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Discovery and saturation analysis of cancer genes across 21 tumour types

Nature volume 505, pages 495–501 (2014)Cite this article

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Abstract

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 (http://www.synapse.org), accession number syn1729383, and in dbGaP (http://www.ncbi.nlm.nih.gov/gap), 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.

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Acknowledgements

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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Author notes

  1. Eric S. Lander and Gad Getz: These authors contributed equally to this work.

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, 7 Cambridge Center, Cambridge, 02142, Massachusetts, USA

    Michael S. Lawrence, Petar Stojanov, Craig H. Mermel, James T. Robinson, Levi A. Garraway, Todd R. Golub, Matthew Meyerson, Stacey B. Gabriel, Eric S. Lander & Gad Getz

  2. Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, 02215, Massachusetts, USA

    Petar Stojanov, Levi A. Garraway, Todd R. Golub & Matthew Meyerson

  3. Cancer Center and Department of Pathology, Massachusetts General Hospital, 55 Fruit Street, Boston, Massachusetts 02114, USA,

    Craig H. Mermel & Gad Getz

  4. Harvard Medical School, 25 Shattuck Street, Boston, 02115, Massachusetts, USA

    Levi A. Garraway, Todd R. Golub, Matthew Meyerson, Eric S. Lander & Gad Getz

  5. Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, 20815, Maryland, USA

    Todd R. Golub

  6. Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, 02139, Massachusetts, USA

    Eric S. Lander

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  1. Michael S. Lawrence
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Contributions

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). https://doi.org/10.1038/nature12912

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