Computational Prediction and Experimental Validation of Gastric Cancer Associated Neoantigens

Gastric cancer (GC) is the fourth most common cancer type and one of the leading causes of cancer-related death in Vietnam. Immunotherapy using checkpoint inhibitors (CPI) in combination with certain types of chemotherapy has been clinically shown to offer survival benefits for patients diagnosed with advanced stomach cancer. However, clinical outcomes of CPI are associated with the quantity as well as the quality of neoantigens which arise due to mutations in coding regions of cancer associated genes. Such neoantigens can be presentable by cancer cells to the host adaptive immune system and activate antitumor responses. Hence, the identification of neoantigens would be of significance for immunotherapeutic approaches. Recent data published by the Tumor Neoantigen Selection Alliance (TESLA) indicate that a large proportion (98%) of predicted neoantigens are not immunogenic and ineffective in activating anti-tumor responses. In the present study, we aim to develop a comprehensive pipeline incorporating both computational prediction tools and experimental validation assays to enhance the accuracy of neoantigen identification.