Esophageal cancer (EC) is the common malignant tumor with poor survival. The long-term surival rate of patients with advanced EC stages has not been improved with multidisciplinary treatments including surgery and chemotherapy and radiation. Recently, immunotherapy approaches using checkpoint inhibitors (CPI), cancer vaccine, and adoptive T cell therapy have improved survival outcomes of EC patients. The clinical outcomes are associated with expression levels as well as the immunogenicity of neoantigens which arise from soma mutations. Therefore, the identification of immunogenic neoantigens is essential for achieving effective therapies. Recent data published by the Tumor Neoantigen Selection Alliance (TESLA) show that the majority (98%) of predicted neoantigens are lack of immunogenicity and ineffective in activating antitumor immune responses. In our study, we aim to develop a pipeline with both computational prediction tools and experimental validation assays to enhance the accuracy of neoantigen identification.
This study is to develop computational pipelines and experimental validation assays for improving the identification of neoantigens from patients with esophageal cancer.