Background The immune response in the tumor microenvironment (TME) plays a crucial role in cancer progression and recurrence. We aimed to develop an immune-related gene (IRG) signature to improve prognostic predictive power and reveal immune infiltration characteristics of pancreatic ductal adenocarcinoma (PDAC). Methods The Cancer Genome Atlas (TCGA) PDAC was used to construct a prognostic model as a training cohort. International Cancer Genome Consortium (ICGC) and the Gene Expression Omnibus (GEO) database were set as validation datasets. Prognostic genes were screened by using univariate cox regression. Then, a novel optimal prognostic model was developed by using least absolute shrinkage and selection operator (LASSO) Cox regression. Cibersort and Estimate algorithms were used to characterize tumor immune infiltrating patterns. TIDE algorithm was used to predict immunotherapy responsiveness. Results A prognostic signature based on five IRGs (MET, ERAP2, IL20RB, EREG, and SHC2) was constructed in TCGA-PDAC and comprehensively validated in ICGC and GEO cohorts. Multivariate cox regression analysis demonstrated that this signature had an independent prognostic value. The area under curve (AUC) value of the receiver operating characteristic (ROC) curve at 1-year, 3-year, and 5-year of survival were 0.724, 0.702, and 0.776 respectively. We further demonstrated that our signature has better prognostic performance than the recently published ones and is superior to traditional clinical factors such as grade and TNM stage in predicting survival. Moreover, we found higher abundance of CD8+ T cells and lower M2-like macrophages in the low-risk group of TCGA-PDAC, and predicted a higher proportion of immunotherapeutic responders in the low-risk group. Conclusions We constructed and validated an optimal prognostic model of independent prognostic value. This five-gene signature could predict immune infiltration characteristics. The signature helps to stratify PDAC patients according to the responsiveness to immunotherapy.

High tumor mutation load (TMB-H, or TMB ≥ 10) has been approved by the U.S. FDA as a biomarker for pembrolizumab treatment of solid tumors, including non‑small cell lung cancer (NSCLC). Patients with cancer who have immunotherapy-resistant gene mutations cannot achieve clinical benefits even in TMB-H. In this study, we aimed to identify gene mutations associated with immunotherapy resistance and further informed mechanisms in NSCLC. A combined cohort of 350 immune checkpoint blockade-treated patients from Memorial Sloan Kettering Cancer Center (MSKCC) was used to identify genes whose mutations could negatively influence immunotherapy efficacy. An external NSCLC cohort for which profession-free survival (PFS) data were available was used for independent validation. CIBERSORT algorithms were used to characterize tumor immune infiltrating patterns. Immunogenomic features were analysed in the TCGA NSCLC cohort. We observed that PBRM1 mutations independently and negatively influence immunotherapy efficacy. Survival analysis showed that the overall survival (OS) and PFS of patients with PBRM1 mutations (MT) were significantly shorter than the wild type (WT). Moreover, compared with PBRM1-WT/TMB-H group, OS was worse in the PBRM1-MT/TMB-H group. Notably, in patients with TMB-H/PBRM1-MT, it was equal to that in the low-TMB group. The CIBERSORT algorithm further confirmed that the immune infiltration abundance of CD8+ T cells and activated CD4+ memory T was significantly lower in the MT group. Immunogenomic differences were observed in terms of immune signatures, T-cell receptor repertoire, and immune-related genes between WT and MT groups. Nevertheless, we noticed an inverse relationship, given that MT tumors had a higher TMB than the WT group in MSKCC and TCGA cohort. In conclusion, our study revealed that NSCLC with PBRM1 mutation might be an immunologically cold phenotype and exhibited immunotherapy resistance. NSCLC with PBRM1 mutation might be misclassified as immunoresponsive based on TMB.

The mRNA vaccines are considered to be effective treatment strategies for cancers, but its progress in chronic hepatitis B virus (HBV) related-hepatocellular carcinoma (HCC) was slow. This study aimed to find potential antigens and identify suitable patients in HBV related-HCC for guiding mRNA vaccine development. We integrated the transcriptome RNA expression matrices and somatic mutation data from TCGA and ICGC datasets. A consistency matrix was constructed by using ConsensusClusterPlus to identify the immune subtypes. Graph learning based dimensional reduction was analyzed to establish immune landscape. Four upregulated and mutated antigens (EPS8L3, TCOF1, EZH2, and NOP56) were highly correlated with unfavorable clinical outcomes and antigen presenting cells (APCs). And two distinct immune phenotypes with differential clinical, cellular, and molecular characteristics were identified by in the ICGC and TCGA cohorts. IS1 is immune “hot” and immunosuppressive phenotype, with low tumor mutation burden (TMB) and high immune checkpoints (ICPs). On the contrary, IS2 is immune “cold” phenotype with high TMB and low ICPs. Monocle3 package was used to further study the intra-cluster heterogeneity, which identified cluster IS2A/2B within IS2 subtype was determined to be more suitable for mRNA vaccine. In summary, EPS8L3, TCOF1, EZH2, and NOP56 are potential antigens for mRNA vaccine development against HBV related-HCC, and patients in IS2A/2B are relatively more suitable for vaccination.

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