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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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

    • Fabio Vandin

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

    • Mark D M Leiserson
    •  & Fabio Vandin

    These authors contributed equally to this work.

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. Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, 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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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.

Competing interests

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

Corresponding author

Correspondence to Benjamin J Raphael.

Supplementary information

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    Supplementary Text and Figures

    Supplementary Note and Supplementary Figures 1–30.

Excel files

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    Supplementary Tables 1–23 and 25–39

    Supplementary Tables 1–23 and 25–39.

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    Supplementary Table 24

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

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DOI

https://doi.org/10.1038/ng.3168

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