title: Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade.
abstract: The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia.at/). Cellular characterization of the immune infiltrates showed that tumor genotypes determine immunophenotypes and tumor escape mechanisms. Using machine learning, we identified determinants of tumor immunogenicity and developed a scoring scheme for the quantification termed immunophenoscore. The immunophenoscore was a superior predictor of response to anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and anti-programmed cell death protein 1 (anti-PD-1) antibodies in two independent validation cohorts. Our findings and this resource may help inform cancer immunotherapy and facilitate the development of precision immuno-oncology.
authors.affiliation: Biocenter, Division of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
authors.forename: Pornpimol
authors.initials: P
authors.lastname: Charoentong
authors.affiliation: Biocenter, Division of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
authors.forename: Francesca
authors.initials: F
authors.lastname: Finotello
authors.affiliation: Biocenter, Division of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
authors.forename: Mihaela
authors.initials: M
authors.lastname: Angelova
authors.affiliation: Biocenter, Division of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
authors.forename: Clemens
authors.initials: C
authors.lastname: Mayer
authors.affiliation: Biocenter, Division of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
authors.forename: Mirjana
authors.initials: M
authors.lastname: Efremova
authors.affiliation: Biocenter, Division of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
authors.forename: Dietmar
authors.initials: D
authors.lastname: Rieder
authors.affiliation: Biocenter, Division of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria.
authors.forename: Hubert
authors.initials: H
authors.lastname: Hackl
authors.affiliation: Biocenter, Division of Bioinformatics, Medical University of Innsbruck, 6020 Innsbruck, Austria. Electronic address: zlatko.trajanoski@i-med.ac.at.
authors.forename: Zlatko
authors.initials: Z
authors.lastname: Trajanoski
mesh_terms: D000951:Antigens, Neoplasm / Q000276:immunology; D015496:CD4-Positive T-Lymphocytes / Q000276:immunology; D018414:CD8-Positive T-Lymphocytes / Q000276:immunology; D060908:CTLA-4 Antigen / Q000378:metabolism; D059447:Cell Cycle Checkpoints; D023281:Genomics / Q000379:methods*; D005838:Genotype; D006801:Humans; D016130:Immunophenotyping; D007167:Immunotherapy; D000069550:Machine Learning; D009154:Mutation / Q000235:genetics; D009369:Neoplasms / Q000235:genetics* / Q000276:immunology*; D011379:Prognosis; D061026:Programmed Cell Death 1 Receptor / Q000378:metabolism
chemical_list: D000951:Antigens, Neoplasm; D060908:CTLA-4 Antigen; D061026:Programmed Cell Death 1 Receptor
keywords: cancer-germline antigens; neoantigens; predictive marker; tumor-infiltrating lymphocytes