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Nat Commun. 2017 Oct 19;8(1):1050. doi: 10.1038/s41467-017-01018-0.

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

Author information

1
Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX, 77054, USA.
2
Department of Internal Medicine, Seoul National University Hospital, Seoul, 03080, Korea.
3
Department of Hematology-Oncology, Chonnam National University Medical School, Gwangju, 61469, Korea.
4
Department of Internal Medicine, School of Medicine, Kyung Hee University, Seoul, 02447, Korea.
5
Department of Molecular Oncology, The Graduate School of Medicine, Seoul National University, Seoul, 08826, Korea.
6
Department of Gastrointestinal Medical Oncology, UT MD Anderson Cancer Center, Houston, TX, 77054, USA.
7
Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, 13620, Korea.
8
Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX, 77054, USA. jlee@mdanderson.org.

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.

PMID:
29051489
PMCID:
PMC5648801
DOI:
10.1038/s41467-017-01018-0
[Indexed for MEDLINE]
Free PMC Article

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