A comprehensive analysis of genomic determinants of response to immune checkpoint inhibitor-based immunotherapy.

Abstract A low response rate is the major challenge for existing cancer immunotherapies. A number of biomarkers have been reported to be associated with the likelihood of patient responses to immune checkpoint inhibitors (ICI), including the expression of PD-L1, tumor mutational burden (TMB), tumor infiltration lymphocytes, and T cell repertoire. However, these biomarkers showed variable performance even conflicting conclusions in different cohorts. And a large proportion of patients exhibited an immune-exclusion or immune-desert phenotype that cannot be explained by current biomarkers. In addition, establishing a cohort with adequate sample size requires multiple years of multi-center efforts. Therefore, it is still a challenge and a lack for the evaluation of efficacy of known biomarkers and the discovery of new signature(s) in a large-scale study. We hypothesized that a data-driven meta-analysis approach integrating multi-cohorts with multi-omics data will help to characterize the predictive genomic features and discover the mechanisms of antitumor immune responses. In this work, we collected 3,037 ICI therapy samples with genetic and transcriptomics data for 14 cancer types. Then a data-driven strategy was designed to explore each type of genomic feature which included gene mutations, pathway mutations, TMB, mutational signatures, gene expression, pathway expression, interaction among checkpoint blockade, and immune cell components. Then a comprehensive analysis, which contained a meta-analysis for the identification of consistent biomarkers across multiple cohorts and a data aggregation-analysis for the identification of rare biomarkers on aggregated data, was performed to explore the associations between ICI response and omics features. Furthermore, we validated these signatures as biomarkers in three independent cohorts. Finally, we identified five types of biomarkers, including 1) two biomarkers regarding source of antigen (TMB and mutations of MYO7B), 2) one feature of responding to antigen (cytotoxic T lymphocytes pathway), 3) three biomarkers indicating macrophages M1 infiltrations (macrophages M1 component, and two marker genes of macrophages M1, CXCL11 and CCL19), 4) one biomarker regarding immune interactions (expression relations among immune checkpoint genes), and 5) two biomarkers about T cell-inflamed tumor microenvironment (IFNG and ligands genes of chemokine receptor CXCR3). In addition, a predictive module was built based on these signatures and outperformed than TMB or PD-L1 expression which were approved molecular biomarkers by FDA. We found high TMB patients had more likelihood to active the cytotoxic T lymphocytes which secret IFNG to kill tumor cell and promote the recruitment of macrophage M1. And the T cells were stimulated by macrophage M1 via the binding of CXCR3 of T cell surface and receptor of CXCR3 of macrophage M1 surface. Citation Format: Jing Yang, Qi Liu, Yu Shyr. A comprehensive analysis of genomic determinants of response to immune checkpoint inhibitor-based immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5018.