was 60.0% in patients receiving ICIs with Amp11q13, supporting the role of Amp11q13 as an HPD predictor.

Currently, most investigators recommend the withdrawal of PD-1 blockade therapy and consider other potentially effective therapies upon HPD (23). In the first case, once ICI-related HPD was diagnosed, we stopped immunotherapy immediately, and HAIC and regorafenib were given subsequently. Imaging study following the adjustment of treatment strategy showed remarkable tumor shrinkage (the maximal shrinkage rate was 29.3%, compared with the HPD imaging). However, in another two patients with deteriorating liver function, salvage therapies failed to improve the survival.

There are several limitations to our study. First, limited number of patients and lack of external validation may lead to bias. From January 2019 to March 2022, only 33 cases of uHCC with complete clinical and genotypic data met the inclusion criteria. Second, it should be noted that due to the progressive nature of advanced stage malignancy, it is difficult to dissociate HPD from primary resistance to immunotherapy, even though a specific growth kinetic model was used. Third, our findings of mIHC lacked an exploration of the relationship between treatment efficacy and immune cell infiltration due to the limitations of sample size. Therefore, we are unable to conclusively demonstrate how Amp11q13 affects primary resistance and HPD.

Conclusions

In conclusion, we found that Amp11q13 was seen more frequently in advanced stage HCC patients, and was associated with HPD in patients receiving anti-PD-1 immunotherapy. These findings may help guide the use of immunotherapy for HCC in routine clinical practice. Patients with Amp11q13 are less likely to benefit from PD-1 blockade therapies. Other options, including locoregional therapies, such as HAIC, and other systemic regimens, including CDK4/6 inhibitor, FGFR4 antibody might be considered as potential effective strategies. Further studies are urgently needed to validate the value of NGS in the prediction of PD-1 blockade responsiveness and interrogate the mechanism of resistance in patients with certain genetic aberrations.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: OMIX repository (OMIX002465), BioProject ID: PRJCA013528, accession: https://ngdc.cncb.ac.cn/omix/release/OMIX002465.

Ethics statement

The studies involving human participants were reviewed and approved by the Chinese Ethics Committee of Registering Clinical Trials. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

HZ, KY and DZ designed the study. YC and DZ acquired and analyzed the NGS and mIHC data. Data collection and data cleansing with clinical information were executed by KY and JL. KY, DZ and YC wrote the manuscript. HZ, WL, YF, MH, JC, SC, TB and YB critically reviewed the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by grants from the Natural Science Foundation of China (No. 82172880) and the Science and Technology Commission of Shanghai Municipality (No. 20Y11909300).

Acknowledgments

We wish to thank the support of the precise detection and data analysis from 3D Medicines Inc.

Conflict of interest

DZ, YC, MH, JC, SC, TB and YB were employed by the company 3D Medicines Shanghai Inc.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1116057/full#supplementary-material

Supplementary Figure 1 | Summary of frequently (Top 20) genomic characterized copy number alterations among 62 patients with HCC.

Supplementary Figure 2 | Summary of frequently (Top 20) genomic characterized single-nucleotide variants among 62 patients with HCC.

Supplementary Figure 3 | The molecular profiling distribution between Amp11q13 and nonAmp11q13 patients

Supplementary Figure 4 | Treatment efficacy in 33 patients with PD-1Ab group and nonPD-1Ab group

Supplementary Figure 5 | Kaplan-Meier curves of PFS comparing Amp11q13 and nonAmp11q13 groups in TCGA cohort.

Supplementary Figure 6 | Immune cell subsets in the tumor microenvironment for patients in Amp11q13 and nonAmp11q13 groups.

Supplementary Figure 7 | Comparison of the expression of PD-1 and infiltration of Treg cells in TCGA database.

Supplementary Table 1 | Specific treatment strategies for patients in PD-1Ab group.

Supplementary Table 2 | Baseline characteristics of 33 patients with recurrent HCC and initially diagnosed uHCC.

Supplementary Table 3 | Demonstration of patients’ response and the amplification status of individual genes within the 11q13 amplicon.

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CLINICAL TRIAL
published: 28 March 2023
doi: 10.3389/fimmu.2023.1133689

Multicenter, single-arm, phase II study (CAP) of radiotherapy plus liposomal irinotecan followed by camrelizumab and anti-angiogenic treatment in advanced solid tumors

Jie Shen ¹, Jing Yan ¹, Juan Du ¹, Xiaoqin Li ², Jia Wei ¹, Qin Liu ¹, Hongmei Yong ³, Xiaolu Wang ¹, Xiaofeng Chang ¹, Zhou Ding ¹, Wu Sun ¹, Chenxi Liu ¹, Sihui Zhu ¹, Jingyi Guo ¹, Huajun Li ⁴, Ying Liu ⁴, Wulou Zhang ⁵, Zonghang Liu ⁵, Rutian Li ¹* and Baorui Liu ¹*

¹ The Comprehensive Cancer Center of Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China, ² Department of Oncology, Affiliated Hospital of Jiangsu University, Zhenjiang, China, ³ Department of Oncology, The Affiliated Huai’an Hospital of Xuzhou Medical University and The Second People’s Hospital of Huai’an, Huai’an, China, ⁴ Department of Clinical Research and Development, Jiangsu Hengrui Pharmaceuticals Co., Ltd, Shanghai, China, ⁵ Department-II of General Surgery, Nanjing Jiangbei Hospital, Nanjing, China

Edited by:
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School, United States

Reviewed by:
Eda K. Holl, Duke University, United States
Shouju Wang, Nanjing Medical University, China

*Correspondence:
Baorui Liu
baoruiliu@nju.edu.cn 
Rutian Li
rutianli@nju.edu.cn

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 29 December 2022

Accepted: 31 January 2023

Published: 28 March 2023

Citation:
Shen J, Yan J, Du J, Li X, Wei J, Liu Q, Yong H, Wang X, Chang X, Ding Z, Sun W, Liu C, Zhu S, Guo J, Li H, Liu Y, Zhang W, Liu Z, Li R and Liu B (2023) Multicenter, single-arm, phase II study (CAP) of radiotherapy plus liposomal irinotecan followed by camrelizumab and anti-angiogenic treatment in advanced solid tumors. Front. Immunol. 14:1133689. doi: 10.3389/fimmu.2023.1133689

Introduction

Combination therapeutic mode is likely to be the key to enhance the efficacy of immunotherapy in a wider range of cancer patients. Herein, we conducted an open-label, single-arm, multicenter, phase II clinical trial that enrolled patients with advanced solid tumors who had progressed after standard treatments.

Methods

Radiotherapy of 24 Gy/3 fractions/3-10 days was given to the targeted lesions. Liposomal irinotecan (80mg/m², dose could be adjusted to 60 mg/m² for intolerable cases) was intravenously (IV) administered once within 48 hours after radiotherapy. Then, camrelizumab (200mg IV, q3w) and anti-angiogenic drugs were given regularly until disease progression. The primary endpoint was objective response rate (ORR) in the target lesions evaluated by investigators per RECIST 1.1. The secondary endpoints were disease control rate (DCR) and treatment-related adverse events (TRAEs).

Results

Between November 2020 and June 2022, 60 patients were enrolled. The median follow-up was 9.0 months (95% confidence interval (CI) 5.5-12.5). Of 52 evaluable patients, the overall ORR and DCR were 34.6% and 82.7%, respectively. Fifty patients with target lesions were evaluable, the ORR and DCR of the target lesions were 35.3% and 82.4%, respectively. The median progression-free survival was 5.3 months (95% CI 3.6, 6.2), and the median overall survival was not reached. TRAEs (all grades) occurred in 55 (91.7%) patients. The most common grade 3-4 TRAEs were lymphopenia (31.7%), anemia (10.0%), and leukopenia (10.0%).

Conclusion

The combination of radiotherapy, liposomal irinotecan, camrelizumab, and anti-angiogenesis therapy demonstrated promising anti-tumor activity and well tolerance in various advanced solid tumors.

Clinical trial registration

https://clinicaltrials.gov/ct2/home, identifier NCT04569916.

Keywords: radiotherapy, immunotherapy, anti-angiogenic therapy, liposomal irinotecan, clinical trial, advanced solid tumors

Introduction

Although immunotherapy has become a powerful weapon for a wide variety of cancer types, its benefit in the overall population is extremely low. Developing effective immune-combination therapy is a potential strategy to benefit a wider range of patients, especially those who have failed standard therapies.

Stereotactic body radiotherapy (SBRT) is a classical and effective locoregional therapy for cancer. In addition to directly causing DNA damage in tumor cells, it also stimulates tumor-specific antigens release, thereby enhancing the antitumor immune response (1). Previous clinical studies have demonstrated that radiotherapy combined with PD-1/PD-L1 inhibitors has long-lasting antitumor activity in advanced or metastatic melanoma (2), non-small cell lung cancer (NSCLC) (3), Hodgkin lymphoma (HL), renal cell carcinoma (RCC) and other cancer types (4).

Nowadays, nanotechnology combined with RT and immunotherapy arises a novel pathway for boosting cancer therapy effect. Liposomal irinotecan is a liposomal formulation of irinotecan, which is one of the well-known chemotherapeutic agents with radiosensitizing property. Liposomes have protective effect on encapsulated drugs and can specifically deliver drugs to tumors owing to enhanced permeability and retention effect (5). Previous study shows that irinotecan plus SBRT have promising results in metastatic colorectal cancer, despite heavily pretreated patients (6). Nanoparticles (NPs) distribution and accumulation were up-regulated by the interaction between RT and tumor microenvironment (TME) (7). Thereby we proposed that SBRT combined with liposomal irinotecan may systematically ameliorate tumor immunity and increase tumor control.

Moreover, preclinical and clinical studies have shown that radiotherapy has a synergistic effect with immunotherapy via a stimulating adaptive immune response (8), and positively regulating the TME (9) to promote the anti-tumor response. The combination therapy has been proved to be effective and safe in the treatment of varieties of tumors (10–12). Furthermore, crosstalk between tumor vessels and immune cells determines the nature of anti-tumor immunity, which contributes to a destructive cycle in tumor growth (13). Emerging preclinical evidence demonstrates the potential of anti-angiogenesis therapy could improve the efficacy of cancer immunotherapy by reprogramming the immune microenvironment via vessel normalization (14, 15). Treatment of camrelizumab (an anti-PD-1 antibody) and apatinib (an anti-angiogenic agent) alone or in combination has been proved to have antitumor activity in nasopharyngeal carcinoma (NPC), head and neck cancer (HNC), hepatocarcinoma (HCC), esophageal squamous cell carcinoma and other types of cancers (16–18).

Based on the abovementioned interactions and synergy, a multi-modal approach combining radiotherapy with liposomal irinotecan, anti-angiogenic therapy, and immunotherapy is a promising therapeutic strategy. To our best knowledge, no clinical trials have investigated this pattern of combination treatment. Herein, we present our phase II study of SBRT with liposomal irinotecan, camrelizumab, and anti-angiogenic therapy for patients with advanced solid tumors who have failed or have no available standard therapy or lack of systemic therapy.

Patients and methods

Study design and patients

This was a single-arm, multicenter, open-label, phase II study (NCT04569916). The main center of the clinical trial was Nanjing Drum Tower Hospital affiliated to Nanjing University Medical School, and sub-centers were Affiliated Hospital of Jiangsu University and Huai’an Second People’s Hospital. This study was approved by the ethics committees of the above three hospitals. Patients provided written informed consent.

Eligible patients were aged 18 years or older, pathologically confirmed with recurrence or metastasis solid tumors (such as pancreatic cancer, colorectal cancer, NSCLC, HCC, HNC, gastric cancer, etc.). Patients have progressed on or lacked systemic therapy. Other key inclusion criteria included at least two measurable lesions (at least one extracranial measurable lesion for patients with brain metastases) according to Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1), an Eastern Cooperative Oncology Group (ECOG) performance score of 0-2, a life expectancy of at least 12 weeks and had adequate organ function. The complete list of eligibility criteria is provided in Supplementary Material 1. All patients provided written informed consent before study participation. The trial was carried out in accordance with the International Conference on Good Clinical Practice Standards and the Declaration of Helsinki.

Procedures

Eligible patients received hypofractionated radiotherapy after enrollment (a total irradiation dose of 15-24Gy in 3 fractions, delivered at 5-8Gy per fraction, completed within 3-10 days). Liposomal irinotecan (80 mg/m², ivdrip) was given once within 48 hours after the last radiotherapy. Dose could be adjusted to 60 mg/m² for intolerable cases judged by investigator. Camrelizumab (200mg, ivdrip, within 30-60 min) was administrated within 48 hours after the treatment of liposomal irinotecan in 21-day cycles, combined with anti-angiogenic therapy. Apatinib 250mg qd was the first choice of anti-angiogenic drug. If patients were intolerant of anti-angiogenic drug, drug withdrawal, interruption, and modification were permitted.

Outcomes

The primary endpoint was the objective response rate (ORR) of the target lesions. Radiographic tumor response evaluation was performed using CT obtained after camrelizumab every 2 cycles until disease progression or unacceptable toxic effects. Tumor response was determined based on RECIST 1.1.

The secondary endpoints included disease control rate (DCR) of target lesions, treatment-related adverse events (TRAEs), and clinical benefit rate (CBR). TRAEs and serious adverse events (SAEs) were collected and evaluated according to the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI CTCAE) (version 4.0.3).

Statistical analysis

The full analysis set (FAS) was defined as all patients enrolled and received at least one dose of any drug in study treatment, which was the primary efficacy analysis set. The safety analysis set included all patients who received at least one dose of any study drug and recorded safety data after administration. Trial results are mainly analyzed using descriptive statistics. Overall survival (OS), median progression-free survival (mPFS), and duration of response (DoR) were estimated using the Kaplan-Meier method, and the 95% CI was calculated using Brookmeyer and Crowley methods. In addition, survival plots are plotted. The chi-square test was used to assess the associations between prior treatment and clinical outcomes. Statistical significance was defined as P < 0.05.

Results

Patient population and baseline characteristics

Between November 2020, and June 2022, a total of 60 eligible patients were enrolled (Figure 1). All patients were evaluable for toxicity, and 50 were radiographically evaluable for efficacy.

Figure 1 | The flowchart of enrolled patients.

The demographic and disease characteristics at baseline are listed in Table 1. The median age was 58 years (range, 21-84 years), 33 patients (55.0%) were male and 27 (45.0%) were female. Of these, 10 patients (16.7%) did not receive systemic therapy before enrollment due to lack of standard treatment or intolerance of chemotherapy, and 28 (46.7%) received third- or later-line therapy. 20 (33.3%), 17 (28.3%) and 21 (35.0%) patients received prior radiotherapy, immunotherapy or anti-angiogenic drugs, respectively. Main reasons for discontinuing study treatment were disease progression (PD) (41.7%, 25/60) and death (18.3%, 11/60). Thirty-eight patients (63.3%) received apatinib, 3 (5.0%) received anlotinib, 4 (6.7%) received lenvatinib, and 15 (25.0%) did not receive any anti-angiogenic drugs during the study due to poor performance status.

Table 1 | Patient baseline demographics and disease characteristics.

Efficacy

At the time of data cutoff (June, 2022), the median duration of follow-up was 9.0 months (95% CI 5.5-12.5). A total of 52 patients were evaluable by RECIST 1.1 criteria, while other 8 patients were not evaluable owing to death or falling off. In terms of overall efficacy, 18 patients had partial response (PR), 24 had stable disease (SD) and 9 had progressive disease (PD). The overall ORR and DCR were 34.6% and 82.7%, respectively (Supplementary Table S1).

We particularly analyzed the efficacy of patients with target lesions. Of 52 evaluable patients, 50 patients with target lesions were evaluable by RECIST 1.1 criteria, while target lesions of two were unevaluable. The ORR and DCR of patients with target lesions were 36.0% and 96.0%, respectively, as PR was observed in 18 patients, SD in 30 patients, and PD in 2 patients (Supplementary Table S1). Among patients with target lesions, we also found that objective response occurred in more patients without prior anti-angiogenic therapy than those with prior treatment (ORR: 45.2% vs. 21.1%, P=0.085). In addition, patients who were not pre-treated with immunotherapy seem more likely to benefit from the treatment with an ORR of 48.5%, compared with those with prior immunotherapy (ORR: 11.8%, P=0.01). The complete list is provided in Supplementary Table S2.

In the current study, more than 20 types of cancer were included. Nearly half of the subjects were with digestive system tumors including 10 with biliary tract cancer (2 PR, 8 SD), 8 with pancreatic cancer (4 evaluable, 4 SD), 3 with gastric cancer (GC) (2 evaluable, 2 PR), and 2 with duodenal carcinoma (2 PR). Additionally, ten patients with soft-tissue sarcomas (STS) were enrolled, six of which were undifferentiated sarcomas. The ORR was 30.0% (3/10) in STS. Besides some common cancer types, such as ovarian cancer (2 PR, 1 SD) and non-small cell lung cancer (3 evaluable, 1 PR, 1 SD, 1 PD), a few of rare types of cancer were also under investigation in our study. For instance, one patient was with anaplastic thyroid cancer (ATC) (PR), and two were with thymic squamous cell carcinoma (2 PR). The best response of target lesions of each cancer type is shown in Table 2 and the swimmer plot according to cancer type was shown in Figure 2.

Table 2 | Overall efficiency and the target lesions evaluation.

Figure 2 | Swimmer plot according to cancer type. PR, partial response; SD, stable disease; PD, progressive disease; CCA, cholangiocarcinoma; GBC, gallbladder carcinoma; STS, soft-tissue sarcomas; SCLC, small lung cancer; NSCLC, non-small lung cancer; PDAC, pancreatic ductal adenocarcinoma; CSC, cutaneous squamous cell carcinoma; tSCC, thymic squamous cell carcinoma; cSCC, cutaneous squamous cell carcinoma; mSCC, metastatic squamous cell carcinoma of unknown primary;DPC, duodenal papilla carcinoma; DAC, duodenal adenocarcinoma; CRC, colorectal cancer; UC, Urothelial carcinoma; ATC, Anaplastic thyroid carcinoma; NPC, nasopharyngeal carcinoma; PNET, pancreatic Neuroendocrine Tumor.

As shown in Figure 3A, a total of 42 patients achieved a shrinkage of their target lesions. Among 18 patients who had PR, the median time to response (TTR) was 2.3 months (95%CI 1.2, 3.4). The median duration of response was 7.5 (95%CI 3.0, NA), and the CBR was 30.0%.

Figure 3 | Waterfall plot and survival data. (A) Best percent change from baseline in the target lesions (n=50). (B, C), Kaplan–Meier curve of PFS and OS.

At the time of data cutoff, eight patients (13.3%) were still receiving treatment, 40 (66.7%) patients had PFS events, and the mPFS was 5.3 months (95% CI 3.6, 6.2) (Figures 3B, C). A total of 19 patients (31.7%) died, and the mOS was not reached (NR) (95% CI 7.4, NA) (Figures 3B, C). The estimated one-year overall survival rate was 57.1% (95%CI 0.4-0.8).

Safety

All of the 60 patients were included in the safety analysis. The median cycle of camrelizumab was 4 cycles (range, 4–28). A total of 55 (91.7%) of the 60 patients experienced at least one TRAE (Tables 3, 4). The most common any-grade TRAEs were lymphocyte count decreased (56.7%), anemia (43.3%), white blood cell count decreased (41.7%), rash (28.3%), and hypokalemia (28.3%). Grade 3 or 4 TRAEs occurred in 45.0% (27/60) of patients. The most common AEs were lymphocyte count decreased (31.7%), anemia (10.0%), and white blood cell count decreased (10.0%). Treatment related SAEs of grade 3 or higher were reported in 3 patients (5.0%), including neutrophil count decreased (2 patients, 3.3%) and diarrhea (1 patient, 1.7%).

Table 3 | Efficacy of each cancer types.

Table 4 | Treatment related adverse events.

TRAEs leading to camrelizumab treatment interruption occurred in 7 patients (11.7%). Patients discontinued camrelizumab because of TRAEs that were lymphopenia and leukopenia. TRAEs leading to apatinib treatment interruption occurred in 10 patients (16.7%). Only three patients discontinued liposomal irinetocan owing to hypersensitivity reactions during infusion.

Immune-related AEs (irAEs) of any grade occurred in 47 (78.3%) patients, and the most frequent irAEs were lymphocyte count decreased (56.7%), white blood cell count decreased (31.7%), anemia (30.0%), and rash (26.7%).

Discussion

In this phase II study, we attempted to improve the antitumor efficacy via a combination treatment with radiotherapy, liposomal irinotecan, camrelizumab and anti-angiogenic drugs in patients with advanced solid tumors who have failed or have no available standard therapy or lack of systemic therapy.

Given that patients in our study generally had no standard of care, or progressed on multi-line therapy in the advanced-stage of tumor, good tolerance and low toxicities were the major concerning. A clinical trial has shown that hypofractionated radiation therapy (3 fractions of 8 Gy) combined with anti-PD-L1-antibody or anti-CTLA-4 antibody could induce the proliferation of CD8⁺ T-cell and increase of M1/M2 macrophage ratios in patients with metastatic microsatellite stable colorectal adenocarcinoma (19). Considering the poor performance status and therapeutic tolerance of the enrolled patients, we adopted 3 fractions of 5-8 Gy, which is not only conducive to activating the body’s anti-tumor immune response, but also well tolerated by the patients.

We found the comprehensive therapeutic strategy provided a promising anti-tumor activity for advanced patients with tolerable toxicities, regardless they received prior immuno- or anti-angiogenic therapy or not. Especially, several types of tumors demonstrated promising efficacy and well-tolerate safety that deserve further exploration.

Soft-tissue sarcoma

Sarcomas are a heterogenous group of cancers can be further divided based on distinct morphology and genetic changes into more than 100 subtypes as defined by the World Health Organization (20). Due to the multiple subtypes, high heterogeneity, and low immunogenicity of STSs, the pathological types of the patients enrolled in previous sarcoma studies were different, and the efficacy was not ideal. Patients with STS progressing after anthracycline-based chemotherapy, naïve from angiogenesis inhibitors received anlotinib as the second-line drug, the efficacy was with an ORR of 12% and mPFS of 5.6 months, of undifferentiated sarcomas, mPFS was 4.1 months and mOS was 11 months (21). A pooled analysis of phase II trials investigating a PD1 or PD-L1 antagonist in patients with advanced STS, the ORR of PD-1/PD-L1 antibody as single-agent immunotherapy was 15.8% (22). In terms of immunotherapy combined with TKI, pembrolizumab combined with axitinib had an ORR of 25%, and TQB2450 (PD-L1 antibody) combined with anlotinib display a good performance with an ORR of 36.7%, mPFS of 7.9 months (23). In CAP study, ten patients with STS were enrolled, including six undifferentiated sarcomas. The ORR in STS was 33.0% (3/10), mPFS was 6.2 months (95%CI:1.9-10.5), mOS was NR. Notably, two of six undifferentiated sarcomas got PR, which showed that undifferentiated sarcoma was relatively easy to achieve remission. The comprehensive treatment regime of immunotherapy combined with anti-angiogenic therapy, radiotherapy, and chemotherapy deserved further exploration in the treatment of undifferentiated sarcoma.

Gastric cancer

Three patients with GC were enrolled in this study. Two patients could be evaluated for efficacy and were still in follow-up, of which the best efficacy was PR, and PFS was not reached. The response rate of this study in GC was higher than that of immunotherapy combined anti-angiogenic therapy or immunotherapy combined chemotherapy (24). Survival data was not reached, and the sample size needed to be expanded to verify.

Duodenal carcinoma

Duodenal carcinoma (DA) is a rare tumor, accounting for less than 1% of gastrointestinal malignancies. The median survival of the patients who underwent resectional operation was 54 months (25). Practical guidelines for the treatment of DA are lacking and there is limited data reporting outcomes for metastatic duodenal adenocarcinoma (26). The gene mutation spectrum of small intestinal adenocarcinoma, colorectal cancer and GC is very different, and the molecular characteristics of unspecified small intestinal adenocarcinoma and duodenal adenocarcinoma are also different (27). Two patients with duodenal carcinoma were enrolled in this research, and the efficacy was PR. mPFS of the patients were 16.2 months and 3.8 months, respectively. In the future, we will explore efficacy correlation in genomic changes and TME of DA.

Anaplastic thyroid cancer

Anaplastic thyroid cancers (ATCs) comprise 1% to 10% of all thyroid cancers worldwide. Patients with ATCs have a median survival of 5 to 12 months and a 1-year overall survival of 20% to 40% (28). One patient with ATC was enrolled in this research. The best efficacy was PR and PFS exceeded 15.3 months. The patient was still in follow-up. Dabrafenib combined with trametinib have been FDA-approved for the treatment of ATC patients with BRAF V600E mutation, while patients without BRAF V600E mutation have no systemic therapy (29). This research provides a comprehensive treatment regime for patients with wild-type BRAF V600E ATC without systemic therapy, so that patients can obtain long-term remission.

We recognize the limitations of our study. This study enrolled a heterogeneous patient population, with a limited number of patients in a variety of primary cancers. Moreover, patients were exposed to a high number of prior therapies, and their physical capability and compliance are relatively poor, thus some patients did not use angiogenesis drugs because of poor performance status, intolerance, financial reasons, or other subjective causes. Furthermore, the immune status of these patients such as PD-L1 expression before and during therapy was not prespecified in our protocol, and whether PD-L1 expression could be consider as a biomarker for the efficacy of the study combination regimen in this challenging disease these warranted further investigation in future trials with larger sample size. Exploring effective predictive biomarkers would be our future work since it is vital to identify which cancer types and populations are particularly suitable for combinatorial approaches.

Conclusion

Regardless of whether the patients previously had received radiotherapy, immunotherapy or anti-angiogenic drugs, the patients progressing on multi-line therapy could still benefit from radiotherapy in combination with liposomal irinotecan, camrelizumab and anti-angiogenic. For rare tumors without prior systemic therapy (such as certain sarcomas, duodenal carcinoma, and ansplastic thyroid carcinoma), the efficacy of this therapy was better than the existing therapies.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.

Ethics statement

The studies involving human participants were reviewed and approved by the ethics committees of Nanjing Drum Tower Hospital, Affiliated Hospital of Jiangsu University and Huai’an Second People’s Hospital. The patients/participants provided their written informed consent to participate in this study.

Author contributions

JS: Methodology, writing-original draft, patient enrollment, treatment, project administration, investigation, data curation, formal analysis, writing-review and editing. JY: Patient enrollment, treatment. JD: Patient enrollment, treatment. XL: Patient enrollment, treatment. JW: Patient enrollment, treatment. QL: Patient enrollment, treatment. HY: Patient enrollment, treatment. XW: Patient enrollment, treatment. XC: Patient enrollment, treatment. ZD: Patient enrollment, treatment. WS: Patient enrollment, treatment. CL: Methodology, writing-review and editing, specimen collection, data entry. SZ: Specimen collection, data entry. JG: Specimen collection, data entry. HL: Resources. YL: Resources. WZ: specimen collection. ZL: specimen collection. RL: Conceptualization, methodology, investigation, project administration. BL: Conceptualization, methodology, investigation, project administration, writing-original draft, patient enrollment, treatment. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by a grant from the Nanjing City General Project (YKK22095).

Acknowledgments

The authors are grateful to the patients, families, investigators and study teams who participated in CAP study. Liposomal irinotecan, camrelizumab and apatinib were provided by Jiangsu Hengrui Pharmaceuticals Co., Ltd.

Conflict of interest

HL and YL are employed by Jiangsu Hengrui Pharmaceuticals Co., Ltd.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer (SW) declared a shared parent affiliation with the authors (JS, JY, JD, JW, QL, XW, XC, ZD, WS, CL, SZ, JG, RL and BL) to the handling editor at the time of the review.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1133689/full#supplementary-material

Abbreviations

ATC, anaplastic thyroid cancer; CBR, clinical benefit rate; CI, confidence interval; DA, duodenal carcinoma; DCR, disease control rate; DoR, duration of response; ECOG, Eastern Cooperative Oncology Group; EPR, enhanced permeability and retention; FAS, full analysis set; HCC, hepatocarcinoma; HL, Hodgkin lymphoma; HNC, head and neck cancer; irAEs, immune-related adverse events; IV, intravenously; NCI CTCEA, National Cancer Institute Common Terminology Criteria for Adverse Events; NE, not evaluable; NPC, nasopharyngeal carcinoma; NPs, nanoparticles; NR, not reached; NSCLC, non-small cell lung cancer; ORR, objective response rate; OS, overall survival; PD, progressive disease; PFS, progression-free survival; RCC, renal cell carcinoma; RECIST 1.1, Response Evaluation Criteria in Solid Tumors version 1.1; SAEs, serious adverse events; SBRT, stereotactic body radiotherapy; SD, stable disease; TME, tumor microenvironment; TRAEs, Treatment-related adverse events; TTR, time to response.

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Copyright © 2023 Shen, Yan, Du, Li, Wei, Liu, Yong, Wang, Chang, Ding, Sun, Liu, Zhu, Guo, Li, Liu, Zhang, Liu, Li and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

ORIGINAL RESEARCH
published: 06 April 2023
doi: 10.3389/fonc.2023.1148498

Transmembrane protein 88 suppresses hepatocellular carcinoma progression and serves as a novel prognostic factor

Lin Cai ¹, Yu Du ²*, Kai Song ¹, Peng Peng ³ and Fei Han ¹

¹ School of Food and Drug, Xuzhou Polytechnic College of Bioengineering, Xuzhou, China, ² Department of Traditional Chinese Medicine, Xuzhou Kuangshan Hospital, Xuzhou, China, ³ Department of General Surgery, Xuzhou Kuangshan Hospital, Xuzhou, China

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Zhaochen Liu, Zhengzhou University, China
Ke-kang Sun, First People’s Hospital of Kunshan, China

*Correspondence:
Yu Du
dudoctor@126.com

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Oncology

Received: 20 January 2023

Accepted: 24 February 2023

Published: 06 April 2023

Citation:
Cai L, Du Y, Song K, Peng P and Han F (2023) Transmembrane protein 88 suppresses hepatocellular carcinoma progression and serves as a novel prognostic factor. Front. Oncol. 13:1148498. doi: 10.3389/fonc.2023.1148498

Background

Transmembrane protein 88 (TMEM88) is known to be involved in the canonical Wnt signaling pathway and is implicated in several malignancies. However, the expression, function, and prognostic significance of TMEM88 in hepatocellular carcinoma (HCC) remain unclear.

Methods

In this study, we analyzed mRNA levels of TMEM88 in HCC specimens from the TCGA dataset (n=374) to explore the correlation between TMEM88 and HCC. We also overexpressed TMEM88 in the Huh7 human HCC cell line to investigate its tumor-related role in HCC. Additionally, we conducted in vivo experiments using a mouse model to further validate the critical function of TMEM88 in modulating HCC growth.

Results

Our results showed that TMEM88 is negatively correlated with the T stage, TNM stage, and pathological grade of HCC. Higher levels of TMEM88 can help predict better overall survival of HCC in both univariate and multivariate analyses. Similarly, higher TMEM88 is a novel prognostic factor for better disease-specific survival of HCC. Overexpression of TMEM88 in Huh7 cells led to a decreased cell proliferation capacity. Xenograft experiments in a mouse model showed that TMEM88 overexpression can remarkably suppress HCC progression.

Conclusions

Transmembrane protein 88 suppresses HCC growth both in vitro and in vivo, which can act as a potential prognostic factor with clinical application potential.

Keywords: transmembrane protein 88, hepatocellular carcinoma, proliferation, xenografts, prognosis

Introduction

Hepatocellular carcinoma (HCC) represents a common malignancy with poor prognosis, ranking the fourth leading cause of cancer-related mortality worldwide (1). Predominant HCC risk factors include hepatitis virus infection, alcohol liver diseases, steatohepatitis, etc (2). Although for HCC patients diagnosed at early-stage, tumor resection and transplantation represent the curative therapies, a large amount of HCC patients are diagnosed at late stages with poor prognoses (3). In general, the five-year survival rate of HCC is less than 20% (4). Further exploration of the disease mechanisms and identification of prognostic biomarkers are critical for HCC treatment.

Transmembrane protein 88 (TMEM88) is a member of the TMEM protein family and was first identified in 2010 by Lee HJ and colleagues (5). It was initially recognized as a disheveled-binding protein, with the authors confirming that the PDZ domain of disheveled binds to the C-terminal domain of TMEM88 through various methods. Interestingly, the authors found that silencing TMEM88 led to enhanced Wnt/β-catenin signaling, suggesting a suppressing role of TMEM88 on this signaling pathway. Microarray analysis also revealed significant correlations between TMEM88 and genes in the Wnt/β-catenin pathway. Further investigation showed that TMEM88 enhances cardiomyocyte differentiation and suppresses endothelial differentiation by inhibiting the Wnt/β-catenin signaling pathway, highlighting the crosstalk between TMEM88 and Wnt signaling (6). Furthermore, TMEM88 participates in the inflammatory responses. For example, Tao et al. reported that TMEM88 promotes TNF-α-induced secretion of inflammatory factors in human hepatic stellate cells (7). Their group later reported that this regulation can be negatively regulated by miR-708, which can directly target TMEM88 3’-UTR regions (8). Similarly, different groups found that TMEM88 play multiple roles in liver diseases, including liver fibrosis (9), alcoholic liver disease (10), as well as non-alcoholic fatty liver disease (11).

Nevertheless, whether TMEM88 play potential roles in HCC remains unclear. Here in the current research, we identified that TMEM88 was negatively correlated with T stage, TNM stage, pathological grade, and plasma Alpha-fetoprotein (AFP) level. In addition, we confirmed the prognostic role of TMEM88 in HCC through statistical analyses. Tumor-related effects of TMEM88 in HCC were further validated through cellular and mice experiments.

Methods

Pan-cancer analysis

Expression levels of TMEM88 within normal tissues and cancer tissues (TCGA samples) were compared by Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/).

Correlation evaluation of immune infiltration of TMEM88 in HCC

Here we utilized the TIMER algorithm (http://timer.cistrome.org/) to evaluate the relationship between TMEM88 in HCC and tumor immune infiltrations (B cells, T cells, T cells, TAM (Tumor-associated macrophage), M1 macrophages (M1), M2 macrophages (M2), Monocyte, Neutrophils, and Natural killer Cell, Dendritic cell).

Cell culture and transfection

Huh7 was purchased from ATCC and cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS), penicillin (100IU/mL) and 100mg/ml streptomycin.

The plasmids containing TMEM88 coding gene and control plasmids were established by GenePharma (Shanghai, China). Overexpression was achieved by transfection using the Lipo3000 Reagent (12, 13).

Cell counting kit 8 experiments

Cellular viability was analyzed via CCK-8 kit. Briefly, after transfection and cultured for 24 hours, Huh7 cells were planted into the 96-well plates at a density of 3000 cells/well and incubated for 4 days. On each designated time point, each well was added with 10 μL of CCK-8 reagent, followed by 2 h incubation in the incubator. Finally, measurement of absorbance at 450 nm was conducted using a microplate reader.

Mice xenograft assay

The protocol of animal experiments was approved and supervised by the Ethics Committee of Xuzhou Polytechnic College of Bioengineering. BALB/c nude mice (5 weeks old) were housed at 20−22°C and 55 ± 10% humidity with 12 h day/night cycles and were free to food and water. Tumor xenograft model was established by subcutaneous injection of transfected Huh7 cells via subcutaneous injection. The volume (V) of tumor was evaluated by the tumor width (W) and length (L) and calculated according the formula: V= (W × W × L)/2. One month after subcutaneous injection, mice were euthanatized and satisficed to isolate subcutaneous tumors. After tumor resection, the tumor weight was recorded and compared.

Statistics

Data statistical analysis and graphing were performed using SPSS22.0 software and GraphPad Prism 6.0 software. Statistical differences were evaluated through Student’s t-test or One-way ANOVA test. Univariate and multivariate survival analyses were conducted for prognostic evaluation. Two sided P value less than 0.05 was considered statistical significance (14).

Results

TMEM88 was decreased in most tumor types comparing to normal tissues

To explore the possible tumor-related role of TMEM88, we firstly used in silico strategies to compare the mRNA level of TMEM88 in various tumors in TCGA datasets. As a result, comparing with normal tissues, TMEM88 was significantly decreased in bladder cancer, breast cancer, cervical cancer, colon cancer, rectum cancer, kidney chromophobe cancer, kidney renal papillary cell carcinoma, NSCLC, prostate cancer, and uterine corpus endometrial carcinoma (Figure 1A). Interestingly, the non-paired data showed that TMEM88 expressed higher in cholangiocarcinoma and HCC compared to nontumorous specimens (Figure 1A). Therefore, we further compared the differences in tumors and their paired nontumorous tissues (Figure 1B). Among the analyzed tumor types, higher TMEM88 was only observed in HCC comparing with its paired liver tissues. In contrast, lower TMEME88 or no statistical significance was observed in other tumor types (Figure 1B).

Figure 1 | TMEM88 mRNA expression in different cancers. (A) Boxplot indicates the mRNA level of TMEM88 in normal tissues and tumor samples derived from different tumor types. Data was analyzed by Wilcoxon signed rank test. (B) The mRNA level of TMEM88 in specific tumor samples and their adjacent matched nontumorous tissues were compared. Each dot represents a sample specimen. Data was analyzed by Wilcoxon rank sum test. *P<0.05, **P<0.01,***P<0.001, ns, not significant.

TMEM88 was lower in HCC tissues that possessing more malignant phenotypes

The expression data of TMEM88 thus attracted us to further investigate whether it played any distinct roles in HCC. Table 1 exhibited the basic information of the enrolled TCGA cohort. Briefly, there were 121 females and 253 males. Among them, 177 patients were diagnosed at 60 years old or younger ages, while the other 196 patients were elder at the time of diagnosis. Up to 65 patietns were characterized with serum AFP higher than 400 ng/ml. There were 55 patients with histologically grade G1, 178 pateints with G2, 124 patients with G3, and 12 patients with G4. According to the T stage, 183 patients were staged as T1, 95 patients as T2, 93 patients as T3-4. Vascular invasion was identified in 110 patients and showed negative invasion in 209 patients. Till the end of follow up, 130 patients died and 79 of them was classified as HCC-related death.

Table 1 | Correlations between TMEM88 level with HCC characteristics.

By comparing its mRNA levels with Chi-square test, we found that TMEM88 showed significant different levels in different patient subgroups (Table 1). For example, patients with higher T stages showed lower TMEM88 levels in the HCC tissues (Figure 2A, P=0.027). Consistently, higher TNM stage was correlated with lower TMEM88 mRNA level (Figure 2B, P=0.003). Similar results were observed regarding the pathological differentiation stage on that HCCs with poorer stages exhibited lower TMEM88 mRNA level (Figure 2C, P=0.048). Of note, patients with higher serum AFP levels, which was well-acknowledged to possess worse prognosis, showed lower TMEM88 mRNA level (Figure 2D, P=0.034). The negative correlations between TMEM88 and malignant characteristics of HCC indicated that TMEM88 may be correlated with HCC prognosis.

Figure 2 | TMEM88 mRNA level was correlated with certain HCC characteristics. The correlations between TMEM88 mRNA levels with HCC T stage (A), TNM stage (B), histological grade (C), and patients serum AFP level (D) were analyzed, respectively. Data was analyzed by Student’s t-test between each two groups. *P<0.05, **P<0.01.

Lower TMEM88 was correlated with worse HCC prognosis

Univariate survival analysis using either log-rank test (Figure 3A, P=0.034) or Cox hazard regression test (Table 2, P=0.035) both showed that HCC patients with lower TMEM88 showed worse disease-specific survival (DSS). HCC patients with advanced TNM stage also exhibited worse DSS [Table 1, hazard ratio (HR)=4.43, P<0.001]. However, multivariate analysis regarding DSS identified TNM stage as the only independent prognostic factor (Table 2, HR=4.21, P<0.001). Although patients with higher TMEM88 level showed a lower DSS risk (HR=0.62), the difference was not statistically significant (Table 2, P=0.062). Nevertheless, based on the multivariate data analysis, we established a nomogram model to help predict 1-year DSS of HCC patients (Figure 3B).

Figure 3 | Lower TMEM88 predicted worse disease-specific survival of HCC. (A) Kaplan-Meier survival analysis and log-rank test showed that HCC patients with lower TMEM88 mRNA level exhibited worse disease-specific survival. (B) A prognostic predicting nomogram was established according to the multivariate analysis data to help predict 1-year disease-specific survival probability of HCC patients. *P<0.05.

Table 2 | Overall survival analyses.

Consistent with the DSS, overall survival (OS) data analyses also reflected a significant prognostic role of TMEM88 in HCC (Table 3). As shown in Figure 4A, lower TMEM88 predicted a worse OS of HCC (P=0.033). Moreover, multivariate Cox hazard regression analysis revealed that patients with higher TMEM88 had a lower hazard ratio (HR=0.074). However, no statistically significant difference was identified (Table 3, P=0.111). Nomogram to predict 1-year OS of HCC patients was shown in Figure 4B.

Table 3 | Overall survival analyses.

Figure 4 | Lower TMEM88 predicted worse overall survival of HCC. (A) Kaplan-Meier survival analysis and log-rank test showed that HCC patients with lower TMEM88 mRNA level exhibited worse overall survival. (B) A prognostic predicting nomogram was established according to the multivariate analysis data to help predict 1-year overall survival probability of HCC patients *P<0.05.

TMEM88 was correlated with immune infiltration of HCC

By analyzing the correlations between TMEM88 levels with the immune cell enrichment, we observed that TMEM88 may be involved in the HCC tumor microenvironment (Figure 5A). For example, higher TMEM88 was positively correlated with NK cell enrichment (Figure 5B), pDC cell enrichment (Figure 5C), CD8+ T cell enrichment (Figure 5D). In contrast, TMEM88 level was negatively correlated with Th2 cell enrichment (Figure 5E).

Figure 5 | TMEM88 was correlated with immune cell enrichment in HCC microenvironment. (A) The correlations between immune cell enrichment and TMEM88-mRNA level in HCC tissues were summarized. Spearman correlation tests indicated positive correlations between TMEM88-mRNA level and NK cell enrichment (B), pDC cell enrichment (C), CD8+ T cell enrichment (D). In contrast, TMEM88 level was negatively correlated with Th2 cell enrichment (E). *P<0.05, **P<0.01,***P<0.001, ns, not significant.

TMEM88 suppressed HCC progression both in vitro and in vivo

Since clinical evidence implied a potential tumor-suppressing role of TMEM88, we next conducted cellular experiments to validate its detailed effects in HCC. TMEM88 plasmids were transfected into Huh7 cells. As a result, CCK-8 proliferation assay revealed an attenuated HCC growth after overexpressing TMEM88 (Figure 6A), highlighting the crucial role of TMEM88 as a novel tumor suppressor.

Figure 6 | TMEM88 suppressed HCC progression both in vitro and in vivo. (A) CCK-8 assay was conducted to test the proliferation capacity of TMEM88-overexpressed HCC cells and control cells. (B) Growth curve of mice subcutaneous xenografts were monitored and recorded. (C) The tumor weights of isolated xenografts were tested and compared in the two groups. (D) The photographed images of resected xenografts from nude mice. ***P<0.001

Finally, we conducted in vivo analysis using nude mice to establish xenograft model. By monitoring the xenograft growth curve, we confirmed that overexpressing TMEM88 attenuated HCC growth (Figure 6B). Consistent with the growth curve, isolated xenografts established by TMEM88-overexpressing cells showed significant lighter tumor weight and smaller tumor size compared to those established by control cells (Figures 6C, D).

Discussion

Involvement of TMEM88 in different malignancies has been reported by different research groups worldwide (15, 16). For example, low TMEM88 expression was identified in both thyroid cancer specimens and corresponding cell lines. Overexpressing TMEM88 remarkably attenuated proliferation and invasion capacities of thyroid carcinoma cells (17). Although a previous gene-sequencing study suggested that TMEM88 combined with several other genes can help distinguish high- or low-risk groups of HCC, their data didn’t illustrated the independent prognostic role of TMEM88 in HCC (18). Meanwhile, their study lacks the experimental verification regarding the detailed roles of TMEM88 in HCC progression.

In this study, we observed a negative correlation between TMEM88 expression and unfavorable prognostic characteristics of HCC, including advanced tumor stage, poorer differentiation grade, and higher serum AFP level. Additionally, we found that higher levels of TMEM88 were associated with improved overall survival and disease-specific survival in HCC patients. We also demonstrated that overexpression of TMEM88 in Huh7 cells resulted in decreased cell proliferation, confirming its role in inhibiting HCC progression. The in vivo xenograft experiments further validated the anti-tumor effects of TMEM88 in HCC, which is consistent with previous findings in bladder cancer (19).

Besides tumor progression, TMEM88 was also reported to be associated with drug resistance. Maria et al.’s data showed that platinum resistant xenografts generated by ovarian cancer cells exhibited significantly lower methylation level of TMEM88, comparing to that of platinum sensitive xenografts. Interestingly, although knockdown of TMEM88 enhanced ovarian cancer cell proliferation, it also re-sensitized cells to platinum treatment. Therefore, TMEM88 plays an important role in platinum resistance. Moreover, considering that Wnt signaling is involved in promoting resistance to cisplatin, docetaxel, radiotherapy, etc, it’s high likely that TMEM88 may help restore therapeutic sensitivity in tumors, highlighting its potential role in tumor treatment.

Apart from its role in tumor progression, TMEM88 has also been implicated in drug resistance. For example, in ovarian cancer, lower methylation levels of TMEM88 were observed in platinum-resistant xenografts compared to platinum-sensitive ones. Interestingly, knockdown of TMEM88 in ovarian cancer cells enhanced cell proliferation but also resensitized them to platinum treatment (20), highlighting the potential role of TMEM88 in restoring therapeutic sensitivity in tumors. Furthermore, given the involvement of Wnt signaling in promoting resistance to various therapies (21), including cisplatin, docetaxel, and radiotherapy, TMEM88 may have broader implications in tumor treatment.

Our study has several limitations. Firstly, we didn’t distinguish the subcellular localization of TMEM88 in HCC, which would be an important aspect. Although TMEM88 is normally localized in cell membrane, Zhang’s data and Yu’s data suggested that cytosolic mislocalization of TMEM88 promotes the progression of NSCLC and triple-negative breast cancer, respectively (22, 23). In contrast, nuclear localization of TMEM88 was negatively correlated with lymph node metastasis in triple-negative breast cancer, implying that TMEM88 may play completely distinct tumor-related roles relying on its subcellular localization (23). Secondly, we didn’t dig into the detailed upstream regulators and downstream effectors of TMEM88. It has been reported that miR-708 can directly bind to TMEM88 and inhibits its functions in human hepatic stellate cells (7). Interestingly, overexpression of TMEM88 in lung adenocarcinoma cells also reduced TMEM88 expression and enhanced cancer cell proliferation and invasion (24), suggesting the possible regulating manner in HCC. In addition, promoter methylation of TMEM88 can downregulate TMEM88 expression and participates in modulating its downstream pathways. For example, Ma et al. reported that NSCLC patients with high TMEM88 methylation exhibited worse prognosis than those with low TMEM88 methylation (25). Although the downstream signaling pathway of TMEM88 in HCC was not explored here, it is high likely that TMEM88 functions through inhibiting Wnt/β-catenin signaling pathways as reported in various cancer types (16, 17, 19). Nevertheless, further molecular biological studies will be necessary to further illuminate the signaling network of TMEM88 in different malignancies.

Conclusions

Taken together, our data suggested an anti-tumor effect of TMEM88 and verified its role as a novel prognostic biomarker in HCC.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

Ethics statement

The animal study was reviewed and approved by Ethics Committee of Xuzhou Polytechnic College of Bioengineering.

Author contributions

LC designed this project. YD supervised this study and conducted clinical data analysis. KS conducted cellular experiments. PP helped with mice experiments. FH contributed to statistical analyses. All authors contributed to the article and approved the submitted version.

Fundings

This study was supported by the Educational Science Reform Project of the National Biotechnology Vocational Education Teaching Steering Committee (No. XM202120) and Key Topics of the “14th Five-Year Plan” of Jiangsu Educational Science (No. C-b/2021/03/37).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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SYSTEMATIC REVIEW
published: 17 April 2023
doi: 10.3389/fimmu.2023.1119168

Prognostic value of programmed cell death ligand 1 expression in patients with intrahepatic cholangiocarcinoma: a meta-analysis

Feng Xian ¹,², Dacheng Ren ², Jun Bie ² and Guohui Xu ³*

¹ School of Medicine, University of Electronic Science and Technology of China, Chengdu, China, ² Department of Oncology, Nanchong Central Hospital, the Second Clinical Medical College, North Sichuan Medical College, Nanchong, China, ³ Department of Interventional Radiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Albino Eccher, Integrated University Hospital Verona, Italy
Bing Feng, Pennington Biomedical Research Center, United States
Jian Zhou, Chinese Academy of Sciences (CAS), China

*Correspondence:
Guohui Xu
xfyixue@sina.com

Specialty section:
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

Received: 08 December 2022

Accepted: 03 April 2023

Published: 17 April 2023

Citation:
Xian F, Ren D, Bie J and Xu G (2023) Prognostic value of programmed cell death ligand 1 expression in patients with intrahepatic cholangiocarcinoma: a meta-analysis. Front. Immunol. 14:1119168. doi: 10.3389/fimmu.2023.1119168

Background

Programmed cell death ligand 1 (PD-L1) is highly expressed in intrahepatic cholangiocarcinoma (ICC) tissues. But there is still a dispute over the prognostic value of PD-L1 in patients with ICC. This study aimed to evaluate the prognostic value of PD-L1 expression in patients with ICC.

Methods

We performed a meta-analysis based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines. We searched the literature from PubMed, Embase, Web of Science, and the Cochrane Library up to December 5, 2022. Hazard ratios (HR) and their 95% confidence intervals (95% CI) were calculated to analyze the overall survival (OS), recurrence-free survival (RFS), and time to relapse. The quality of the studies was assessed using the Newcastle-Ottawa scale. Publication bias was assessed using a funnel plot and Egger’s test.

Results

Ten trials with 1944 cases were included in this meta-analysis. The results showed that the low-PD-L1 group had a statistically significant advantage in OS (HR, 1.57; 95% CI, 1.38–1.79, P <0.00001), RFS (HR, 1.62; 95% CI, 1.34–1.97, P <0.00001), and time to relapse (HR, 1.60; 95% CI, 1.25–2.05, P = 0.0002) compared with the high-PD-L1 group. High programmed cell death (PD1)levels, on the other hand, were correlated with poorer OS (HR, 1.96; 95% CI, 1.43–2.70; P <0.0001) and RFS (HR, 1.87; 95% CI, 1.21–2.91; P = 0.005). Multivariate analysis showed that PD-L1 could act as an independent predictor for OS (HR, 1.48; 95% CI, 1.14–1.91; P = 0.003) and RFS (HR, 1.74; 95% CI, 1.22–2.47; P = 0.002), and PD1 acted as an independent predictor for OS (HR, 1.66; 95% CI, 1.15–2.38; P = 0.006).

Conclusion

This meta-analysis demonstrated that high PD-L1/PD1 expression is associated with poor survival in ICC. PD-L1/PD1 may be a valuable prognostic and predictive biomarker and potential therapeutic target in ICC.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022380093.

Keywords: intrahepatic carcinoma, prognostic value, meta-analysis, programmed cell death ligand 1 (PDL1), programmed cell death 1 (PD-1)

1 Introduction

Intrahepatic cholangiocarcinoma (ICC) accounts for over 20% of primary liver cancers and is the second most common primary liver tumor after hepatocellular carcinoma (1). ICC is a highly lethal neoplasm because it is always misdiagnosed and there is no effective therapeutic regimen to control it, which is not sensitive to chemotherapy and radiotherapy (2). Therefore, an effective therapeutic target has recently been focused on ICC worldwide.

The programmed cell death (PD) ligand 1 (PD1)/PD-L1 axis is one of the most important immune checkpoints and a valuable therapeutic target because it plays a critical role in facilitating immune evasion (3). Tumor cells express PD-L1, which binds to PD1 to evade antitumor responses (4). PD-L1 also plays an important role in subsequent tumor progression by binding to PD1 and activating proliferative and survival signaling pathways (5). Furthermore, PD-L1 expression can be modulated by various tumorigenesis-related signaling pathways, including the phosphoinositide 3-kinase–protein kinase B/Akt (PI3K/AKT) (6–8), mitogen-activated protein kinase (MAPK) (9), and Wingless/Integrated (WNT) pathways (10). An increasing number of studies have clearly demonstrated that PD-L1 is overexpressed in various malignant tumors, such as nasopharyngeal carcinoma, lung cancer, and hepatocellular carcinoma (11–16). Additionally, PD-L1 expression is significantly associated with prognosis and clinicopathological features; for example, PD-L1 overexpression contributes to poor prognosis in gastric, colorectal, breast, and pancreatic cancers, and renal cell and hepatocellular carcinomas (17–21). Furthermore, many studies have found that PD-L1 is highly expressed in ICC and is markedly related to the prognosis of patients with ICC (22, 23). However, few studies have reported the efficacy of anti-PD1/PD-L1 treatment for ICC (24, 25), and PD-L1 as a therapeutic target for ICC is still in demand, which requires further evaluation.

Therefore, we performed this meta-analysis to assess the effect of PD-L1 expression on the prognosis of ICC. The purpose of this study was to evaluate the correlation between PD-L1 overexpression and survival in ICC, thereby shedding more light on the development of PD-L1 targeted therapy and prognostic prediction.

2 Materials and methods

This study has been conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (26, 27) (Supplementary Table 1).

2.1 Search strategy

Two investigators independently searched the online databases PubMed, Embase, Cochrane Library, and Web of Science for interrelated studies up to December 5, 2022. The search terms were “intrahepatic cholangiocarcinoma” AND “programmed cell death ligand 1” OR “PD-L1” OR “B7-H1” (Supplementary Table 2). Reference lists from published studies were also searched.

2.2 Inclusion and exclusion criteria

The inclusion criteria were as follows: (1) patients who were diagnosed with ICC; (2) articles that were published in English with full texts, involving humans as study subjects; (3) the correlation between PD-L1 and survival (overall survival [OS], recurrence-free survival [RFS], and time to relapse [TTR]) was detected; (4) the hazard ratio (HR) and 95% confidence intervals (CI) for survival times were computed by included articles that provided sufficient data; and (5) PD-L1 expression levels were gauged in clinical ICC tissues. The exclusion criteria were as follows: (1) patients with ICC and other cancers; (2) case reports, abstracts, and reviews; (3) duplicated articles; and (4) articles with incomplete data.

2.3 Data extraction

Two reviewers independently extracted the data. Any disagreements regarding the information were resolved by consensus or by the judgment of a third reviewer. The following data were extracted from each study: authors of the article, year of publication, detection method, region, cutoff value, follow-up time, and HRs with 95% CI (28). The main outcomes were PD-L1 expression-related OS, RFS, and TTR. The following data were also extracted if the study contains: PD1 expression-related survival data. While the original survival data were hardly accessed, the extracted data from the Kaplan–Meier curves were obtained using the software Engauge Digitizer version12.1.

2.4 Quality assessment

Two reviewers independently evaluated the quality of the included literature using a 9-score system of the Newcastle-Ottawa Scale (NOS) (29). The NOS comprises eight items, with a score >6 indicating high-quality research.

2.5 Statistical analysis

All data analyses were performed using Review Manager version 5.3 and Stata Software version 14.0. We applied pooled HRs with their 95% CIs to evaluate the association between prognostic value and the expression levels of PD-L1 in ICC. Cochran’s Q and I² statistics were used to assess the heterogeneity among these articles (30). Heterogeneity was considered insignificant when P >0.10 or I² <50%, and a fixed-effects model was used to pool the effects; otherwise, a random-effects model was used. Funnel plot and Egger’s test were used to estimate publication bias, and a P value of <0.05 implied statistical significance.

3 Results

3.1 Selected studies

We searched for 329 trials in four electronic databases. Subsequently, we excluded 107 duplicates, 182 records after screening the titles and abstracts, and 30 records due to irrelevant or incomplete data. Eventually, ten trials (22, 31–39) and 1944 cases were included according to the inclusion and exclusion criteria (Figure 1).

Figure 1 | Flow diagram of study selection.

3.2 Study characteristics and quality assessment

Nine studies were included from Asia (China, Korea, and Japan), and one was from Germany. All of the included studies used either immunohistochemistry or immunofluorescence to detect PD-L1 expression. Moreover, we extracted two sets of survival data from one trial with the training and validation cohorts. Four studies reported the effect of PD1 on the prognosis of ICC. The quality of the included trials was assessed by NOS, and all included studies were high-quality studies. The NOS scores and characteristics of the included studies are summarized in Table 1, the quality evaluations of all included articles are in Supplementary Table 3.

Table 1 | The characteristics of the included studies.

3.3 OS

Regarding the effect of PD-L1 expression on OS, the low-PD-L1 group had a significantly better OS (HR, 1.57; 95% CI, 1.38–1.79; P <0.00001; I², 26%; P = 0.22) than the high-PD-L1 group (Figure 2A). Furthermore, multivariate analysis showed that PD-L1 was also a significant independent prognostic factor (HR, 1.48; 95% CI, 1.14–1.91; P = 0.003; I², 14%; P = 0.31) (Figure 2B). While concerning the effect of PD1 expression on OS, a statistically significant difference in OS (HR, 1.96; 95% CI, 1.43–2.70; P <0.0001) was found between the PD1 low and high groups (Figure 2C). Multivariate analysis showed that PD1 was also a significant independent prognostic factor (HR, 1.66; 95% CI, 1.15–2.38; P = 0.006) (Figure 2D). Namely, both high PD-L1 and PD1 expression were associated with poor OS of patients with ICC.

Figure 2 | A forest plot of OS between low- and high-PD-L1 expression: (A) pooled OS by univariate analysis; (B) pooled OS by multivariate analysis. A forest plot of OS between low- and high-PD1 expression: (C) pooled OS by univariate analysis; (D) pooled OS by multivariate analysis.PD-L1, programmed cell death ligand 1; OS, overall survival; SE, standard error.

3.4 RFS

Regarding the effect of PD-L1 expression on RFS, the low-PD-L1 group had a statistically significant advantage in RFS (HR, 1.62; 95% CI, 1.34–1.97; P <0.00001) in univariate analysis and (HR, 1.74; 95% CI, 1.22–2.47; P = 0.002) (Figures 3A, B), means that the high level of PD-L1 was relevant to poor RFS and may be used to predict the recurrence time after antitumor therapy. For the effect of PD1 expression on RFS, there was a statistically significant difference in RFS (HR, 1.87; 95% CI, 1.21–2.91; P = 0.005) (Figure 3C), whereas multivariate analysis did not show any statistically significant difference in RFS (HR, 1.31; 95% CI, 0.85–2.04; P = 0.23; Figure 3D). The HRs of the RFS for PD1 expression were pooled using a random-effects model because there was slight heterogeneity among the three studies (I², 62%; P = 0.07). Based on the above, the PD1 overexpression could be related to poor RFS, but there were not any correlations between PD1 levels and RFS in multivariate analysis.

Figure 3 | A forest plot of RFS between low- and high-PD-L1 expression: (A) pooled RFS in univariate analysis; (B) pooled RFS in multivariate analysis. A forest plot of RFS between low- and high-PD1 expression: (C) pooled RFS in univariate analysis; (D) pooled RFS in multivariate analysis. RFS, recurrence-free survival; PD-L1, programmed cell death ligand 1; SE, standard error.

3.5 TTR

Two cohorts in one trial reported the TTR of patients with PD-L1 expression. The results showed that the low-PD-L1 group had a statistically significant advantage in TTR (HR, 1.60; 95% CI, 1.25–2.05; P = 0.0002; I², 0%; P = 0.49) (Figure 4). So, the low level of PD-L1 was beneficial to well TTR of ICC patients.

Figure 4 | A forest plot of time to relapse between low- and high-PD-L1 expression. PD-L1, programmed cell death ligand 1; SE, standard error.

3.6 Subgroup analysis for PD-L1 expression

Considering that cut-offs for PD-L1 positivity, the scoring method may be important sources of heterogeneity, we conducted the subgroup analyses based on the cut-offs and scoring methods. The subgroups were divided by cut-offs (≤50%, >50% and not reported) and scoring methods (IHC, CPS and not reported). In OS, the results showed there were not any differences among the cut-off groups (I², 0%; P = 0.42) (Figure 5A), and the scoring method groups (I², 40.9%; P = 0.18) (Figure 5B). Besides, the cut-off groups also had no differences (I², 0%; P = 0.87) (Figure 5C). Therefore, the cut-offs and scoring methods may not be the important sources of heterogeneity, means the different cut-offs and scoring method could not affect the pooled HRs.

Figure 5 | The subgroup analyses for PD-L1 expression related OS and RFS. (A) the subgroup analyses for PD-L1 expression related OS based on different cut-offs, (B) the subgroup analyses for PD-L1 expression related OS based on different scoring methods, (C) the subgroup analyses for PD-L1 expression related RFS based on cut-offs. NR, not reported.

3.7 Sensitivity analysis

We conducted a sensitivity analysis for OS (Figure 6A), RFS (Figure 6B), and TTR (Figure 6C) for PD-L1 expression and OS (Figure 6D) and RFS (Figure 6E) for PD1 expression in ICC by separately removing each study and then merging the effect quality. After excluding each study from the main analysis, the overall results showed no significant changes, which suggests that our combined results are reliable.

Figure 6 | Sensitivity analysis of the meta-analysis: (A) OS in PD-L1 expression; (B) RFS in PD-L1 expression; (C) TTR in PD-L1 expression; (D) OS in PD1 expression; (E) RFS in PD1 expression. PD-L1, programmed cell death ligand 1; OS, overall survival; RFS, recurrence-free survival.

3.8 Publication bias

The funnel plots and Egger’s test were used to test for potential publication bias. The funnel plot showed that the left and right sides were symmetrical (Figures 7A–E). Egger’s test showed that all P >0.1, indicating a low probability of publication bias.

Figure 7 | Funnel plot of the meta-analysis: (A) OS in PD-L1 expression; (B) RFS in PD-L1 expression; (C) TTR in PD-L1 expression; (D) OS in PD1 expression; (E) RFS in PD1 expression. PD-L1, programmed cell death ligand 1; OS, overall survival; RFS, recurrence-free survival; TTR, time to relapse.

4 Discussion

The high recurrence rate after surgical removal and low disease control response to treatment for ICC have become a problem for doctors and patients. This study is the first meta-analysis of the prognostic value of PD-L1 expression in patients with ICC. The results showed the prognostic value of PD-L1/PD1 staining in ICC.

In our study, the OS of the low-PD-L1 group was 1.57 times longer than that of the high group. This finding indicates that the prognosis of patients with high PD-L1 levels is worse due to the ability of PD-L1 to facilitate immune evasion (3). Interferon-γ can induce protein kinase D isoform, which can cause PD-L1 upregulation in various solid cancers, and inhibiting its activity could suppress PD-L1 expression and promote an obvious antitumor immune response (40). Moreover, the multivariate analysis also showed that the OS of the low-PD-L1 group was significantly different (HR, 1.96; P<0.0001), indicating that PD-L1 expression was an independent indicator of prognosis in patients with ICC. This finding is consistent with the feature of PD-L1 as a pro-tumorigenic factor (5).

The low-PD-L1 group also had statistically significant advantages in RFS and TTR compared to the high-PD-L1 group (HR, 1.74; P <0.00001 vs. HR, 1.60; P = 0.0002, respectively). These findings confirmed that patients with high PD-L1 expression had significantly higher recurrence rates than those with low PD-L1, especially after curative resection. Multivariate analysis showed that the RFS of the low-PD-L1 group was 1.74 times longer than that of the high-PD-L1 group. This indicates that PD-L1 levels could act as independent predictors of RFS and elevated recurrence. These results may be correlated with the fact that PD-L1 is active and maintains the proliferation of tumor cells (5). For example, the intrinsic pathway of PD-L1 promotes renal cell carcinoma progression by inducing stem cell-like phenotypes in renal cancer cells (41).

Furthermore, we analyzed the effect of PD1 expression on the prognosis of ICC. The results showed that the OS and RFS of PD1 low levels were statistically significantly longer than those of high levels (HR, 1.66; P = 0.006 vs. HR, 1.87; P = 0.005, respectively), which illustrated that PD1 has potential prognostic value for ICC. Multivariate analysis showed that PD1 also acted as an independent predictor for the OS of ICC (HR, 1.87; P = 0.005). PD1 is mainly expressed in activated immune cells as an inhibitor of innate immune responses. PD1 regulates the immune microenvironment by binding to PD-L1; therefore, in theory, PD1 has a similar predictive effect to PD-L1 (42). However, no statistically significant difference in RFS was observed in the multivariate analysis (HR, 1.31; P = 0.23), indicating that PD1 was not an independent predictor of RFS. Which is different from the relationship between PD1 and RFS in other cancers, such as PD1 positivity was an independent predictor of RFS in renal cell carcinoma (43). The number of infiltrated immune cells and the limited number of trials that were included could change the results. The correlation between PD1 expression and the RFS of ICC need to be further explored in future studies.

Therefore, PD-L1/PD1 inhibitors can be used as effective target medications for patients with ICC who exhibit PD-L1/PD1 overexpression. Zhu et al. reported that stage IIIB ICC could be treated with neoadjuvant therapy with a PD1 inhibitor (44). PD-L1/PD1 inhibitors combined with targeted therapy, chemotherapy, and radiotherapy had a synergistic effect on ICC, which improved disease control responses (24, 45, 46). PD-L1/PD1 inhibitors have achieved good results in the treatment of advanced or neoadjuvant ICC. We expect that this regimen could be utilized for patients with ICC as a first-line treatment rather than a salvage treatment.

In order to exclude other factors, we conducted subgroup analyses based on cut-offs and scoring methods. The results showed that both them did not change the pooled results, which is similar to some studies (47). For example, good overall concordance on analytic performance was observed for two assays (Dako 22C3 and Ventana SP263) with both scoring algorithms: the combined positive score (CPS) and tumor infiltrating immune cells (IC), but the SP 142 showed lower positivity rates, especially using the CPS algorithm (48). So different scoring methods could still affect the results. Besides, there were a similar correlation between PD-L1 levels and survival in papillary thyroid carcinoma, PD-L1 expression was significantly associated with a reduced disease-free survival (49). However, no association was found with the OS (49), which is opposite to the results in ICC. Which may be related to the different intensity of promoting cancer progression of PD-L1 in different cancers. Based on the above, the influence of PD-L1 expression on tumor prognosis may vary with different IHC assays, scoring methods and tumor types.

Our study has several limitations. First, due to a lack of data, further subgroup analysis according to clinicopathological features was not conducted. Second, because the majority of patients in our study were from Asia, the results could not fully represent other groups worldwide. Finally, the sample sizes of the included studies were insufficient to conduct a good pooled analysis, especially for PD1. Therefore, we expect that more valuable related trials will be reported in the future.

5 Conclusion

The meta-analysis suggested that high-PD-L1 expression levels predicted poorer OS, RFS, and TTR in patients with ICC. PD1 expression levels are also related to the prognosis of patients with ICC. Moreover, both PD-L1 and PD1 were independent predictors of OS, and PD-L1 was an independent predictor of RFS. Our results indicate that PD-L1/PD1 has great potential as an effective prognostic biomarker and therapeutic target in ICC.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author contributions

FX and GX designed the study. FX and DR screened the studies and extracted data. The quality of the evidence was assessed using FX and JB. FX and DR analyzed and interpreted the data. FX and DR prepared figures and drafted the manuscript. JB and GX contributed to reviewing and editing the manuscript. All authors have approved the final version of the article, including the authorship list. All authors contributed to the article.

Funding

This study was funded by the Natural Science Foundation of Sichuan (No. 23ZDYF1517). Science and Technology Project of Nanchong (No. 22SXQT0066).

Acknowledgments

We want to thank Jing Wu from North Sichuan Medical College in Nanchong, China, for providing guidance on statistical analysis.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1119168/full#supplementary-material

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ORIGINAL RESEARCH
published: 28 July 2023
doi: 10.3389/fimmu.2023.1116034

Functional reprogramming of peripheral blood monocytes by soluble mediators in patients with pancreatic cancer and intraductal papillary mucinous neoplasms

Austin M. Eckhoff ¹†, Michael C. Brown ²†, Karenia Landa ¹, Ibtehaj Naqvi ³, Eda K. Holl ¹, David Boczkowski ¹, Ashley Fletcher ¹, Kristen E. Rhodin ¹, Minh Huy Giang ², Bruce Sullenger ¹, Georgia M. Beasley ¹, Peter J. Allen ¹ and Smita K. Nair ¹,²,⁴*

¹ Department of Surgery, Duke University, Durham, NC, United States, ² Department of Neurosurgery, Duke University, Durham, NC, United States, ³ Department of Anesthesiology, Duke University, Durham, NC, United States, ⁴ Department of Pathology, Duke University, Durham, NC, United States

Edited by:
Hongda Liu, Nanjing Medical University, China

Reviewed by:
Alex Jaeger, Moffitt Cancer Center, United States
Anm Nazmul Hasan Khan, University at Buffalo, United States
Yuting Ke, Massachusetts Institute of Technology, United States

*Correspondence:
Smita K. Nair
smita.nair@duke.edu

†These authors have contributed equally to this work and share first authorship

Received: 05 December 2022

Accepted: 04 July 2023

Published: 28 July 2023

Citation:
Eckhoff AM, Brown MC, Landa K, Naqvi I, Holl EK, Boczkowski D, Fletcher A, Rhodin KE, Giang MH, Sullenger B, Beasley GM, Allen PJ and Nair SK (2023) Functional reprogramming of peripheral blood monocytes by soluble mediators in patients with pancreatic cancer and intraductal papillary mucinous neoplasms. Front. Immunol. 14:1116034. doi: 10.3389/fimmu.2023.1116034

Background

Monocytes and monocyte-derived tumor infiltrating cells have been implicated in the immunosuppression and immune evasion associated with pancreatic adenocarcinoma (PDAC). Yet, precisely how monocytes in the periphery and tumor microenvironment in patients with intraductal papillary mucinous neoplasm (IPMN), a precursor lesion to PDAC, change during disease progression has not been defined. Here we functionally profiled the peripheral immune system and characterized the tumor microenvironment of patients with both IPMN and PDAC. We also tested if sera from patients with IPMN and PDAC functionally reprogram monocytes relative to that of healthy donors.

Methods

Pancreatic tissue and peripheral blood were collected at the time of resection from 16 patients with IPMN and 32 patients with PDAC. Peripheral blood and pancreatic tissue/tumor were immunophenotyped using flow cytometry. Whole blood was plated and incubated with R848 (a TLR 7/8 agonist) or LPS (a TLR4 agonist) for 6 hours and TNF expression in monocytes was measured by flow cytometry to measure monocyte activation. To test if TLR sensitivity is determined by factors in patient sera, we preconditioned healthy donor monocytes in serum from PDAC (n=23), IPMN (n=15), or age-matched healthy donors (n=10) followed by in vitro stimulation with R848 or LPS and multiplex cytokine measurements in the supernatant.

Results

TNF expression in R848-stimulated peripheral blood monocytes was higher in patients with low grade vs high grade IPMN (65% vs 32%, p = 0.03) and stage 1 vs stage 2/3 PDAC (58% vs 42%, p = 0.03), this was not observed after LPS stimulation. TLR activation correlated with increasing grade of dysplasia from low grade IPMN to high grade IPMN. Serum from patients with IPMN and PDAC recapitulated suppression of TNF induction after R848 stimulation in naïve, healthy donor monocytes.

Conclusion

Peripheral blood monocyte TNF secretion inversely correlates with the degree of dysplasia in IPMN and cancer stage in PDAC, suggesting innate immune reprogramming as IPMNs progress to invasive disease. These effects are, at least in part, mediated by soluble mediators in sera.

Keywords: pancreatic cancer, IPMN, monocyte, innate immunity, TLR

1 Introduction

Intraductal papillary mucinous neoplasms (IPMN) are cystic precursors to pancreatic adenocarcinoma (PDAC) that account for 15-20% of PDAC cases (1). These precursor lesions are detectable on radiographic imaging and exist on a spectrum of disease from low grade to high grade to those harboring invasive PDAC (2). Early detection and predicting which lesions will develop invasive disease provides an opportunity to intervene prior to the onset of PDAC which is known for its high mortality and immune resistance (1, 3, 4).

Prior work has attributed tumor associated macrophages (TAMs) to PDAC oncogenesis, suggesting that TAMs are critical in creating an immunosuppressive microenvironment and contribute to the extensive desmoplastic remodeling that limits drug delivery (5, 6). However, less is known about the systemic and tumor microenvironment of IPMN. Recently, Bernard et al. performed single cell sequencing of six IPMN tumors and found that as IPMNs progress from low grade dysplasia (LGD) to invasive adenocarcinoma, there is increasing infiltration of myeloid-derived suppressor cells accompanied by T cell depletion (7). Additionally, our own group used spatial transcriptomics to characterize the tumor microenvironment of IPMNs and found that macrophages were generally more abundant in regions of high grade dysplasia (HGD) compared to LGD within the same tumor (8).

Although these prior studies have described the tumor immune cell infiltrate and noted increased myeloid derived cells in HGD IPMN, the functional status of myeloid cells remain undefined, and the systemic immune response is not well described. Elucidating immunologic hallmarks of PDAC development from precursor lesions is expected to inform the development of novel interventions that counter disease progression and/or identification of prognostic biomarkers. Therefore, we sought to comprehensively profile the IPMN tumor microenvironment and systemic immune system with a particular focus on measuring monocyte function. We hypothesized that as IPMNs progress from LGD to HGD, monocytes become more dysfunctional, contributing to innate immune tolerance/reprogramming.

2 Materials and methods

2.1 Patient recruitment

This study was approved by the Duke University Health System Institutional Review Board (Pro#00100930). Eligible patients were 18 years or older with a diagnosis of IPMN or PDAC who were proceeding to standard of care surgical resection at Duke University Hospital between December 2018 and March 2022. Patients consented to research blood and tumor tissue collection though the Duke BioRepository and Precision Pathology Center (BRPC) at Duke University School of Medicine. Matched blood and tumor tissue from consented patients was obtained by the BRPC and used for examination of the cellular immunome. A board-certified pathologist confirmed tissue diagnosis by separate analysis of tissue sections. Clinicopathological data was prospectively collected by study coordinators and securely stored in a RedCap database.

2.2 Blood and tumor processing

Blood was collected on the day of surgery prior to surgical incision. Blood for flow cytometry was obtained by venipuncture and collected in Vacutainer collection tubes containing Heparin or EDTA anticoagulant (BD Biosciences). Blood was rotated on a shaker at room temperature until flow cytometry analysis (10 minutes-4 hours post collection). Blood for serum assays was collected in tubes without anticoagulant (BD Biosciences). Surgically resected tumors were collected and stored in MACS tissue storage solution at 4°C (Miltenyi). Storage time was 1–16 hours post-tumor collection. Tumor tissue was mechanically disrupted with scissors until tumor pieces were 1-2 mm. PDAC was digested using the Tumor Dissociation Kit and Gentle MACS mechanical dissociator (Miltenyi) following manufacturer’s recommendations. IPMN tissue was digested with a separate protocol which is detailed below. Tissue and digestion media (RPMI with 10% FBS, 10mM Hepes, 5mM CaCl₂ 1x protease inhibitor cocktail, 1x trypsin inhibitor and 100 U/ml DNase) was heated to 37°C for 20-40 minutes until homogenization was achieved. Both PDAC and IPMN tissues were filtered 3 times using a 70 μM cell strainer to remove undigested tissue and dead cell debris and resuspended in 1 ml of PBS. The resulting single cell suspension was immediately analyzed by flow cytometry.

2.3 Flow cytometry

All analysis was performed using DuraClone IM (Immune Monitoring) basic antibody panel (CD45, CD3, CD4, CD8, CD19, CD14, CD16, CD56) (9). For peripheral blood analysis, 100 μl of blood was added to the basic panel tube and cells were processed according to the manufacturer’s instructions. In brief, blood was incubated with the antibodies for 15 min in the dark, followed by red blood cell lysis using VersaLyse (Beckman Coulter) for 15 min in the dark. Cells were then washed twice in PBS prior to data acquisition. For tumor cells and tumor-infiltrating immune cells, 1 × 10⁶ cells were added to the basic panel tube and cell staining was performed as described above for peripheral blood. Additionally, propidium iodide (PI, Sigma-Aldrich, St. Louis, MO) was added as a live-dead marker to tumor single cell suspensions. All processed samples were then analyzed on a 13-color CytoFlex flow cytometer (Beckman Coulter). Data was analyzed using Kaluza Software (Beckman Coulter) as previously described (9).

2.4 Monocyte stimulation in whole blood

For the monocyte activation assay, fresh whole blood was diluted 1:10 in RPMI and Brefeldin A (BioLegend) was added at 5 µg/ml. Three ml of the blood-RPMI solution was plated in a 12-well plate. LPS, a TLR4 agonist, (100 ng/ml, InvivoGen), R848, a TLR7/8 agonist, (1 µM, InvivoGen) or PBS (negative control) were added to each well and incubated at 37°C for 6 hours. After incubation, cells were harvested from the plate using agitation and cell dissociation buffer.

Cells were analyzed using flow cytometry for surface and intracellular antigens using the Perfix-NC kit (Beckman Coulter). Briefly, 100μl of peripheral blood was incubated with 25 µl of Buffer R1 (fixative agent) for 15 minutes followed by a wash in 2 ml of PBS and centrifugation at 200 ×g for 5 minutes. Cells were then resuspended in 25 μl of 100% fetal calf serum followed by 300 μl of PerFix-NC Buffer R2 (permeabilizing reagent). Next, cells were incubated for 45 minutes with the following antibodies: lineage markers: CD45-KO, HLA-DR-PB, CD14-PC7 and CD16-ECD (Beckman Coulter); activation markers: TNF-APC-AF700 and IL-12-APC. Cells were then washed with 3 ml of Buffer R3 diluted 1:10 in deionized water (wash reagent) and resuspended in 500 µl buffer R3 prior to data acquisition. All processed samples were then analyzed on a 13-color CytoFlex flow cytometer (Beckman Coulter). Data were analyzed using Kaluza Software (Beckman Coulter).

2.5 Serum TLR activation assay

HEK-Blue TLR 3, 4, 8, and 9 reporter cell lines were purchased from InvivoGen, and activation in response to control agonists or human sera was determined according to the manufacturer’s instructions. These cells stably co-express a TLR gene and an NF-κB-inducible SEAP (secreted embryonic alkaline phosphatase) reporter gene that can be monitored using SEAP detection media. Briefly, these cells were plated in 96-well plates at a density of 40,000 cells per well and treated for 24 hours with either (1) media alone, (2) a control agonist for each given TLR (Poly I:C [10 µg/ml] for TLR3, LPS [1 μg/ml] for TLR4, R848 [1 µg/ml] for TLR7/TLR8, and CpG ODN [5 μM] for TLR9), (3) cancer patient sera (10 μl), (4) normal human sera (10 μl), (5) media in a final volume of 100 μl. The cell supernatant was subsequently collected and mixed with Quantiblue (InvivoGen) at a 60:40 v:v ratio and incubated for 1 hour at 37°C, after which time absorbance at 650 nm was measured using a Spectramax i3 plate reader (Molecular Devices).

2.6 Monocyte stimulation after serum preconditioning

Human peripheral blood mononuclear cells (PBMCs) derived from a LeukoPak (Stemcell Tech) were processed and cryopreserved as previously described (10). PBMCs were thawed and washed in Aim-V media (Gibco), resuspended in Aim-V supplemented with 1 μg/ml DNAse I (Roche) and plated into two 96 well plates (5 × 10⁵ PBMCs per well), incubating at 37°C for 1 hour to allow monocyte attachment. Non-adherent cells were removed and 100 μl of Aim-V media was added per well. Fifty microliters of serum from deidentified patients with PDAC (n=23) or IPMN (n=14), or age-matched healthy donors (n=10) was added per well. Serum was banked under a Duke IRB approved protocol. Monocytes were incubated with sera for 48 hours, followed by removal of sera containing media, gentle washing with 200ul Aim-V media, and replacement of Aim-V media containing mock, R848 (1 μg/ml), or LPS (1 μg/ml). Twenty-four hours later plates were frozen at -80°C. Cytokines were measured in thawed supernatants using the Human Anti-Virus Legendplex (BioLegend). Data were collected on a FACS Canto (Duke Cancer Institute Flow Cytometry Core Facility) and analyzed using the manufacturer’s software. Data were normalized for each cytokine by dividing R848 or LPS MFI values by the respective mock control values to determine fold induction. All experiments/data collection were performed blinded to sera associated disease status.

2.7 Statistical analysis

All data were analyzed using GraphPad Prism. Statistical analysis for the flow cytometry data and TLR assays was performed using a Multiple Mann-Whitney test. Tukey’s post hoc testing was used to compare cytokine induction after R848 or LPS stimulation between IPMN, PDAC, or healthy donor serum conditioned monocytes. The designated significance level was 0.05 or less.

3 Results

3.1 Patient characteristics

For flow cytometry analysis, a total of 16 patients between December 2018 and April 2022 were prospectively identified who were undergoing pancreatic resection for IPMN. We obtained IPMN tissue for flow analysis from 14 of those patients and were able to obtain blood for flow cytometry analysis and monocyte activation analysis from 13 patients. Five patients had IPMNs with low grade (LGD) and eleven patients had IPMNs with high grade dysplasia (HGD) or invasive cancer. Basic patient demographics are in Table 1.

Table 1 | Patient demographics and tumor characteristics.

As expected, a larger percentage of the patients with HGD had main duct (45%) or mixed duct (18%) IPMN than those with LGD. Sixty-four percent of patients with HGD had IPMN lesions that harbored invasive adenocarcinoma. PDAC AJCC staging was as follows: IA (1), IB (1), IIA (1), IIB (2). After a median follow-up of 24 months, three out of five patients with PDAC had recurred, and one of the patients from the HGD cohort had died from recurrent PDAC.

As a positive control, we enrolled 32 patients with a diagnosis of PDAC between December 2018 and April 2022. We divided this cohort by AJCC 8th edition stage at the time of resection (stage 1, n = 14; stage 2/3, n = 18). Basic patient demographics and clinical characteristics are shown in Table 1. The majority of patients in both the stage 1 and stage 2/3 cohort had received neoadjuvant chemotherapy (stage 1 - 79% vs stage 2/3 - 56%) but only one patient in the stage 1 cohort had received radiation. After a median follow-up of 7 months for the PDAC cohort, 11 (34%) patients had recurrence and 8 (25%) had died from disease.

3.2 HGD IPMN is associated with attenuated TNF induction by monocytes after TLR7/8 stimulation

We analyzed matched blood and tumor tissue from patients with IPMN of varying degrees of dysplasia. Figure 1A depicts results from the whole blood analyses. We examined lymphocytes including B cells (CD19+), T cells (CD3+), CD3+/CD4+ T cells, CD3+/CD8+ T cells, and NK/NKT cells (CD56+). Total monocytes were then separated into classical monocytes (CD16-CD14+) and intermediate/nonclassical monocytes (CD16+CD14+/-), shown in Figure 1B. Additionally, because we analyzed whole blood, we were able to examine populations that are excluded when analysis is conducted on peripheral blood mononuclear cells (PBMCs), namely granulocytes (Figure 1A). Such phenotyping analyses revealed that patients with LGD had a higher percentage of T cells and CD4+ T cells when compared to patients with HGD in the peripheral blood immune cell populations Figure 1A. Otherwise no significant differences were observed in peripheral blood immune cell populations between patients with LGD and HGD. Representative analysis and flow cytometry gating strategy is presented in Supplementary Figure 1 (Figure S1).

Figure 1 | Analysis of immune cell subsets in peripheral blood and IPMN tumor. (A) Flow cytometry analysis of immune cells subsets in blood samples from LGD, HGD and healthy volunteer (control). (B) Comparison of monocyte subsets (CD16+CD14+/- and CD16-CD14+) in blood from LGD, HGD and healthy volunteer (control). (C) Flow cytometry analysis of LGD and HGD IPMN tumor. (D) Analysis of monocyte function in whole blood from patients with LGD and HGD post-stimulation with LPS, R848 and negative control (mock). P-value shown obtained with Mann-Whitney test.

Next, we examined the tumor microenvironment in patients with IPMN. Surgically resected fresh tumor tissue (matched to blood from the same patient) was processed into a single cell suspension within 16 hours of tumor harvest followed immediately by flow cytometry. Figure 1C shows basic analysis of all tumor-infiltrating lymphocytes within IPMN tissue. Dead cells were excluded and CD45 was used to distinguish immune cells from tumor cells followed by examination of CD4+ T cells, CD8+ T cells, and CD19+ B cells. We collected data from a total of 14 patients presenting with low grade (n=3) and high grade (n=11) dysplasia. Overall leukocyte infiltration in the analyzed tumor accounted for a median of 3% (interquartile range (IQR) 2-9%) of total live cells. T cells accounted for most of the immune infiltrate and accounted for 56% (IQR 43-65%) of the total immune infiltrate. No significant difference between LGD and HGD was observed in total leukocyte infiltrate or any of the immune cell subsets. Representative analysis and flow cytometry gating strategy is presented in Supplementary Figure 2 (Figure S2).

To study the innate immune system and its ability to respond to immune agonists (stressors), we performed a monocyte activation assay wherein peripheral blood was treated with R848, a TLR7/8 agonist, and intracellular TNF production was measured by flow cytometry. We were particularly interested in the monocytes’ response to the TLR7/8 agonist since TLR7/8 has been shown to increase tumor cell proliferation in human PDAC and a TLR7/8 agonist has been shown to prolong survival in a PDAC mouse model (11–13). A statistical difference was found in the monocyte activation between LGD and HGD IPMN patient samples. Figure 1D indicates that peripheral blood monocytes from patients with LGD IPMN had greater monocyte activation (LGD 67% vs HGD 28%, p = 0.03). These data may imply that peripheral monocytes from patients with HGD IPMN are tolerized to TLR 7/8 activation relative to patients with LGD IPMN. Representative analysis and flow cytometry gating strategy is presented in Supplementary Figure 3 (Figure S3).

3.3 Monocytes from patients with later stage PDAC mount weaker TNF responses to TLR7/8 stimulation

Next, we asked if a similar phenotype of monocyte activation existed in patients with PDAC. For this analysis, we separated the patients into stage 1 (n=14) and stage 2/3 (n=18) PDAC. We analyzed matched blood and tumor tissue from patients with PDAC. Phenotyping of immune subsets in peripheral blood revealed that CD56+ NK/NKT cells (p = 0.02) and monocytes (p = 0.002) were significantly enriched in the stage 1 patients compared to the stage 2/3 patients. However, no difference was observed in the peripheral monocyte subsets (CD16+CD14+/- and CD16-/CD14+) between stage 1 and stage 2/3 PDAC patients (Figures 2A, B). Phenotyping of the PDAC tumor microenvironment showed that leukocytes accounted for a median of 26% (IQR 14-43%) of total live cells within the tumor (Figure 2C). Overall leukocyte tumor infiltration was significantly higher in stage 2/3 PDAC compared to stage 1 PDAC (stage 1 6% vs stage 2/3 35%, p = 0.004). T cells accounted for most of the immune infiltrate and accounted for 67% (IQR 52-78%) of the total immune cells. Monocytes accounted for very little of the immune cell tumor infiltration and had greater tumor infiltration in stage 2/3 PDAC tumors than in stage 1 PDAC tumors (Stage 1 0.1% vs Stage 2/3 0.8%, p = 0.03) (Figure 2C).

Figure 2 | Analysis of immune cell subsets in peripheral blood and PDAC. (A) Flow cytometry analysis of immune cells subsets in blood samples from stage 1, stage 2/3 and healthy volunteer (control). (B) Comparison of monocyte subsets (CD16+CD14+/- and CD16-CD14+) in blood from stage 1, stage 2/3 and healthy volunteer (control). (C) Flow cytometry analysis of stage 1 and stage 2/3 PDAC. (D) Analysis of monocyte function in whole blood from patients with stage 1 and stage 2/3 PDAC post-stimulation with LPS, R848 and negative control (mock). P-value shown obtained with Mann-Whitney test.

Next, we examined monocyte responsiveness in the peripheral blood of patients with PDAC using the same TLR agonist that was used for IPMN functional analysis. Notably, as shown in Figure 2D, we observed patients with stage 1 PDAC had greater monocyte activation compared to patients with stage 2/3 PDAC (p = 0.03). This result mirrors our IPMN data – patients with higher grade disease, whether IPMN or PDAC, have peripheral blood monocytes that show diminished TNF induction after TLR 7/8 stimulation. Extended data in Supplementary Figure 5 (Figure S5) shows TNF alone, TNF+IL12 and total TNF expression in monocytes treated with agonists for IPMN and PDAC.

3.4 TLR Activation is induced by sera from patients with IPMN and PDAC

Monocytes and their subsets are key sensors of and responders to TLR-mediated inflammation and thus status of monocyte function could reflect the degree of systemic TLR-mediated inflammation. In a recent study, we observed that excessive TLR-mediated inflammation affects monocyte function and induces tolerance (14). Thus, we measured the ability of patients’ serum to induce TLR activation in TLR reporter cells to determine if generalized TLR mediated inflammation corresponded with monocyte function and could contribute to peripheral monocytes tolerization in patients with high grade IPMN and later stage PDAC. We hypothesized that the serum of patients with HGD IPMN would elicit greater TLR activation at baseline than those with LGD. Using TLR reporter cell lines, we found HGD samples have higher amounts of TLR activation ability than LGD IPMN for TLR3 (0.365 vs 0.294, p = 0.0001) and TLR4 (0.156 vs 0.136, p =0.0102) (Figure 3). However, we did not find any difference in activation of TLR8 and TLR9 when comparing LGD and HGD IPMN samples.

Figure 3 | Comparison of the ability of serum from patients with LGD vs HGD IPMN and patients with stage 1 and stage 2/3 PDAC to stimulate TLRs 3, 4, 8, and 9. In the IPMN groups, serum from patients with HGD stimulate TLRs 3 and 4 more significantly than patients with LGD. Stimulation of TLRs 8 and 9 are otherwise not significantly different between serum from patients with LGD vs HGD. For patients with PDAC, serum from patients with stage 2/3 PDAC trended towards higher TLRs 3, 4, 8, and 9 stimulation when compared to serum from patients with stage 1 PDAC. P-values shown obtained with Mann-Whitney test; ns= not significant (p>0.05).

Additionally, we performed TLR activation assays using the serum from patients with PDAC. We did not find a statistically significant difference in TLR activation between stage 1 PDAC vs stage 2/3 PDAC. However, across TLR3 (p = 0.05), TLR4 (p = 0.3), TLR8 (p = 0.8), and TLR9 (p = 0.6) stage 2/3 trended towards greater activation (Figure 3). To summarize, we observed higher TLR-mediated inflammation and reduced monocyte function in the blood of patients with HGD vs LGD IPMN and a similar trend in the blood of patients with higher vs lower stage PDAC. Supplementary Figure S6 shows extended data with positive and negative controls for IPMN and PDAC analysis.

3.5 Sera from patients with IPMN and PDAC reprograms TLR responses in monocytes

Exposure to TLR ligands can ‘tolerize’ responses to subsequent TLR stimulation in myeloid cells (15–17); other soluble mediators (e.g., cytokines) have also been shown to play a role in systemic immune dysfunction in cancer (18). Alternatively, increased myeloid derived suppressor cells, Tregs, and other immunosuppressive cell types may attenuate monocyte inflammatory responses. To test whether soluble mediators and/or the observed increased TLR ligands in IPMN and PDAC sera (Figure 3) are capable of mediating monocyte reprogramming, we incubated healthy donor monocytes with serum from IPMN (n=14), PDAC (n=23), or age-matched healthy donors (n=10) for 48 hours, washed to remove serum constituents, and stimulated with R848 or LPS for 24 hours (Figure 4A). Figures 4B, C shows cytokines with mean induction >5-fold. Remarkably consistent with in vitro whole blood TLR7/8 and TLR4 stimulations using blood from patients with IPMN or PDAC (Figures 1, 2), monocytes previously incubated with sera from patients with IPMN or PDAC induced weaker TNF release compared to that of healthy donors (Figures 4B, C). Intriguingly, however, this was selective for TNF, as IL-1β levels were unchanged after IPMN sera exposure but induced after PDAC sera exposure. Other tested cytokines were not different by disease status (IL-6 and GM-CSF), and TNF and GM-CSF induction in IPMN-treated monocytes after LPS stimulation was higher than that of healthy donors (Figure 4C). Notably direct analysis of serum content did not reveal significant differences in detected cytokines (IL-1β, IL-6, TNF, CXCL10, IFN-Λ1, IL-8, IFN-α2, IFN-Λ2, IFN-β, or IFN-γ; Supplementary Figure S7). Together these data imply functional reprogramming of monocytes observed in IPMN and PDAC is mediated by soluble factors.

Figure 4 | Sera from patients with IPMN and PDAC reprograms TLR responses in monocytes. (A) Healthy donor monocytes were co-cultured with 30% serum in media from healthy donors or donors with IPMN or PDAC for 48 hours. The monocytes were washed with media and treated with mock, R848 (1 µg/ml), or LPS (100 ng/ml) for 24 hours. Supernatant cytokines were measured and shown as fold mock R848 induction (B). or fold mock LPS induction (C). Cytokines induced by >5-fold mock after R848 stimulation are shown; data bars indicate mean + SEM; asterisks indicate Tukey’s post hoc test p<0.05 (two-tailed).

4 Discussion

In this study, we comprehensively profiled the systemic and local tumor microenvironment of patients with IPMN. We found only a few differences in the tumor immune cell infiltrate between patients with HGD vs LGD IPMN. For example, there were greater peripheral blood total T cells and CD4+ T cells in patients with LGD IPMN than HGD IPMN. Unique to our study is the measurement of innate immune cell function. We observed superior TNF responses in monocytes from patients with LGD IPMN after challenge with a TLR7/8 agonist. Similarly, we noted greater TNF responses in patients with stage 1 vs stage 2 PDAC. These findings indicate that monocyte reprogramming increases in both higher grade IPMNs and higher stage PDAC.

Since TLR mediated inflammation has been implicated in monocyte reprogramming, we assessed the systemic TLR stimulatory profile in patients using serum samples and TLR reporter cell lines (14). We noted increased TLR3 and TLR4 stimulation by the serum from patients with HGD vs LGD IPMN and a trend towards increased TLR stimulation by the serum from stage 2 vs stage 1 PDAC. This difference suggests a link between higher TLR-mediated inflammation and decreased monocyte function. Indeed, sera from patients with IPMN and PDAC led to suppressed TNF induction after TLR7/8 stimulation. Future studies are needed to confirm whether this is due to the presence of TLR ligands in sera or other factors. Notably, in both whole blood from patients with IPMN/PDAC as well as IPMN/PDAC patient sera treated healthy donor monocytes, TNF secretion was only impaired after TLR7/8, but not after TLR4, stimulation. This likely indicates that general suppression of the TNF transcription (e.g. epigenetic regulation) or biosynthesis does not explain these differences, but rather may indicate altered TLR-specific cell signaling processes. Indeed, the induction of GM-CSF was higher after TLR4 stimulation in IPMN sera preconditioned monocytes, and levels of IL-1β trended higher in both IPMN and PDAC. Together these data do not necessarily reflect tolerance or desensitization of monocytes to TLR stimuli, but rather reprogramming of the responses elicited by TLR stimulation.

Previous immunophenotyping of IPMNs has focused on describing the local tumor microenvironment. Single cell RNA sequencing efforts by Bernard et al. have suggested that as IPMNs progress from LGD to invasive disease, there is enriched stromal infiltration of myeloid-derived suppressor cells (MDSCs) and these stroma MDSCs acquire a tumor-promoting phenotype (7). Roth et al. utilized immunohistochemical analysis to confirm that macrophages not only increase in the peritumor stroma but also invade regions directly surrounding neoplastic cells as IPMNs progress from LGD to HGD (19). Our findings demonstrate that innate immune rearrangements are systemic, potentially via soluble mediators in serum. Monocytes have been previously implicated in cancer propagation and metastasis. In response to the chemoattractant CCL2, monocytes extravasate from the vasculature into primary tumor sites and are reprogrammed within the tumor microenvironment to limit their cytotoxity. These reprogrammed monocytes and monocyte-derived tumor associated macrophages have been shown to promote tumorigenesis by facilitating immune suppression, extracellular matrix remodeling, angiogenesis, and tumor cell migration (14, 20, 21). In patients with PDAC, Sanford et al. described an inverse correlation between monocyte prevalence in the peripheral blood and survival. Additionally, they found that human PDAC tumors had elevated CCL2 mRNA, which initiates monocyte extravasation into tissue, compared to normal pancreas (22). In a mechanistic study, Mitchem et al. targeted monocyte and macrophage tumor infiltration with CSF1R and CCR2 inhibitors in a PDAC mouse model. After CSF1R and CCR2 antagonism, they confirmed a significant reduction in tumor infiltrating monocytes and macrophages and found that tumor size decreased, an enhanced response to chemotherapy, and that hepatic and peritoneal metastasis decreased (23). Our data supports these findings and shows that as IPMN tumors undergo malignant transformation into PDAC, monocytes are also functionally altered.

We previously showed that systemic TLR inflammation can have profound effects on innate immune function, specifically that of monocytes (24). For example, patients with severe COVID-19 demonstrate excessive systemic TLR inflammation which can tolerize monocytes and that these “tolerant” monocytes are unable to respond to subsequent TLR stimulation (14). Similar to the excessive inflammation in COVID, the precancerous and cancer state of IPMN and PDAC may cause excessive TLR-mediated inflammation that reprograms monocytes. Patients with LGD IPMN, which is the least inflammatory stage of pre-cancer, have unaltered monocytes with proficient TNF responses to TLR stimulation. As the stage of the pre-cancerous lesion progresses to HGD IPMN and eventually PDAC, systemic TLR inflammation also increases. We hypothesize that this increase in systemic TLR inflammation results in further monocyte exposure to chronic stimulation, altering their response upon repeat challenge. Concordant with our findings in COVID patients, this study of patients with IPMN and PDAC supports that monocyte reprogramming is associated with poor outcomes.

A significant limitation of our study is the small sample size for the cohort of patients with IPMN. The majority of patients with IPMNs do not proceed to surgical resection so we were unable to study early LGD lesions (without concerning features on imaging). Additionally, the COVID pandemic interrupted the collection and analysis of samples because research activities were temporarily stopped at our institution, leading to a smaller sample size. In general, our institution is high volume in pancreas surgery and only about ten patients with IPMN undergo surgical resection. Thus, our 16 patient samples signify 50% patient accrual during our enrollment time period. Another limitation of our study is the short median follow up on both our IPMN and PDAC cohort. This issue limited our ability to correlate monocyte function and TLR activation with clinical characteristics such as survival. In the future, as this cohort of patients matures, we will adjust our analysis to see if the same trends apply to disease-free and overall survival.

Our observations indicate systemic changes in innate immune function in both HGD IPMN and PDAC. Such changes may have roles in enabling tumor progression and/or serve as prognostic features. Further study into whether peripheral innate immune reprogramming impacts or merely reflects tumor progression are warranted.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving human participants were reviewed and approved by Duke Health IRB. The patients/participants provided their written informed consent to participate in this study.

Author contributions

AE, MB, KL, IN, EH, DB, PA and SN contributed to conception and design of the study. SN administered the project and reviewed data. AE, MB, KL, IN, BS, GB and SN interpreted data. AE, AF and KL consented patients and biobanked surgical specimens. AE, MB, KL, IN, DB, MG, AF and KR carried out the experiments. AE, MB and IN performed the statistical analysis. AE, MB, SN and IN contributed towards manuscript writing. All authors contributed to manuscript revision and read and approved the submitted version.

Funding

This work was supported in part by the NIH T32 Training Grant T32-CA093245 for Translational Research in Surgical Oncology (AE, KL, KR), the NIH T32 Training Grant (T32-AI141342) Advanced Immunobiology Training Program for Surgeons (KL), the Duke Cancer Institute as part of the NCI Cancer Center Support Grant P30CA014236 (AE, KR) and the National Cancer Center post-doctoral award (IN). This research received no federal funding.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2023.1116034/full#supplementary-material

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