A novel immunogenomic signature to predict prognosis and reveal immune infiltration characteristics in pancreatic ductal adenocarcinoma

Abstract

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.

Perspectives for immunotherapy of EBV‐associated GLELC: A relatively “hot” tumor microenvironment

“…47 Abundant infiltration of CD3 + T cells and CD8 + T cells not only helps form a hot TME but also contributes to predicting ICB therapeutic efficacy. [48][49][50] Research also shows the crucial role of CD4+ T cells in the antitumor immune response, and their interaction with dendritic cells (DCs) is significant for stimulating cytotoxic T lymphocyte function. 51 Moreover, a recent study revealed that CD4 + T cells might reprogram the TME to a hotter TME by facilitating the normalization of tumor vessels.…”

“…Effective antitumor immunity is mainly dependent on the orchestration of powerful T‐cell responses, and generally, CD3 and CD8 are identified as essential effectors of efficacy and representative of hot tumors 47 . Abundant infiltration of CD3 + T cells and CD8 + T cells not only helps form a hot TME but also contributes to predicting ICB therapeutic efficacy 48–50 . Research also shows the crucial role of CD4+ T cells in the antitumor immune response, and their interaction with dendritic cells (DCs) is significant for stimulating cytotoxic T lymphocyte function 51 .…”

Keratin 17 modulates the immune topography of pancreatic cancer

“…Cytotoxic T cells target tumor cells that expose tumor-specific antigens in various malignancies, including pancreatic ductal adenocarcinoma [7,29,44] and higher CD8+ T-cell density in tumor is generally associated with prolonged pancreatic cancer survival [10,26,55,63]. Conversely, K17 has been associated with immune cell response in psoriasis as well as in basal cell skin cancer and in cervical carcinoma and is a negative prognostic biomarker in PDAC, suggesting that K17 might have some role in CD8+ T cell exclusion [60,66].…”