TY - JOUR
T1 - Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes
AU - Leiserson, Mark D.M.
AU - Vandin, Fabio
AU - Wu, Hsin Ta
AU - Dobson, Jason R.
AU - Eldridge, Jonathan V.
AU - Thomas, Jacob L.
AU - Papoutsaki, Alexandra
AU - Kim, Younhun
AU - Niu, Beifang
AU - McLellan, Michael
AU - Lawrence, Michael S.
AU - Gonzalez-Perez, Abel
AU - Tamborero, David
AU - Cheng, Yuwei
AU - Ryslik, Gregory A.
AU - Lopez-Bigas, Nuria
AU - Getz, Gad
AU - Ding, Li
AU - Raphael, Benjamin J.
N1 - Funding Information:
The authors thank F. Roth for his assistance in constructing the HINT+HI2012 interaction network. We gratefully acknowledge the contributions of the TCGA Research Network and its TCGA Pan-Cancer Analysis Working Group. This work is supported by US National Science Foundation (NSF) grant IIS-1016648 and US National Institutes of Health (NIH) grants R01HG005690, R01HG007069 and R01CA180776 to B.J.R. and by National Human Genome Research Institute (NHGRI) grant U01HG006517 to L.D. B.J.R. is supported by a Career Award at the Scientific Interface from the Burroughs Wellcome Fund, an Alfred P. Sloan Research Fellowship and an NSF CAREER Award (CCF-1053753). M.D.M.L. is supported by NSF fellowship GRFP DGE 0228243. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Data for HI2012, created by the Center for Cancer Systems Biology (CCSB) at the Dana-Farber Cancer Institute, are supported by the NHGRI of the US NIH, the Ellison Foundation and the Dana-Farber Cancer Institute Strategic Initiative.
Publisher Copyright:
© 2015 Nature America, Inc. All rights reserved.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
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U2 - 10.1038/ng.3168
DO - 10.1038/ng.3168
M3 - Article
C2 - 25501392
AN - SCOPUS:84926157747
SN - 1061-4036
VL - 47
SP - 106
EP - 114
JO - Nature Genetics
JF - Nature Genetics
IS - 2
ER -