Understanding the Immuno-Oncological Mechanism of Cancer Using Systems Immunology Approaches

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

The treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs) has been proven to induce lasting tumor remission. Screening suitable populations for immunotherapy through predictive markers is an important approach to improving the clinical benefits of patients. Evidence has shown that there may be a close connection between NOTCH signaling and the tumor microenvironment (TME). Hence, we explored the impact of the mutation status of NOTCH signaling on the prognosis of NSCLC patients treated with immunotherapy with the aim to apply NSCLC immunotherapy to the greatest extent possible. We examined two clinical cohorts of NSCLC patients receiving ICIs (MSKCC and NG cohorts). The mutation and prognostic data of the ICI-treated cohort were used to evaluate the association between the mutation status of NOTCH signaling and prognosis following immunotherapy. The expression and mutation data of The Cancer Genome Atlas (TCGA)-NSCLC cohort were used to analyze the differences in the immune microenvironment under different NOTCH signaling mutation states. In the ICI-treated cohorts, the univariate and multivariate Cox regression analyses indicated that high-mutated NOTCH signaling could serve as an independent predictor of NSCLC patients receiving ICIs. Patients with high-mutated NOTCH signaling had significantly improved progression-free survival (PFS) (P = 0.03, HR = 0.69; MSKCC cohort) and prolonged overall survival (OS) (P = 0.004, HR = 0.34; NG cohort). Additionally, high-mutated NOTCH signaling was related to the inflammatory immune microenvironment, inflammatory expression profile, and enhanced immunogenicity. According to this study, high-mutated NOTCH signaling may serve as a biomarker for the prediction of the prognosis of NSCLC patients treated with ICIs. A series of prospective clinical studies and molecular mechanism explorations are still needed in the future.

Background: Esophageal squamous cell carcinoma (ESCC) is one of the most common cancer types and represents a threat to global public health. N6-Methyladenosine (m6A) methylation plays a key role in the occurrence and development of many tumors, but there are still few studies investigating ESCC. This study attempts to construct a prognostic signature of ESCC based on m6A RNA methylation regulators and to explore the potential association of these regulators with the tumor immune microenvironment (TIME).

Methods: The transcriptome sequencing data and clinical information of 20 m6A RNA methylation regulators in 453 patients with ESCC (The Cancer Genome Atlas [TCGA] cohort, n = 95; Gene Expression Omnibus [GEO] cohort, n = 358) were obtained. The differing expression levels of m6A regulators between ESCC and normal tissue were evaluated. Based on the expression of these regulators, consensus clustering was performed to investigate different ESCC clusters. PD-L1 expression, immune score, immune cell infiltration and potential mechanisms among different clusters were examined. LASSO Cox regression analysis was utilized to obtain a prognostic signature based on m6A RNA methylation modulators. The relationship between the risk score based on the prognostic signature and the TIME of ESCC patients was studied in detail.

Results: Six m6A regulators (METTL3, WTAP, IGF2BP3, YTHDF1, HNRNPA2B1 and HNRNPC) were observed to be significantly highly expressed in ESCC tissues. Two molecular subtypes (clusters 1/2) were determined by consensus clustering of 20 m6A modulators. The expression level of PD-L1 in ESCC tissues increased significantly and was significantly negatively correlated with the expression levels of YTHDF2, METL14 and KIAA1429. The immune score, CD8 T cells, resting mast cells, and regulatory T cells (Tregs) in cluster 2 were significantly increased. Gene set enrichment analysis (GSEA) shows that this cluster involves multiple hallmark pathways. We constructed a five-gene prognostic signature based on m6A RNA methylation, and the risk score based on the prognostic signature was determined to be an independent prognostic indicator of ESCC. More importantly, the prognostic value of the prognostic signature was verified using another independent cohort. m6A regulators are related to TIME, and their copy-number alterations will dynamically affect the number of tumor-infiltrating immune cells.

Conclusion: Our study established a strong prognostic signature based on m6A RNA methylation regulators; this signature was able to accurately predict the prognosis of ESCC patients. The m6A methylation regulator may be a key mediator of PD-L1 expression and immune cell infiltration and may strongly affect the TIME of ESCC.