A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types.

ABSTRACT: Tumor neoepitopes presented by major histocompatibility complex (MHC) class I are recognized by tumor-infiltrating lymphocytes (TIL) and are targeted by adoptive T-cell therapies. Identifying which mutant neoepitopes from tumor cells are capable of recognition by T cells can assist in the development of tumor-specific, cell-based therapies and can shed light on antitumor responses. Here, we generate a ranking algorithm for class I candidate neoepitopes by using next-generation sequencing data and a dataset of 185 neoepitopes that are recognized by HLA class I-restricted TIL from individuals with metastatic cancer. Random forest model analysis showed that the inclusion of multiple factors impacting epitope presentation and recognition increased output sensitivity and specificity compared to

SUBMITTER: Gartner JJ 

PROVIDER: S-EPMC8680775 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature