Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

Subjects

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

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.

References

  1. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

  2. Cancer Genome Atlas Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

  3. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 99, 43–49 (2013).

  4. Cancer Genome Atlas Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).

  5. Cancer Genome Atlas Research Network. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

  6. Kandoth, C. et al. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. Cancer Genome Atlas Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

  8. Stratton, M.R., Campbell, P.J. & Futreal, P.A. The cancer genome. Nature 458, 719–724 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Garraway, L.A. & Lander, E.S. Lessons from the cancer genome. Cell 153, 17–37 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Lawrence, M.S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kandoth, C. et al. Mutational landscape and significance across 12 major cancer types. Nature 502, 333–339 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zack, T.I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Weinstein, J.N. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hanahan, D. & Weinberg, R.a. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  16. Vandin, F., Upfal, E. & Raphael, B.J. Algorithms for detecting significantly mutated pathways in cancer. J. Comput. Biol. 18, 507–522 (2011).

    Article  CAS  PubMed  Google Scholar 

  17. Vandin, F., Clay, P., Upfal, E. & Raphael, B.J. Discovery of mutated subnetworks associated with clinical data in cancer. Pac. Symp. Biocomput. 2012, 55–66 (2012).

    Google Scholar 

  18. Grasso, C.S. et al. The mutational landscape of lethal castration-resistant prostate cancer. Nature 487, 239–243 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hofree, M., Shen, J.P., Carter, H., Gross, A. & Ideker, T. Network-based stratification of tumor mutations. Nat. Methods 10, 1108–1115 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lawrence, M.S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Das, J. & Yu, H. HINT: high-quality protein interactomes and their applications in understanding human disease. BMC Syst. Biol. 6, 92 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Yu, H. et al. Next-generation sequencing to generate interactome datasets. Nat. Methods 8, 478–480 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Khurana, E., Fu, Y., Chen, J. & Gerstein, M. Interpretation of genomic variants using a unified biological network approach. PLOS Comput. Biol. 9, e1002886 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Razick, S., Magklaras, G. & Donaldson, I.M. iRefIndex: a consolidated protein interaction database with provenance. BMC Bioinformatics 9, 405 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hoadley, K.A. et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158, 929–944 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gonzalez-Perez, A. & Lopez-Bigas, N. Functional impact bias reveals cancer drivers. Nucleic Acids Res. 40, e169 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tamborero, D., Lopez-Bigas, N. & Gonzalez-Perez, A. Oncodrive-CIS: a method to reveal likely driver genes based on the impact of their copy number changes on expression. PLoS ONE 8, e55489 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Dees, N.D. et al. MuSiC: identifying mutational significance in cancer genomes. Genome Res. 22, 1589–1598 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Mermel, C.H. et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 12, R41 (2011).

    PubMed  PubMed Central  Google Scholar 

  30. Ye, J., Pavlicek, A., Lunney, E.A., Rejto, P.A. & Teng, C.-H. Statistical method on nonrandom clustering with application to somatic mutations in cancer. BMC Bioinformatics 11, 11 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ryslik, G.A., Cheng, Y., Cheung, K.-H., Modis, Y. & Zhao, H. Utilizing protein structure to identify non-random somatic mutations. BMC Bioinformatics 14, 190 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Yeang, C.-H., McCormick, F. & Levine, A. Combinatorial patterns of somatic gene mutations in cancer. FASEB J. 22, 2605–2622 (2008).

    Article  CAS  PubMed  Google Scholar 

  33. Vandin, F., Upfal, E. & Raphael, B.J. De novo discovery of mutated driver pathways in cancer. Genome Res. 22, 375–385 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Solis, L.M. et al. Nrf2 and Keap1 abnormalities in non–small cell lung carcinoma and association with clinicopathologic features. Clin. Cancer Res. 16, 3743–3753 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yamadori, T. et al. Molecular mechanisms for the regulation of Nrf2-mediated cell proliferation in non-small-cell lung cancers. Oncogene 31, 4768–4777 (2012).

    Article  CAS  PubMed  Google Scholar 

  36. Thompson, B.A., Tremblay, V., Lin, G. & Bochar, D.A. CHD8 is an ATP-dependent chromatin remodeling factor that regulates β-catenin target genes. Mol. Cell. Biol. 28, 3894–3904 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Greife, A. et al. Canonical Notch signalling is inactive in urothelial carcinoma. BMC Cancer 14, 628 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wilson, B.G. & Roberts, C.W.M. SWI/SNF nucleosome remodellers and cancer. Nat. Rev. Cancer 11, 481–492 (2011).

    Article  CAS  PubMed  Google Scholar 

  39. Varela, I. et al. Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 469, 539–542 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kadoch, C. et al. Proteomic and bioinformatic analysis of mammalian SWI/SNF complexes identifies extensive roles in human malignancy. Nat. Genet. 45, 592–601 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Sausen, M. et al. Integrated genomic analyses identify ARID1A and ARID1B alterations in the childhood cancer neuroblastoma. Nat. Genet. 45, 12–17 (2013).

    Article  CAS  PubMed  Google Scholar 

  42. Tsurusaki, Y. et al. Mutations affecting components of the SWI/SNF complex cause Coffin-Siris syndrome. Nat. Genet. 44, 376–378 (2012).

    Article  CAS  PubMed  Google Scholar 

  43. Mandel, S. & Gozes, I. Activity-dependent neuroprotective protein constitutes a novel element in the SWI/SNF chromatin remodeling complex. J. Biol. Chem. 282, 34448–34456 (2007).

    Article  CAS  PubMed  Google Scholar 

  44. Steingart, R.A. & Gozes, I. Recombinant activity-dependent neuroprotective protein protects cells against oxidative stress. Mol. Cell. Endocrinol. 252, 148–153 (2006).

    Article  CAS  PubMed  Google Scholar 

  45. Carbone, M. et al. BAP1 and cancer. Nat. Rev. Cancer 13, 153–159 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Peña-Llopis, S. et al. BAP1 loss defines a new class of renal cell carcinoma. Nat. Genet. 44, 751–759 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Fang, R. et al. Human LSD2/KDM1b/AOF1 regulates gene transcription by modulating intragenic H3K4me2 methylation. Mol. Cell 39, 222–233 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Shi, Y. et al. Histone demethylation mediated by the nuclear amine oxidase homolog LSD1. Cell 119, 941–953 (2004).

    Article  CAS  PubMed  Google Scholar 

  49. Xu, H., Tomaszewski, J.M. & McKay, M.J. Can corruption of chromosome cohesion create a conduit to cancer? Nat. Rev. Cancer 11, 199–210 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. Rubio, E.D. et al. CTCF physically links cohesin to chromatin. Proc. Natl. Acad. Sci. USA 105, 8309–8314 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Schmidt, D. et al. A CTCF-independent role for cohesin in tissue-specific transcription. Genome Res. 20, 578–588 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kon, A. et al. Recurrent mutations in multiple components of the cohesin complex in myeloid neoplasms. Nat. Genet. 45, 1232–1237 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Solomon, D.A. et al. Frequent truncating mutations of STAG2 in bladder cancer. Nat. Genet. 45, 1428–1430 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Wood, A.J., Severson, A.F. & Meyer, B.J. Condensin and cohesin complexity: the expanding repertoire of functions. Nat. Rev. Genet. 11, 391–404 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Hirano, T. Condensins: universal organizers of chromosomes with diverse functions. Genes Dev. 26, 1659–1678 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Lapointe, J. et al. hCAP-D3 expression marks a prostate cancer subtype with favorable clinical behavior and androgen signaling signature. Am. J. Surg. Pathol. 32, 205–209 (2008).

    Article  PubMed  Google Scholar 

  57. Ciriello, G. et al. Emerging landscape of oncogenic signatures across human cancers. Nat. Genet. 45, 1127–1133 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Mitra, K., Carvunis, A.-R., Ramesh, S.K. & Ideker, T. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719–732 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Chung, F. The heat kernel as the pagerank of a graph. Proc. Natl. Acad. Sci. USA 104, 19735–19740 (2007).

    Article  PubMed Central  Google Scholar 

  60. Berkhin, P. Bookmark-Coloring algorithm for personalized PageRank computing. Internet Math. 3, 41–62 (2006).

    Article  Google Scholar 

  61. Huang, W., Sherman, B.T. & Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources. Nat. Protoc. 4, 44–57 (2009).

    Article  CAS  Google Scholar 

  62. Huang, W., Sherman, B.T. & Lempicki, R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).

    Article  CAS  Google Scholar 

  63. Mootha, V.K. et al. PGC-1α–responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    Article  CAS  PubMed  Google Scholar 

  64. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Ciriello, G., Cerami, E.G., Sander, C. & Schultz, N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 22, 398–406 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Shay, J.W., Zou, Y., Hiyama, E. & Wright, W.E. Telomerase and cancer. Hum. Mol. Genet. 10, 677–685 (2001).

    Article  CAS  PubMed  Google Scholar 

Download references

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

Authors
  1. Mark D M Leiserson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fabio Vandin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hsin-Ta Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jason R Dobson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jonathan V Eldridge
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jacob L Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alexandra Papoutsaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Younhun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Beifang Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Michael McLellan
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Michael S Lawrence
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Abel Gonzalez-Perez
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. David Tamborero
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Yuwei Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Gregory A Ryslik
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Nuria Lopez-Bigas
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Gad Getz
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Li Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Benjamin J Raphael
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

Competing interests

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

Supplementary information

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)

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer