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.

You are viewing this page in draft mode.

High-throughput oncogene mutation profiling in human cancer

A Corrigendum to this article was published on 01 April 2007

This article has been updated

Abstract

Systematic efforts are underway to decipher the genetic changes associated with tumor initiation and progression1,2. However, widespread clinical application of this information is hampered by an inability to identify critical genetic events across the spectrum of human tumors with adequate sensitivity and scalability. Here, we have adapted high-throughput genotyping to query 238 known oncogene mutations across 1,000 human tumor samples. This approach established robust mutation distributions spanning 17 cancer types. Of 17 oncogenes analyzed, we found 14 to be mutated at least once, and 298 (30%) samples carried at least one mutation. Moreover, we identified previously unrecognized oncogene mutations in several tumor types and observed an unexpectedly high number of co-occurring mutations. These results offer a new dimension in tumor genetics, where mutations involving multiple cancer genes may be interrogated simultaneously and in 'real time' to guide cancer classification and rational therapeutic intervention.

NOTE: In the version of this article initially published, the name of an author was spelled incorrectly as Laura MacConnaill. The correct spelling is Laura MacConaill. The error has been corrected in the HTML and PDF versions of the article.

This is a preview of subscription content

Access options

Figure 1: Frequencies of oncogene mutations across human tumor types.
Figure 2: Mutually exclusive and co-occurring oncogene mutations in human cancer.

Change history

  • 14 March 2007

    NOTE: In the version of this article initially published, the name of an author was spelled incorrectly as Laura MacConnaill. The correct spelling is Laura MacConaill. The error has been corrected in the HTML and PDF versions of the article.

References

  1. National Human Genome Research Institute. Cancer Sequencing.http://www.genome.gov/cancersequencing/〉 (2006).

  2. Sjoblom, T. et al. The consensus coding sequences of human breast and colorectal cancers. Science 314, 268–274 (2006).

    Article  Google Scholar 

  3. National Cancer Institute and National Human Genome Research Institute. The Cancer Genome Atlas. 〈http://cancergenome.nih.gov/index.asp〉 (2006).

  4. Heinrich, M.C. et al. Kinase mutations and imatinib response in patients with metastatic gastrointestinal stromal tumor. J. Clin. Oncol. 21, 4342–4349 (2003).

    CAS  Article  Google Scholar 

  5. Thomas, R.K. et al. Detection of oncogenic mutations in the EGFR gene in lung adenocarcinoma with differential sensitivity to EGFR tyrosine kinase inhibitors. Cold Spring Harb. Symp. Quant. Biol. 70, 73–81 (2005).

    CAS  Article  Google Scholar 

  6. Paez, J.G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497–1500 (2004).

    CAS  Article  Google Scholar 

  7. Pao, W. et al. EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc. Natl. Acad. Sci. USA 101, 13306–13311 (2004).

    CAS  Article  Google Scholar 

  8. Lynch, T.J. et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 350, 2129–2139 (2004).

    CAS  Google Scholar 

  9. Solit, D.B. et al. BRAF mutation predicts sensitivity to MEK inhibition. Nature 439, 358–362 (2006).

    CAS  Article  Google Scholar 

  10. Thomas, R.K. et al. Sensitive mutation detection in heterogeneous cancer specimens by massively parallel picoliter reactor sequencing. Nat. Med. 12, 852–855 (2006).

    CAS  Article  Google Scholar 

  11. Bansal, A. et al. Association testing by DNA pooling: an effective initial screen. Proc. Natl. Acad. Sci. USA 99, 16871–16874 (2002).

    CAS  Article  Google Scholar 

  12. Werner, M. et al. Large-scale determination of SNP allele frequencies in DNA pools using MALDI-TOF mass spectrometry. Hum. Mutat. 20, 57–64 (2002).

    CAS  Article  Google Scholar 

  13. Kralovics, R. et al. A gain-of-function mutation of JAK2 in myeloproliferative disorders. N. Engl. J. Med. 352, 1779–1790 (2005).

    CAS  Article  Google Scholar 

  14. Levine, R.L. et al. Activating mutation in the tyrosine kinase JAK2 in polycythemia vera, essential thrombocythemia, and myeloid metaplasia with myelofibrosis. Cancer Cell 7, 387–397 (2005).

    CAS  Article  Google Scholar 

  15. James, C. et al. A unique clonal JAK2 mutation leading to constitutive signalling causes polycythaemia vera. Nature 434, 1144–1148 (2005).

    CAS  Article  Google Scholar 

  16. Baxter, E.J. et al. Acquired mutation of the tyrosine kinase JAK2 in human myeloproliferative disorders. Lancet 365, 1054–1061 (2005).

    CAS  Article  Google Scholar 

  17. Chesi, M. et al. Frequent translocation t(4;14)(p16.3;q32.3) in multiple myeloma is associated with increased expression and activating mutations of fibroblast growth factor receptor 3. Nat. Genet. 16, 260–264 (1997).

    CAS  Article  Google Scholar 

  18. Nakahara, M. et al. A novel gain-of-function mutation of c-kit gene in gastrointestinal stromal tumors. Gastroenterology 115, 1090–1095 (1998).

    CAS  Article  Google Scholar 

  19. Heinrich, M.C. et al. Molecular correlates of imatinib resistance in gastrointestinal stromal tumors. J. Clin. Oncol. 24, 4764–4774 (2006).

    CAS  Article  Google Scholar 

  20. Ikediobi, O.N. et al. Mutation analysis of 24 known cancer genes in the NCI-60 cell line set. Mol. Cancer Ther. 5, 2606–2612 (2006).

    CAS  Article  Google Scholar 

  21. Lee, J.C. et al. EGFR activation in glioblastoma through novel missense mutations in the extracellular domain. PLoS Med. 3, e485 (2006).

    Article  Google Scholar 

  22. Wan, P.T. et al. Mechanism of activation of the RAF-ERK signaling pathway by oncogenic mutations of B-RAF. Cell 116, 855–867 (2004).

    CAS  Article  Google Scholar 

  23. Weinstein, I.B. & Joe, A.K. Mechanisms of disease: oncogene addiction--a rationale for molecular targeting in cancer therapy. Nat. Clin. Pract. Oncol. 8, 448–457 (2006).

    Article  Google Scholar 

  24. Bamford, S. et al. The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br. J. Cancer 91, 355–358 (2004).

    CAS  Article  Google Scholar 

  25. Jiang, J. et al. Identification and characterization of a novel activating mutation of the FLT3 tyrosine kinase in AML. Blood (2004).

  26. Naoki, K., Chen, T.H., Richards, W.G., Sugarbaker, D.J. & Meyerson, M. Missense mutations of the BRAF gene in human lung adenocarcinoma. Cancer Res. 62, 7001–7003 (2002).

    CAS  PubMed  Google Scholar 

  27. Paez, J.G. et al. Genome coverage and sequence fidelity of phi29 polymerase-based multiple strand displacement whole genome amplification. Nucleic Acids Res. 32, e71 (2004).

    Article  Google Scholar 

Download references

Acknowledgements

We thank E. Lander and G. Getz for comments and advice. R.K.T. is a Mildred-Scheel fellow of the Deutsche Krebshilfe. R.K.T. is supported by the International Association for the Study of Lung Cancer (IASLC). R.M.D. is supported by the Swiss national science foundation (no: 3100A0-103671/1). A.G and J.M. are supported by the National Cancer Institute through SPORE grant P50CA70907. G.D.D. is supported by the Virginia and Daniel K. Ludwig Trust for Cancer Research, the Quick Family Fund for Cancer Research and the Ronald O. Perelman Fund for Cancer Research at Dana-Farber. I.K.M. and P.S.M. are supported by Accelerate Brain Tumor Cure. I.K.M., L.M.L, T.F.C., and P.S.M. are supported by the Henry E. Singleton Brain Tumor Program. I.K.M., L.M.L, T.F.C., S.F.N., M.M., W.R.S. and P.S.M. are supported by the Brain Tumor Funders' Collaborative. M.M. and L.A.G. are supported by a grant from Genentech, Inc. M.M. is supported by the American Cancer Society. L.A.G is supported by the National Cancer Institute, the Prostate Cancer Foundation, the Burroughs-Wellcome Fund, the Robert Wood Johnson Foundation and the Novartis Institute for Biomedical Research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Levi A Garraway.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1

Distribution of peak height ratios for recurrent oncogene point mutations. (PDF 1731 kb)

Supplementary Fig. 2

Mutation prevalence as determined by high-throughput genotyping. (PDF 666 kb)

Supplementary Fig. 3

Distribution of oncogene mutations by gene. (PDF 1275 kb)

Supplementary Fig. 4

Oncogene mutation prevalence by tumor type. (PDF 2953 kb)

Supplementary Table 1

Oncogene mutations and nucleotide changes. (XLS 120 kb)

Supplementary Table 2

Oncogene mutation peak height ratios. (XLS 75 kb)

Supplementary Table 3

Oncogene mutation assay completion. (XLS 51 kb)

Supplementary Note (PDF 66 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Thomas, R., Baker, A., DeBiasi, R. et al. High-throughput oncogene mutation profiling in human cancer. Nat Genet 39, 347–351 (2007). https://doi.org/10.1038/ng1975

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng1975

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing