Genomic landscape associated with potential response to anti-CTLA-4 treatment in cancers

Chan-Young Ock, Jun-Eul Hwang, Bhumsuk Keam, Sang-Bae Kim, Jae-Jun Shim, Hee-Jin Jang, Sarang Park, Bo Hwa Sohn, Minse Cha, Jaffer A. Ajani, Scott Kopetz, Keun-Wook Lee, Tae Min Kim, Dae Seog Heo and Ju-Seog Lee ()
Additional contact information
Chan-Young Ock: UT MD Anderson Cancer Center
Jun-Eul Hwang: UT MD Anderson Cancer Center
Bhumsuk Keam: Seoul National University Hospital
Sang-Bae Kim: UT MD Anderson Cancer Center
Jae-Jun Shim: UT MD Anderson Cancer Center
Hee-Jin Jang: UT MD Anderson Cancer Center
Sarang Park: UT MD Anderson Cancer Center
Bo Hwa Sohn: UT MD Anderson Cancer Center
Minse Cha: UT MD Anderson Cancer Center
Jaffer A. Ajani: UT MD Anderson Cancer Center
Scott Kopetz: UT MD Anderson Cancer Center
Keun-Wook Lee: Seoul National University Bundang Hospital
Tae Min Kim: Seoul National University Hospital
Dae Seog Heo: Seoul National University Hospital
Ju-Seog Lee: UT MD Anderson Cancer Center

Nature Communications, 2017, vol. 8, issue 1, 1-13

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-01018-0

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