Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes.

ABSTRACT: Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity using approaches modeled from tumor-specific antigen data. Here, we describe peptide-intrinsic biochemical features associated with neoantigen and minor histocompatibility mismatch antigen immunogenicity and present a gradient boosting algorithm for predicting tumor antigen immunogenicity. This algorithm was validated in two murine tumor models and demonstrated the capacity to select for therapeutically active antigens. Immune correlates of neoantigen immunogenicity were studied in a pan-cancer data set from The Cancer Genome Atlas and demonstrate

SUBMITTER: Smith CC 

PROVIDER: S-EPMC6774822 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature