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Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples

Nature Biotechnology volume 31, pages 213–219 (2013) Cite this article

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Abstract

Detection of somatic point substitutions is a key step in characterizing the cancer genome. However, existing methods typically miss low-allelic-fraction mutations that occur in only a subset of the sequenced cells owing to either tumor heterogeneity or contamination by normal cells. Here we present MuTect, a method that applies a Bayesian classifier to detect somatic mutations with very low allele fractions, requiring only a few supporting reads, followed by carefully tuned filters that ensure high specificity. We also describe benchmarking approaches that use real, rather than simulated, sequencing data to evaluate the sensitivity and specificity as a function of sequencing depth, base quality and allelic fraction. Compared with other methods, MuTect has higher sensitivity with similar specificity, especially for mutations with allelic fractions as low as 0.1 and below, making MuTect particularly useful for studying cancer subclones and their evolution in standard exome and genome sequencing data.

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Figure 1: Overview of the detection of a somatic point mutation using MuTect.
Figure 2: Sensitivity as a function of sequencing depth and allelic fraction.
Figure 3: Specificity of variant detection and variant classification estimated using the virtual-tumor approach.
Figure 4: Benchmarking mutation-detection methods.

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References

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

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

  3. Banerji, S. et al. Sequence analysis of mutations and translocations across breast cancer subtypes. Nature 486, 405–409 (2012).

    Article  CAS  Google Scholar 

  4. Stransky, N. et al. The mutational landscape of head and neck squamous cell carcinoma. Science 333, 1157–1160 (2011).

    Article  CAS  Google Scholar 

  5. Ding, L. et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 455, 1069–1075 (2008).

    Article  CAS  Google Scholar 

  6. Berger, M.F. et al. Melanoma genome sequencing reveals frequent PREX2 mutations. Nature 485, 502–506 (2012).

    Article  CAS  Google Scholar 

  7. Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

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

    Article  CAS  Google Scholar 

  9. Walter, M.J. et al. Clonal architecture of secondary acute myeloid leukemia. N. Engl. J. Med. 366, 1090–1098 (2012).

    Article  CAS  Google Scholar 

  10. Park, S.Y., Gönen, M., Kim, H.J., Michor, F. & Polyak, K. Cellular and genetic diversity in the progression of in situ human breast carcinomas to an invasive phenotype. J. Clin. Invest. 120, 636 (2010).

    Article  CAS  Google Scholar 

  11. Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).

    Article  CAS  Google Scholar 

  12. Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).

    Article  CAS  Google Scholar 

  13. Landau, D.A. et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell advance online publication, doi: 10.1016/j.cell.2013.01.019 (14 February 2013).

  14. Campbell, P.J. et al. The patterns and dynamics of genomic instability in metastatic pancreatic cancer. Nature 467, 1109–1113 (2010).

    Article  CAS  Google Scholar 

  15. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    Article  CAS  Google Scholar 

  16. Hou, Y. et al. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell 148, 873–885 (2012).

    Article  CAS  Google Scholar 

  17. Xu, X. et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886–895 (2012).

    Article  CAS  Google Scholar 

  18. International Cancer Genome Consortium et al. International network of cancer genome projects. Nature 464, 993–998 (2010).

  19. Chapman, M.A. et al. Initial genome sequencing and analysis of multiple myeloma. Nature 471, 467–472 (2011).

    Article  CAS  Google Scholar 

  20. Getz, G. et al. Comment on “The consensus coding sequences of human breast and colorectal cancers”. Science 317, 1500 (2007).

    Article  CAS  Google Scholar 

  21. Larson, D.E. et al. SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics 28, 311–317 (2012).

    Article  CAS  Google Scholar 

  22. Roth, A. et al. JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Bioinformatics 28, 907–913 (2012).

    Article  CAS  Google Scholar 

  23. Saunders, C.T. et al. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics 28, 1811–1817 (2012).

    Article  CAS  Google Scholar 

  24. Barbieri, C.E. et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat. Genet. 44, 685–689 (2012).

    Article  CAS  Google Scholar 

  25. Bass, A.J. et al. Genomic sequencing of colorectal adenocarcinomas identifies a recurrent VTI1A–TCF7L2 fusion. Nat. Genet. 43, 964–968 (2011).

    Article  CAS  Google Scholar 

  26. Wang, L. et al. SF3B1 and other novel cancer genes in chronic lymphocytic leukemia. N. Engl. J. Med. 365, 2497–2506 (2011).

    Article  CAS  Google Scholar 

  27. Pugh, T.J. et al. Medulloblastoma exome sequencing uncovers subtype-specific somatic mutations. Nature 488, 106–110 (2012).

    Article  CAS  Google Scholar 

  28. Berger, M.F. et al. The genomic complexity of primary human prostate cancer. Nature 470, 214–220 (2011).

    Article  CAS  Google Scholar 

  29. 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  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  31. Wang, P. et al. Mutations in isocitrate dehydrogenase 1 and 2 occur frequently in intrahepatic cholangiocarcinomas and share hypermethylation targets with glioblastomas. Oncogene advance, online publication, doi:10.1038/onc.2012.315 (23 July 2012).

    Article  Google Scholar 

  32. Durinck, S. et al. Temporal dissection of tumorigenesis in primary cancers. Cancer Discov. 1, 137–143 (2011).

    Article  CAS  Google Scholar 

  33. Lee, R.S. et al. A remarkably simple genome underlies highly malignant pediatric rhabdoid cancers. J. Clin. Invest. 122, 2983–2988 (2012).

    Article  CAS  Google Scholar 

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

  35. Hodis, E. et al. A landscape of driver mutations in melanoma. Cell 150, 251–263 (2012).

    Article  CAS  Google Scholar 

  36. Forbes, S.A. et al. COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res. 39, D945–D950 (2011).

    Article  CAS  Google Scholar 

  37. Pleasance, E.D. et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2010).

    Article  CAS  Google Scholar 

  38. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  Google Scholar 

  39. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  40. DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    Article  CAS  Google Scholar 

  41. Sherry, S.T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).

    Article  CAS  Google Scholar 

  42. 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  43. Gnerre, S. et al. High-quality draft assemblies of mammalian genomes from massively parallel sequence data. Proc. Natl. Acad. Sci. USA 108, 1513–1518 (2011).

    Article  CAS  Google Scholar 

  44. Shah, S.P. et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 461, 809–813 (2009).

    Article  CAS  Google Scholar 

  45. Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010).

    Article  CAS  Google Scholar 

  46. Cibulskis, K. et al. ContEst: estimating cross-contamination of human samples in next-generation sequencing data. Bioinformatics 27, 2601–2602 (2011).

    Article  CAS  Google Scholar 

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Acknowledgements

This work was supported by US National Institutes of Health grants U54HG003067 and U24CA143845. We thank the Genome Analysis ToolKit (GATK) group, and our beta test users for their valuable feedback.

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Authors and Affiliations

  1. The Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA

    Kristian Cibulskis, Michael S Lawrence, Scott L Carter, Andrey Sivachenko, David Jaffe, Carrie Sougnez, Stacey Gabriel, Matthew Meyerson, Eric S Lander & Gad Getz

  2. Divisions of Medical Oncology and Cancer Biology, and Center for Cancer Genome Discovery, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Matthew Meyerson

  3. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA

    Eric S Lander

  4. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Eric S Lander

  5. Massachusetts General Hospital Cancer Center and Department of Pathology, Boston, Massachusetts, USA

    Gad Getz

Authors
  1. Kristian Cibulskis
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  2. Michael S Lawrence
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  3. Scott L Carter
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  4. Andrey Sivachenko
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  8. Matthew Meyerson
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  9. Eric S Lander
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  10. Gad Getz
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Contributions

D.J. posed the concept of using a statistical method and filters to detect somatic mutations. G.G. and K.C. conceived and designed MuTect and the analysis. K.C. implemented the algorithm and performed the analysis. M.S.L. conceived of and initially developed the PON filter. G.G. and S.L.C. developed the power calculations and investigated subclonal events detected with MuTect. C.S. and A.S. assisted in the generation and interpretation of validation data. D.J., C.S. and M.M. critically reviewed the manuscript. K.C., G.G. and E.S.L. wrote the manuscript. G.G., M.M., S.G. and E.S.L. led the project.

Corresponding author

Correspondence to Gad Getz.

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Competing interests

K.C., G.G. and M.S.L. are inventors on US provisional patent application no. 61/693,987 covering the method described in the paper.

Supplementary information

Supplementary Text and Figures (download PDF )

Supplementary Methods and Supplementary Figures 1–6 (PDF 265 kb)

Supplementary Tables 1 and 4 (download XLSX )

MuTect Calculated Sensitivity by Mutant Allele Fraction and Tumor Sequencing Depth and Novel Chromosome 20 COLO-829 Mutations Detected By 4 Methods (MuTect, SomaticSniper, JointSNVMix and Strelka) (XLSX 24 kb)

Supplementary Data (download ZIP )

MuTect source code (ZIP 101 kb)

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Cibulskis, K., Lawrence, M., Carter, S. et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol 31, 213–219 (2013). https://doi.org/10.1038/nbt.2514

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