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Nat Biotechnol. 2016 May;34(5):539-46. doi: 10.1038/nbt.3527. Epub 2016 Apr 18.

Characterizing genomic alterations in cancer by complementary functional associations.

Author information

1
Eli and Edythe Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
2
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
3
Bioinformatics and Systems Biology Program, University of California at San Diego, La Jolla, California, USA.
4
Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, California, USA.
5
Stem Cell Program and Institute for Genomic Medicine, University of California at San Diego, La Jolla, California, USA.
6
Harvard Medical School, Boston, Massachusetts, USA.
7
Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio, USA.
8
Department of Medicine, Dana Farber Cancer Institute, Boston, Massachusetts, USA.
9
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Alberta, Canada.
10
Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
11
Boston Children's Hospital, Boston, Massachusetts, USA.
12
Bioinformatics Graduate Program, Boston University, Boston, Massachusetts, USA.
13
Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
14
Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
15
Program in Biophysics, Harvard University, Boston, Massachusetts, USA.
16
Department of Biology, University of Padova, Padova, Italy.
17
Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
18
Department of Medicine, University of California San Diego, La Jolla, California, USA.
19
Moores Cancer Center, University of California San Diego, La Jolla, California, USA.

Abstract

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes.

PMID:
27088724
PMCID:
PMC4868596
DOI:
10.1038/nbt.3527
[Indexed for MEDLINE]
Free PMC Article

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