Genomic landscape associated with potential response to anti-CTLA-4 treatment in cancers
 Authors and Affiliations
 Authors and Affiliations
Chan-Young Ock1,2, Jun-Eul Hwang1,3, Bhumsuk Keam2, Sang-Bae Kim1, Jae-Jun Shim1,4, Hee-Jin Jang1,5, Sarang Park1, Bo Hwa Sohn1, Minse Cha1, Jaffer A. Ajani6, Scott Kopetz6, Keun-Wook Lee7, Tae Min Kim2, Dae Seog Heo2 & Ju-Seog Lee1,*
1Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77054, USA. 2Department of Internal Medicine, Seoul National University Hospital, Seoul 03080, Korea. 3Department of Hematology-Oncology, Chonnam National University Medical School, Gwangju 61469, Korea. 4Department of Internal Medicine, School of Medicine, Kyung Hee University, Seoul 02447, Korea. 5Department of Molecular Oncology, The Graduate School of Medicine, Seoul National University, Seoul 08826, Korea. 6Department of Gastrointestinal Medical Oncology, UT MD Anderson Cancer Center, Houston, TX 77054, USA. 7Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Korea
*Correspondence to Ju-Seog Lee.
Abstract Immunotherapy has emerged as a promising anti-cancer treatment, however, little is known about the genetic characteristics that dictate response to immunotherapy. We develop a transcriptional predictor of immunotherapy response and assess its prediction in genomic data from ~10,000 human tissues across 30 different cancer types to estimate the potential response to immunotherapy. The integrative analysis reveals two distinct tumor types: the mutator type is positively associated with potential response to immunotherapy, whereas the chromosome-instable type is negatively associated with it. We identify somatic mutations and copy number alterations significantly associated with potential response to immunotherapy, in particular treatment with anti-CTLA-4 antibody. Our findings suggest that tumors may evolve through two different paths that would lead to marked differences in immunotherapy response as well as different strategies for evading immune surveillance. Our analysis provides resources to facilitate the discovery of predictive biomarkers for immunotherapy that could be tested in clinical trials.
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