AUTHOR=Lao Yueqiong , Wang Yuqian , Yang Jie , Liu Tianyuan , Ma Yuling , Luo Yingying , Sun Yanxia , Li Kai , Zhao Xuan , Niu Xiangjie , Xi Yiyi , Zhong Ce 

TITLE=Characterization of genomic alterations and neoantigens and analysis of immune infiltration identified therapeutic and prognostic biomarkers in adenocarcinoma at the gastroesophageal junction

JOURNAL=Frontiers in Oncology

VOLUME=12

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.941868

DOI=10.3389/fonc.2022.941868

ISSN=2234-943X

ABSTRACT=<sec><title>Objectives</title><p>Adenocarcinoma at the gastroesophageal junction (ACGEJ) refers to a malignant tumor that occurs at the esophagogastric junction. Despite some progress in targeted therapies for HER2, FGFR2, EGFR, MET, Claudin 18.2 and immune checkpoints in ACGEJ tumors, the 5-year survival rate of patients remains poor. Thus, it is urgent to explore genomic alterations and neoantigen characteristics of tumors and identify CD8+ T-cell infiltration-associated genes to find potential therapeutic targets and develop a risk model to predict ACGEJ patients’ overall survival (OS).</p></sec><sec><title>Methods</title><p>Whole-exome sequencing (WES) was performed on 55 paired samples from Chinese ACGEJ patients. Somatic mutations and copy number variations were detected by Strelka2 and FACETS, respectively. SigProfiler and SciClone were employed to decipher the mutation signature and clonal structure of each sample, respectively. Neoantigens were predicted using the MuPeXI pipeline. RNA sequencing (RNA-seq) data of ACGEJ samples from our previous studies and The Cancer Genome Atlas (TCGA) were used to identify genes significantly associated with CD8+ T-cell infiltration by weighted gene coexpression network analysis (WGCNA). To construct a risk model, we conducted LASSO and univariate and multivariate Cox regression analyses.</p></sec><sec><title>Results</title><p>Recurrent <italic>MAP2K7</italic>, <italic>RNF43</italic> and <italic>RHOA</italic> mutations were found in ACGEJ tumors. The COSMIC signature SBS17 was associated with ACGEJ progression. <italic>CCNE1</italic> and <italic>VEGFA</italic> were identified as putative CNV driver genes. <italic>PI3KCA</italic> and <italic>TP53</italic> mutations conferred selective advantages to cancer cells. The Chinese ACGEJ patient neoantigen landscape was revealed for the first time, and 58 potential neoantigens common to TSNAdb and IEDB were identified. Compared with Siewert type II samples, Siewert type III samples had significant enrichment of the SBS17 signature, a lower <italic>TNFRSF14</italic> copy number, a higher proportion of samples with complex clonal architecture and a higher neoantigen load. We identified 10 important CD8+ T-cell infiltration-related Hub genes (<italic>CCL5</italic>, <italic>CD2</italic>, <italic>CST7</italic>, <italic>GVINP1</italic>, <italic>GZMK</italic>, <italic>IL2RB</italic>, <italic>IKZF3</italic>, <italic>PLA2G2D</italic>, <italic>P2RY10</italic> and <italic>ZAP70</italic>) as potential therapeutic targets from the RNA-seq data. Seven CD8+ T-cell infiltration-related genes (<italic>ADAM28</italic>, <italic>ASPH</italic>, <italic>CAMK2N1</italic>, <italic>F2R</italic>, <italic>STAP1</italic>, <italic>TP53INP2</italic>, <italic>ZC3H3</italic>) were selected to construct a prognostic model. Patients classified as high risk based on this model had significantly worse OS than low-risk patients, which was replicated in the TCGA-ACGEJ cohort.</p></sec><sec><title>Conclusions</title><p>This study provides new neoantigen-based immunotherapeutic targets for ACGEJ treatment and effective disease prognosis biomarkers.</p></sec>