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Bayesian inference of negative and positive selection in human cancers


Cancer genomics efforts have identified genes and regulatory elements driving cancer development and neoplastic progression. From a microevolution standpoint, these are subject to positive selection. Although elusive in current studies, genes whose wild-type coding sequences are needed for tumor growth are also of key interest. They are expected to experience negative selection and stay intact under pressure of incessant mutation. The detection of significantly mutated (or undermutated) genes is completely confounded by the genomic heterogeneity of cancer mutation1. Here we present a hierarchical framework that allows modeling of coding point mutations. Application of the model to sequencing data from 17 cancer types demonstrates an increased power to detect known cancer driver genes and identifies new significantly mutated genes with highly plausible biological functions. The signal of negative selection is very subtle, but is detectable in several cancer types and in a pan-cancer data set. It is enriched in cell-essential genes identified in a CRISPR screen2, as well as in genes with reported roles in cancer.

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Figure 1: Observed and expected neutral distributions of mutation counts per gene.
Figure 2: Per-gene inference of selection and validation.
Figure 3: Genome-wide intensity of selection.


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

    Article  CAS  Google Scholar 

  2. Wang, T. et al. Identification and characterization of essential genes in the human genome. Science 350, 1096–1101 (2015).

    Article  CAS  Google Scholar 

  3. Polak, P. et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518, 360–364 (2015).

    Article  CAS  Google Scholar 

  4. Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).

    Article  CAS  Google Scholar 

  5. Rooney, M.S., Shukla, S.A., Wu, C.J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).

    Article  CAS  Google Scholar 

  6. Lindeboom, R.G.H., Supek, F. & Lehner, B. The rules and impact of nonsense-mediated mRNA decay in human cancers. Nat. Genet. 48, 1112–1118 (2016).

    Article  CAS  Google Scholar 

  7. Van den Eynden, J., Basu, S. & Larsson, E. Somatic mutation patterns in hemizygous genomic regions unveil purifying selection during tumor evolution. PLoS Genet. 12, e1006506 (2016).

    Article  Google Scholar 

  8. Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Preprint at bioRxiv (2017).

  9. McFarland, C.D., Korolev, K.S., Kryukov, G.V., Sunyaev, S.R. & Mirny, L.A. Impact of deleterious passenger mutations on cancer progression. Proc. Natl. Acad. Sci. USA 110, 2910–2915 (2013).

    Article  CAS  Google Scholar 

  10. Melton, C., Reuter, J.A., Spacek, D.V. & Snyder, M. Recurrent somatic mutations in regulatory regions of human cancer genomes. Nat. Genet. 47, 710–716 (2015).

    Article  CAS  Google Scholar 

  11. Davoli, T. et al. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome. Cell 155, 948–962 (2013).

    Article  CAS  Google Scholar 

  12. Scharenberg, M.A., Chiquet-Ehrismann, R. & Asparuhova, M.B. Megakaryoblastic leukemia protein-1 (MKL1): increasing evidence for an involvement in cancer progression and metastasis. Int. J. Biochem. Cell Biol. 42, 1911–1914 (2010).

    Article  CAS  Google Scholar 

  13. Sheriff, S. et al. Neuropeptide Y Y5 receptor promotes cell growth through extracellular signal–regulated kinase signaling and cyclic AMP inhibition in a human breast cancer cell line. Mol. Cancer Res. 8, 604–614 (2010).

    Article  CAS  Google Scholar 

  14. Law, M.H. et al. Genome-wide meta-analysis identifies five new susceptibility loci for cutaneous malignant melanoma. Nat. Genet. 47, 987–995 (2015).

    Article  CAS  Google Scholar 

  15. Nürnberg, A., Kitzing, T. & Grosse, R. Nucleating actin for invasion. Nat. Rev. Cancer 11, 177–187 (2011).

    Article  Google Scholar 

  16. Grabarczyk, P. et al. Inhibition of BCL11B expression leads to apoptosis of malignant but not normal mature T cells. Oncogene 26, 3797–3810 (2007).

    Article  CAS  Google Scholar 

  17. Adams, J.M. & Cory, S. The Bcl-2 apoptotic switch in cancer development and therapy. Oncogene 26, 1324–1337 (2007).

    Article  CAS  Google Scholar 

  18. Fine, B. et al. Activation of the PI3K pathway in cancer through inhibition of PTEN by exchange factor P-REX2a. Science 325, 1261–1265 (2009).

    Article  CAS  Google Scholar 

  19. Boggs, A.E. et al. α-Tubulin acetylation elevated in metastatic and basal-like breast cancer cells promotes microtentacle formation, adhesion, and invasive migration. Cancer Res. 75, 203–215 (2015).

    Article  CAS  Google Scholar 

  20. Bilbe, G. et al. Restin: a novel intermediate filament–associated protein highly expressed in the Reed–Sternberg cells of Hodgkin's disease. EMBO J. 11, 2103–2113 (1992).

    Article  CAS  Google Scholar 

  21. Park, J.H. et al. Critical roles of mucin 1 glycosylation by transactivated polypeptide N-acetylgalactosaminyltransferase 6 in mammary carcinogenesis. Cancer Res. 70, 2759–2769 (2010).

    Article  CAS  Google Scholar 

  22. Fielding, A.B., Lim, S., Montgomery, K., Dobreva, I. & Dedhar, S. A critical role of integrin-linked kinase, ch-TOG and TACC3 in centrosome clustering in cancer cells. Oncogene 30, 521–534 (2011).

    Article  CAS  Google Scholar 

  23. Xie, K., Doles, J., Hemann, M.T. & Walker, G.C. Error-prone translesion synthesis mediates acquired chemoresistance. Proc. Natl. Acad. Sci. USA 107, 20792–20797 (2010).

    Article  CAS  Google Scholar 

  24. Futreal, P.A. et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).

    Article  CAS  Google Scholar 

  25. Binamé, F. et al. Cancer-associated mutations in the protrusion-targeting region of p190RhoGAP impact tumor cell migration. J. Cell Biol. 214, 859–873 (2016).

    Article  Google Scholar 

  26. Baud, V. & Karin, M. Is NF-κB a good target for cancer therapy? Hopes and pitfalls. Nat. Rev. Drug Discov. 8, 33–40 (2009).

    Article  CAS  Google Scholar 

  27. Pasquale, E.B. Eph receptors and ephrins in cancer: bidirectional signalling and beyond. Nat. Rev. Cancer 10, 165–180 (2010).

    Article  CAS  Google Scholar 

  28. Tanaka, I. et al. LIM-domain protein AJUBA suppresses malignant mesothelioma cell proliferation via Hippo signaling cascade. Oncogene 34, 73–83 (2015).

    Article  CAS  Google Scholar 

  29. Ullah, F. et al. Promoter methylation status modulate the expression of tumor suppressor (RbL2/p130) gene in breast cancer. PLoS One 10, e0134687 (2015).

    Article  Google Scholar 

  30. Felsenstein, J. The evolutionary advantage of recombination. Genetics 78, 737–756 (1974).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Good, B.H. & Desai, M.M. Deleterious passengers in adapting populations. Genetics 198, 1183–1208 (2014).

    Article  Google Scholar 

  32. Supek, F., Miñana, B., Valcárcel, J., Gabaldón, T. & Lehner, B. Synonymous mutations frequently act as driver mutations in human cancers. Cell 156, 1324–1335 (2014).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  34. Meyer, M.J. et al. mutation3D: cancer gene prediction through atomic clustering of coding variants in the structural proteome. Hum. Mutat. 37, 447–456 (2016).

    Article  CAS  Google Scholar 

  35. 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  Google Scholar 

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We thank S. Schiffels and L. Mirny for helpful discussion during the conception of the project and feedback on the manuscript, as well as P. Polak and Z. Li for critical reading of the manuscript. We also thank V. Seplyarskiy for advice on mutational processes and I. Adzhubey for support with the setup of the web interface for CBaSE. This work was supported by US National Institutes of Health (NIH) grants U54 CA143874, R01 MH101244, and R01 GM078598 (S.S.).

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Authors and Affiliations



D.W. designed the statistical framework, wrote code, analyzed and interpreted data, created the web interface, and wrote the manuscript. S.S. supervised the project, gave technical and conceptual advice, and wrote the manuscript.

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Correspondence to Shamil Sunyaev.

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The authors declare no competing financial interests.

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Weghorn, D., Sunyaev, S. Bayesian inference of negative and positive selection in human cancers. Nat Genet 49, 1785–1788 (2017).

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