Immune-based mutation classification enables neoantigen prioritization and immune feature discovery in cancer immunotherapy.

ABSTRACT: Genetic mutations lead to the production of mutated proteins from which peptides are presented to T cells as cancer neoantigens. Evidence suggests that T cells that target neoantigens are the main mediators of effective cancer immunotherapies. Although algorithms have been used to predict neoantigens, only a minority are immunogenic. The factors that influence neoantigen immunogenicity are not completely understood. Here, we classified human neoantigen/neopeptide data into three categories based on their TCR-pMHC binding events. We observed a conservative mutant orientation of the anchor residue from immunogenic neoantigens which we termed the "NP" rule. By integrating this rule with an existing prediction algorithm, we found improved performance in neoantigen prioritization. To better und

SUBMITTER: Bai P 

PROVIDER: S-EPMC7833777 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature