Structure-based prediction of neoantigens paired with T cell receptors on pre-existing CD8+ tumor-infiltrating lymphocytes.

Abstract

e14583 Background: Tumor neoantigens have ability to expand tumor-specific T cell immunity, which could be boosted by anti-PD-1 immunotherapy. Therapeutically relevant neoantigens should be presented on patient HLAs and recognized by tumor-reactive T cells. Despite tumor-infiltrating lymphocytes (TILs) have enriched tumor-reactive T cell populations, most tumor-specific TILs exhibit exhausted phenotype and have limitations to in vitro screening. Here, we developed in silico structure-based prediction of neoantigen discovery pipeline by pairing with T cell receptors (TCRs) on pre-existing CD8+ exhausted tumor-infiltrating lymphocytes. Methods: Tumor resections from patients with liver, stomach, colorectal, and lung cancers are subject to whole exome and transcriptome. In addition, TILs from the patients are subject to scRNA/TCRseq to select TCRs of exhausted T cells, which are mostly thought to have tumor-reactivity. Neoantigen epitopes are prioritized from our in silico pipeline including HLA binding and TCR-peptide-HLA structure-based pairing score. To evaluate the immunogenicity of selected neoantigens, the neoantigens are tested for in vitro IFNg ELISPOT assay using peripheral blood mononuclear cells (PBMCs) from the same patients, which sensitively detects antigen-experienced T cells. Results: For 5 patients with T cell-reactive neoantigens, among 23 neoantigens selected by our in silico pipeline and tested in vitro, 10 neoantigens showed positive pre-existing T cell responses by matched patients. Then, we analyzed gene expression profiles of TILs paired in silico with in vitro T cell response-positive or negative neoantigens (pTILs and nTILs, respectively) combined with TILs from 23 solid cancer patients. Interestingly, pTILs were mostly mapped to the clusters which highly expressed markers of exhaustion and cytotoxicity, while nTILs showed random distribution across all the clusters. Conclusions: Neoantigen prediction using structure-based pairing of neoantigens and phenotype-selected TILs showed promising potential to better select therapeutically-relevant cancer vaccines. This data supports further investigation of our in silico pipeline into more patient samples and preclinical studies in mouse tumor models.