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Nat Genet. 2015 Feb;47(2):106-14. doi: 10.1038/ng.3168. Epub 2014 Dec 15.

Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes.

Author information

1
1] Department of Computer Science, Brown University, Providence, Rhode Island, USA. [2] Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA.
2
1] Department of Computer Science, Brown University, Providence, Rhode Island, USA. [2] Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA. [3] Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA.
3
Department of Computer Science, Brown University, Providence, Rhode Island, USA.
4
Genome Institute, Washington University in St. Louis, St. Louis, Missouri, USA.
5
Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.
6
Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, University Pompeu Fabra, Barcelona, Spain.
7
Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA.
8
Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
9
1] Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, University Pompeu Fabra, Barcelona, Spain. [2] Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
10
1] Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. [2] Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA.
11
1] Genome Institute, Washington University in St. Louis, St. Louis, Missouri, USA. [2] Department of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA. [3] Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri, USA.

Abstract

Cancers exhibit extensive mutational heterogeneity, and the resulting long-tail phenomenon complicates the discovery of genes and pathways that are significantly mutated in cancer. We perform a pan-cancer analysis of mutated networks in 3,281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a new algorithm to find mutated subnetworks that overcomes the limitations of existing single-gene, pathway and network approaches. We identify 16 significantly mutated subnetworks that comprise well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer, including cohesin, condensin and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, pan-cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types.

PMID:
25501392
PMCID:
PMC4444046
DOI:
10.1038/ng.3168
[Indexed for MEDLINE]
Free PMC Article
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