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

Evidences have suggested that T cells that target mutation derived neoantigens are the main mediators of many effective cancer immunotherapies. Although algorithms have been used to predict neoantigens, only a handful of those are truly immunogenic. It is unclear which other factors influence neoantigen immunogenicity. Here, we classified clinical human neoantigen/neopeptide data based on their peptide-MHC binding events into three categories. We observed a conserved mutation orientation in anchor mutated neoantigen cohort after classification. By integrating this rule with existing prediction algorithm, we achieved improved performance of neoantigen prioritization. We solved several neoantigen/MHC structures, which showed that neoantigens which follow this rule can not only increase peptide-MHC binding affinity but create new TCR binding features. We also found neoantigen exposed surface area may lead to TCR bias in cancer immunotherapy. These evidences highlighted the value of immune-based classification during neoantigen study and enabled improved efficiency for cancer treatment.