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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.