부산대학교 도서관

Keywords immunotherapy; neoantigen; immunogenomics; epitope; TESLA; immunogenicity Highlights * Diverse neoantigen predictions on shared genomic data from a global consortium * 37 out of 608 tested peptide-MHCs are bound by patient-matched T cells * Epitope presentation and recognition characteristics predict immunogenicity * Model-based interventions improve neoantigen prediction Summary Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.