Description

Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade

P. Charoentong^†, F. Finotello^†, M. Angelova^†, C. Mayer, E. Tappeiner, M. Efremova,
D. Rieder, H. Hackl, Z. Trajanoski^*

Biocenter, Division of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria

^* Corresponding author

^† Equal contribution

Immunotherapies have been shown to have great clinical impact including adoptive T-cell transfer therapy, cellular vaccines, and checkpoint blockade inhibitors, such as the anti-CTLA4 monoclonal antibody and the antibodies that block signaling through PD-1 and PD-L1. As only a minority of the patients benefit from these treatments, it is crucial to identify those patients that are likely to respond to therapy and to develop strategies to treat non-responders. To address these challenging tasks and facilitate understanding of the tumor-immune cell interactions we developed TIminer, a computational pipeline for immuno-genomic analyses, and inferred the cellular composition and functional orientation of immune infiltrates, and characterized tumor antigens in 20 solid cancers from TCGA. The analyses results were then deposited in a web accessible database: TCIA - The Cancer Immunome Atlas - (https://tcia.at). Computational segregation of immune infiltrates revealed a role of cancer-germline antigens in spontaneous immunity and showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. The genotype-phenotype relationships were evident at high-level view (mutational load, tumor heterogeneity) and at low-level view (mutational origin) of the genomic landscapes. Using a machine-learning approach we identified determinants of immunogenicity and developed a scoring scheme named “immunophenoscore” which is based on the infiltration of immune subsets and expression of immunomodulatory molecules. The immunophenoscore predicted response to immunotherapy with anti-CTLA-4 and anti-PD-1 antibodies in two independent validation cohorts. Our findings and the developed resources may help informing cancer immunotherapy and facilitate the development of precision immuno-oncology.