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Discovery and saturation analysis of cancer genes across 21 tumour types



Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2–20%). To explore the feasibility of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exome sequences from 4,742 human cancers and their matched normal-tissue samples across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types. Our analysis also identified 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600–5,000 samples per tumour type, depending on background mutation frequency. The results may help to guide the next stage of cancer genomics.

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Figure 1: Mutation patterns for one known and two novel cancer genes.
Figure 2: Cancer genes in selected tumour types.
Figure 3: Cancer genes identified from a data set of 4,742 tumours.
Figure 4: Down-sampling analysis shows that gene discovery is continuing as samples and tumour types are added.
Figure 5: Number of samples needed to detect significantly mutated genes, as a function of a tumour type’s median background mutation frequency and a cancer gene’s mutation rate above background.

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Data deposits

The data analysed in this manuscript have been deposited in Synapse (, accession number syn1729383, and in dbGaP (, accession numbers phs000330.v1.p1, phs000348.v1.p1, phs000369.v1.p1, phs000370.v1.p1, phs000374.v1.p1, phs000435.v2.p1, phs000447.v1.p1, phs000450.v1.p1, phs000452.v1.p1, phs000467.v6.p1, phs000488.v1.p1, phs000504.v1.p1, phs000508.v1.p1, phs000579.v1.p1, phs000598.v1.p1.


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

    Article  CAS  PubMed  Google Scholar 

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. Imielinski, M. et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 150, 1107–1120 (2012)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nature Biotechnol. 30, 413–421 (2012)

    Article  CAS  Google Scholar 

  5. Cibulskis, K. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature Biotechnol. 31, 213–219 (2013)

    Article  CAS  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  8. Lohr, J. G. et al. Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing. Proc. Natl Acad. Sci. USA 109, 3879–3884 (2012)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Cancer Genome Atlas Research. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Tamborero, D. et al. Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci. Rep. 3, 2650 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  13. Ferlay, J. et al. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int. J. Cancer 127, 2893–2917 (2010)

    Article  CAS  PubMed  Google Scholar 

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This work was conducted as part of TCGA, a project of the National Cancer Institute and the National Human Genome Research Institute. We are grateful to T. I. Zack, S. E. Schumacher, and R. Beroukhim for sharing their copy-number analyses before publication.

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Authors and Affiliations



G.G., E.S.L., T.R.G., M.M., L.A.G. and S.B.G. conceived the project and provided leadership. M.S.L., G.G., E.S.L., P.S. and C.H.M. analysed the data and contributed to scientific discussions. M.S.L., E.S.L. and G.G. wrote the paper. J.T.R., M.S.L., E.S.L. and G.G. created the website for visualizing this data set.

Corresponding authors

Correspondence to Eric S. Lander or Gad Getz.

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Supplementary information

Supplementary Information

This file contains Supplementary Figures 1-9 and legends for Supplementary Tables 1-6 (see separate files for tables). (PDF 1511 kb)

Supplementary Table 1

This file contains a list of source datasets analyzed in this work, and references to the corresponding publications. (XLS 28 kb)

Supplementary Table 2

This file contains the 260 significantly mutated cancer genes found by analysis with the MutSig suite (see Supplementary Information file for full legend). (XLSX 709 kb)

Supplementary Table 3

This file contains a list of the 21 tumor types studied, and the significantly mutated genes found by the MutSig suite in each tumor type (see Supplementary Information file for full legend). (XLSX 16 kb)

Supplementary Table 4

The file contains a list of references reporting the identification of candidate cancer genes (see Supplementary Information file for full legend). (XLSX 58 kb)

Supplementary Table 5

This file contains a list of references to biological literature supporting the 33 novel candidate cancer genes with clear and compelling connections to cancer biology. (XLSX 21 kb)

Supplementary Table 6

This file contains a summary of the analysis comparing the performance of each of the three MutSig metrics separately, in pairwise combinations, and all three combined as in the main analysis (see Supplementary Information file for full legend). (XLS 24 kb)

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

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