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

Nature Genetics volume 47, pages 106–114 (2015)Cite this article

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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.

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Figure 1: HotNet2 pan-cancer analysis.
Figure 2: Overview of HotNet2 pan-cancer results.
Figure 3: HotNet2 pan-cancer subnetworks overlapping SWI/SNF and BAP1 complexes.
Figure 4: HotNet2 pan-cancer subnetworks overlapping the cohesin and condensin complexes.

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Acknowledgements

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.

Author information

Author notes

  1. Fabio Vandin

    Present address: Present address: Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.,

  2. Mark D M Leiserson and Fabio Vandin: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science, Brown University, Providence, Rhode Island, USA

    Mark D M Leiserson, Fabio Vandin, Hsin-Ta Wu, Jason R Dobson, Jonathan V Eldridge, Jacob L Thomas, Alexandra Papoutsaki, Younhun Kim & Benjamin J Raphael

  2. Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA

    Mark D M Leiserson, Fabio Vandin, Hsin-Ta Wu, Jason R Dobson & Benjamin J Raphael

  3. Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA

    Jason R Dobson

  4. Genome Institute, Washington University in St. Louis, St. Louis, Missouri, USA

    Beifang Niu, Michael McLellan & Li Ding

  5. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Michael S Lawrence & Gad Getz

  6. Department of Experimental and Health Sciences, Research Unit on Biomedical Informatics, University Pompeu Fabra, Barcelona, Spain

    Abel Gonzalez-Perez, David Tamborero & Nuria Lopez-Bigas

  7. Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA

    Yuwei Cheng

  8. Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA

    Gregory A Ryslik

  9. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain

    Nuria Lopez-Bigas

  10. Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA

    Gad Getz

  11. Department of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA

    Li Ding

  12. Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri, USA

    Li Ding

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  1. Mark D M Leiserson
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Contributions

M.D.M.L., F.V., H.-T.W. and B.J.R. designed the HotNet2 algorithm. M.D.M.L., F.V., H.-T.W., J.R.D., J.V.E., J.L.T., Y.K. and B.J.R. performed pan-cancer network analysis, analyzed results and benchmarked algorithms. A.P., J.R.D., Y.C. and G.A.R. analyzed mutation clusters in genes. B.N., M.M. and L.D. provided MuSiC gene scores, assisted with figures and generated mutation validation data. M.S.L., G.G., A.G.-P., D.T. and N.L.-B. provided MutSigCV and Oncodrive gene scores. M.D.M.L., F.V., H.-T.W., J.R.D. and B.J.R. wrote the manuscript with input from all authors. B.J.R. conceived and supervised the project.

Corresponding author

Correspondence to Benjamin J Raphael.

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A patent application related to this work has been filed with the US Patent and Trademark Office (USPTO).

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Supplementary Text and Figures

Supplementary Note and Supplementary Figures 1–30. (PDF 14283 kb)

Supplementary Tables 1–23 and 25–39

Supplementary Tables 1–23 and 25–39. (XLSX 219 kb)

Supplementary Table 24

Mutually exclusive and co-occurring test for pairwise genes within the pair of HotNet2 identified subnetworks across all pan-cancer samples. (XLSX 364 kb)

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Leiserson, M., Vandin, F., Wu, HT. et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat Genet 47, 106–114 (2015). https://doi.org/10.1038/ng.3168

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