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.

Emerging patterns of somatic mutations in cancer

Key Points

  • The emergence of next-generation sequencing technology has made important contributions to our understanding of cancer genomes.

  • In this Review, we present a current view of the mutational landscapes of diverse cancer types.

  • There are various challenges to identifying driver mutations and functionally validating significantly mutated genes.

  • Whole-exome and whole-genome sequencing studies, and integrative analysis with other genomic platforms have provided biological insights into the aetiology of cancer.

  • Such studies are enabling the classification of cancers on the basis of genetic alterations rather than of tissue of origin.

Abstract

Recent advances in technological tools for massively parallel, high-throughput sequencing of DNA have enabled the comprehensive characterization of somatic mutations in a large number of tumour samples. In this Review, we describe recent cancer genomic studies that have assembled emerging views of the landscapes of somatic mutations through deep-sequencing analyses of the coding exomes and whole genomes in various cancer types. We discuss the comparative genomics of different cancers, including mutation rates and spectra, as well as the roles of environmental insults that influence these processes. We highlight the developing statistical approaches that are used to identify significantly mutated genes, and discuss the emerging biological and clinical insights from such analyses, as well as the future challenges of translating these genomic data into clinical impacts.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Frequent mutations of epigenetic regulators and pre-mRNA splicing machinery in cancers.

References

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

    CAS  PubMed  Google Scholar 

  2. Stratton, M. R. Exploring the genomes of cancer cells: progress and promise. Science 331, 1553–1558 (2011).

    Article  CAS  PubMed  Google Scholar 

  3. Vogelstein, B. & Kinzler, K. W. The multistep nature of cancer. Trends Genet. 9, 138–141 (1993).

    Article  CAS  PubMed  Google Scholar 

  4. Chin, L., Hahn, W. C., Getz, G. & Meyerson, M. Making sense of cancer genomic data. Genes Dev. 25, 534–555 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Meyerson, M., Gabriel, S. & Getz, G. Advances in understanding cancer genomes through second-generation sequencing. Nature Rev. Genet. 11, 685–696 (2010).

    Article  CAS  PubMed  Google Scholar 

  6. Parsons, D. W. et al. An integrated genomic analysis of human glioblastoma multiforme. Science 321, 1807–1812 (2008). This study demonstrated the promise of unbiased genomic sequencing through the identification of a highly recurrent mutation in a previously unknown oncogene, IDH1 , by using pre-NGS approaches involving PCR amplification and Sanger-based sequencing of more than 20,000 protein-coding genes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  8. Dang, L. et al. Cancer-associated IDH1 mutations produce 2-hydroxyglutarate. Nature 462, 739–744 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ward, P. S. et al. The common feature of leukemia-associated IDH1 and IDH2 mutations is a neomorphic enzyme activity converting α-ketoglutarate to 2-hydroxyglutarate. Cancer Cell 17, 225–234 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Noushmehr, H. et al. Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell 17, 510–522 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Crasta, K. et al. DNA breaks and chromosome pulverization from errors in mitosis. Nature 482, 53–58 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Stephens, P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011). This WGS study led to the description of a phenomenon termed chromothripsis, in which up to hundreds of genomic rearrangements take place in a single cellular crisis event.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Baca, S. C. et al. Punctuated evolution of prostate cancer genomes. Cell 153, 666–677 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Burns, M. B. et al. APOBEC3B is an enzymatic source of mutation in breast cancer. Nature 494, 366–370 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Nik-Zainal, S. et al. Mutational processes molding the genomes of 21 breast cancers. Cell 149, 979–993 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Taylor, B. J. et al. DNA deaminases induce break-associated mutation showers with implication of APOBEC3B and 3A in breast cancer kataegis. eLife 2, e00534 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

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

  19. Creighton, C. J. et al. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).

    Article  CAS  Google Scholar 

  20. Kandoth, C. et al. Integrated genomic characterization of endometrial carcinoma. Nature 497, 67–73 (2013).

    Article  CAS  PubMed  Google Scholar 

  21. Biankin, A. V. et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399–405 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

  23. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

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

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

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

  27. Fujimoto, A. et al. Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators. Nature Genet. 44, 760–764 (2012).

    Article  CAS  PubMed  Google Scholar 

  28. Papaemmanuil, E. et al. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N. Engl. J. Med. 365, 1384–1395 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Puente, X. S. et al. Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia. Nature 475, 101–105 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Quesada, V. et al. Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia. Nature Genet. 44, 47–52 (2012).

    Article  CAS  Google Scholar 

  31. Stephens, P. J. et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 486, 400–404 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Totoki, Y. et al. High-resolution characterization of a hepatocellular carcinoma genome. Nature Genet. 43, 464–469 (2011).

    Article  CAS  PubMed  Google Scholar 

  33. Cancer Genome Atlas Network. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

  34. Wang, K. et al. Exome sequencing identifies frequent mutation of ARID1A in molecular subtypes of gastric cancer. Nature Genet. 43, 1219–1223 (2011).

    Article  CAS  PubMed  Google Scholar 

  35. Zang, Z. J. et al. Exome sequencing of gastric adenocarcinoma identifies recurrent somatic mutations in cell adhesion and chromatin remodeling genes. Nature Genet. 44, 570–574 (2012).

    Article  CAS  PubMed  Google Scholar 

  36. Guichard, C. et al. Integrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinoma. Nature Genet. 44, 694–698 (2012).

    Article  CAS  PubMed  Google Scholar 

  37. Li, M. et al. Inactivating mutations of the chromatin remodeling gene ARID2 in hepatocellular carcinoma. Nature Genet. 43, 828–829 (2011).

    Article  CAS  PubMed  Google Scholar 

  38. Huang, J. et al. Exome sequencing of hepatitis B virus-associated hepatocellular carcinoma. Nature Genet. 44, 1117–1121 (2012).

    Article  CAS  PubMed  Google Scholar 

  39. Ong, C. K. et al. Exome sequencing of liver fluke-associated cholangiocarcinoma. Nature Genet. 44, 690–693 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lawrence, M. S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013). This paper highlights sources of mutational heterogeneity across cancer, which include cancer type, mutational spectrum, gene expression and DNA replication time. The authors developed MutSigCV, a method that corrects for these variations by using patient-specific mutation frequency and spectrum, and gene-specific BMR by incorporating expression level and replication time, to better identify lung cancer-associated genes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hodis, E. et al. A landscape of driver mutations in melanoma. Cell 150, 251–263 (2012). This study discusses the difficult task of identifying SMGs in cancers that possess a high and heterogeneous BMR. By taking into account mutations in flanking exons to better ascertain a gene-specific BMR, the authors developed the algorithm InVEx to identify melanoma- and lung cancer-associated genes.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Singh, D. et al. Transforming fusions of FGFR and TACC genes in human glioblastoma. Science 337, 1231–1235 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Banks, R. E. et al. Genetic and epigenetic analysis of von Hippel-Lindau (VHL) gene alterations and relationship with clinical variables in sporadic renal cancer. Cancer Res. 66, 2000–2011 (2006).

    Article  CAS  PubMed  Google Scholar 

  46. Dalgliesh, G. L. et al. Systematic sequencing of renal carcinoma reveals inactivation of histone modifying genes. Nature 463, 360–363 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Varela, I. et al. Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma. Nature 469, 539–542 (2011). This study provides another example of the power of unbiased genomic sequencing by identifying a second major ccRCC cancer gene, the SWI/SNF chromatin remodelling gene PBRM1 , targeted by frequent LOF mutations in 41% of samples.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wilson, B. G. & Roberts, C. W. SWI/SNF nucleosome remodellers and cancer. Nature Rev. Cancer 11, 481–492 (2011).

    Article  CAS  Google Scholar 

  49. Versteege, I. et al. Truncating mutations of hSNF5/INI1 in aggressive paediatric cancer. Nature 394, 203–206 (1998).

    Article  CAS  PubMed  Google Scholar 

  50. Wong, A. K. et al. BRG1, a component of the SWI-SNF complex, is mutated in multiple human tumor cell lines. Cancer Res. 60, 6171–6177 (2000).

    CAS  PubMed  Google Scholar 

  51. Harbour, J. W. et al. Frequent mutation of BAP1 in metastasizing uveal melanomas. Science 330, 1410–1413 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Testa, J. R. et al. Germline BAP1 mutations predispose to malignant mesothelioma. Nature Genet. 43, 1022–1025 (2011).

    Article  CAS  PubMed  Google Scholar 

  53. Pena-Llopis, S. et al. BAP1 loss defines a new class of renal cell carcinoma. Nature Genet. 44, 751–759 (2012).

    Article  CAS  PubMed  Google Scholar 

  54. Hakimi, A. A. et al. Adverse outcomes in clear cell renal cell carcinoma with mutations of 3p21 epigenetic regulators BAP1 and SETD2: A report by MSKCC and the KIRC TCGA research network. Clin. Cancer Res. 19, 3259–3267 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Sato, Y. et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nature Genet. 45, 860–867 (2013).

    Article  CAS  PubMed  Google Scholar 

  56. Agrawal, N. et al. Exome sequencing of head and neck squamous cell carcinoma reveals inactivating mutations in NOTCH1. Science 333, 1154–1157 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Nicolas, M. et al. Notch1 functions as a tumor suppressor in mouse skin. Nature Genet. 33, 416–421 (2003).

    Article  CAS  PubMed  Google Scholar 

  59. Extance, A. Alzheimer's failure raises questions about disease-modifying strategies. Nature Rev. Drug Discov. 9, 749–751 (2010).

    Article  CAS  Google Scholar 

  60. Davies, H. et al. Mutations of the BRAF gene in human cancer. Nature 417, 949–954 (2002). This study discovered the BRAF V600E mutation in 66% of malignant melanomas and at lower frequency in a wide range of human cancers. The subsequent development of an inhibitor to treat patients with BRAF -mutant metastatic melanoma provided the proof of concept for genomics-informed personalized therapy.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Krauthammer, M. et al. Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nature Genet. 44, 1006–1014 (2012).

    Article  CAS  PubMed  Google Scholar 

  63. Mok, T. S. Personalized medicine in lung cancer: what we need to know. Nature Rev. Clin. Oncol. 8, 661–668 (2011).

    Article  CAS  Google Scholar 

  64. 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 

  65. Rudin, C. M. et al. Comprehensive genomic analysis identifies SOX2 as a frequently amplified gene in small-cell lung cancer. Nature Genet. 44, 1111–1116 (2012).

    Article  CAS  PubMed  Google Scholar 

  66. Pleasance, E. D. et al. A small-cell lung cancer genome with complex signatures of tobacco exposure. Nature 463, 184–190 (2010).

    Article  CAS  PubMed  Google Scholar 

  67. Tomlins, S. A. et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer. Science 310, 644–648 (2005).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  69. Grasso, C. S. et al. The mutational landscape of lethal castration-resistant prostate cancer. Nature 487, 239–243 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Seshagiri, S. et al. Recurrent R–spondin fusions in colon cancer. Nature 488, 660–664 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Ellis, M. J. et al. Whole-genome analysis informs breast cancer response to aromatase inhibition. Nature 486, 353–360 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Shah, S. P. et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395–399 (2012).

    Article  CAS  PubMed  Google Scholar 

  76. Paterlini-Brechot, P. et al. Hepatitis B virus-related insertional mutagenesis occurs frequently in human liver cancers and recurrently targets human telomerase gene. Oncogene 22, 3911–3916 (2003).

    Article  CAS  PubMed  Google Scholar 

  77. Sung, W. K. et al. Genome-wide survey of recurrent HBV integration in hepatocellular carcinoma. Nature Genet. 44, 765–769 (2012).

    Article  CAS  PubMed  Google Scholar 

  78. Grimwade, D. & Hills, R. K. Independent prognostic factors for AML outcome. Hematology Am. Soc. Hematol. Educ. Program 385–395 (2009).

  79. Abdel-Wahab, O. et al. Genetic characterization of TET1, TET2, and TET3 alterations in myeloid malignancies. Blood 114, 144–147 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Gelsi-Boyer, V. et al. Mutations of polycomb-associated gene ASXL1 in myelodysplastic syndromes and chronic myelomonocytic leukaemia. Br. J. Haematol. 145, 788–800 (2009).

    Article  CAS  PubMed  Google Scholar 

  81. Makishima, H. et al. Novel homo- and hemizygous mutations in EZH2 in myeloid malignancies. Leukemia 24, 1799–1804 (2010).

    Article  CAS  PubMed  Google Scholar 

  82. Ernst, T. et al. Inactivating mutations of the histone methyltransferase gene EZH2 in myeloid disorders. Nature Genet. 42, 722–726 (2010).

    Article  CAS  PubMed  Google Scholar 

  83. Ley, T. J. et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 456, 66–72 (2008). The first WGS report for a human cancer using NGS technology that identified mutations in DNMT3A, IDH1 and IDH2 in human AML.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Ley, T. J. et al. DNMT3A mutations in acute myeloid leukemia. N. Engl. J. Med. 363, 2424–2433 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Yan, X. J. et al. Exome sequencing identifies somatic mutations of DNA methyltransferase gene DNMT3A in acute monocytic leukemia. Nature Genet. 43, 309–315 (2011).

    Article  CAS  PubMed  Google Scholar 

  86. Mardis, E. R. et al. Recurring mutations found by sequencing an acute myeloid leukemia genome. N. Engl. J. Med. 361, 1058–1066 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Graubert, T. A. et al. Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nature Genet. 44, 53–57 (2012).

    Article  CAS  Google Scholar 

  88. Welch, J. S. et al. The origin and evolution of mutations in acute myeloid leukemia. Cell 150, 264–278 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Yoshida, K. et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 478, 64–69 (2011). The first study to report frequent novel pathway mutations involving multiple components of the RNA splicing machinery, which occurred in a mutually exclusive manner, specific to myeloid neoplasms showing features of myelodysplasia. Frequent mutations in spliceosomal genes have since been discovered in a number of haematological malignancies and solid tumours.

    Article  CAS  PubMed  Google Scholar 

  90. Walter, M. J. et al. Clonal diversity of recurrently mutated genes in myelodysplastic syndromes. Leukemia 27, 1275–1282 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Makishima, H. et al. Somatic SETBP1 mutations in myeloid malignancies. Nature Genet. 45, 942–946 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Quesada, V., Ramsay, A. J. & Lopez-Otin, C. Chronic lymphocytic leukemia with SF3B1 mutation. N. Engl. J. Med. 366, 2530 (2012).

    Article  CAS  PubMed  Google Scholar 

  94. Fabbri, G. et al. Analysis of the chronic lymphocytic leukemia coding genome: role of NOTCH1 mutational activation. J. Exp. Med. 208, 1389–1401 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Mullighan, C. G. et al. Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia. Nature 446, 758–764 (2007).

    Article  CAS  PubMed  Google Scholar 

  96. Mullighan, C. G. et al. CREBBP mutations in relapsed acute lymphoblastic leukaemia. Nature 471, 235–239 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Zhang, J. et al. The genetic basis of early T-cell precursor acute lymphoblastic leukaemia. Nature 481, 157–163 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Morin, R. D. et al. Frequent mutation of histone-modifying genes in non-Hodgkin lymphoma. Nature 476, 298–303 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. 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  PubMed  PubMed Central  Google Scholar 

  100. Pasqualucci, L. et al. Inactivating mutations of acetyltransferase genes in B-cell lymphoma. Nature 471, 189–195 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Pasqualucci, L. et al. Analysis of the coding genome of diffuse large B-cell lymphoma. Nature Genet. 43, 830–837 (2011).

    Article  CAS  PubMed  Google Scholar 

  102. Kridel, R. et al. Whole transcriptome sequencing reveals recurrent NOTCH1 mutations in mantle cell lymphoma. Blood 119, 1963–1971 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Walker, B. A. et al. Intraclonal heterogeneity and distinct molecular mechanisms characterize the development of t(4;14) and t(11;14) myeloma. Blood 120, 1077–1086 (2012).

    Article  CAS  PubMed  Google Scholar 

  105. Zenz, T. et al. TP53 mutation and survival in chronic lymphocytic leukemia. J. Clin. Oncol. 28, 4473–4479 (2010).

    Article  PubMed  Google Scholar 

  106. Trbusek, M. et al. Missense mutations located in structural p53 DNA-binding motifs are associated with extremely poor survival in chronic lymphocytic leukemia. J. Clin. Oncol. 29, 2703–2708 (2011).

    Article  CAS  PubMed  Google Scholar 

  107. Austen, B. et al. Mutations in the ATM gene lead to impaired overall and treatment-free survival that is independent of IGVH mutation status in patients with B-CLL. Blood 106, 3175–3182 (2005).

    Article  CAS  PubMed  Google Scholar 

  108. Treon, S. P. et al. MYD88 L265P somatic mutation in Waldenstrom's macroglobulinemia. N. Engl. J. Med. 367, 826–833 (2012).

    Article  CAS  PubMed  Google Scholar 

  109. Kiel, M. J. et al. Whole-genome sequencing identifies recurrent somatic NOTCH2 mutations in splenic marginal zone lymphoma. J. Exp. Med. 209, 1553–1565 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Koskela, H. L. et al. Somatic STAT3 mutations in large granular lymphocytic leukemia. N. Engl. J. Med. 366, 1905–1913 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Tiacci, E. et al. BRAF mutations in hairy-cell leukemia. N. Engl. J. Med. 364, 2305–2315 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Dietrich, S. et al. BRAF inhibition in refractory hairy-cell leukemia. N. Engl. J. Med. 366, 2038–2040 (2012).

    Article  PubMed  Google Scholar 

  113. Kuo, K. T. et al. Frequent activating mutations of PIK3CA in ovarian clear cell carcinoma. Am. J. Pathol. 174, 1597–1601 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Jones, S. et al. Frequent mutations of chromatin remodeling gene ARID1A in ovarian clear cell carcinoma. Science 330, 228–231 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Wiegand, K. C. et al. ARID1A mutations in endometriosis-associated ovarian carcinomas. N. Engl. J. Med. 363, 1532–1543 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Van Raamsdonk, C. D. et al. Frequent somatic mutations of GNAQ in uveal melanoma and blue naevi. Nature 457, 599–602 (2009).

    Article  CAS  PubMed  Google Scholar 

  117. Van Raamsdonk, C. D. et al. Mutations in GNA11 in uveal melanoma. N. Engl. J. Med. 363, 2191–2199 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Schwartzentruber, J. et al. Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature 482, 226–231 (2012).

    Article  CAS  PubMed  Google Scholar 

  119. Tefferi, A. et al. Proposals and rationale for revision of the World Health Organization diagnostic criteria for polycythemia vera, essential thrombocythemia, and primary myelofibrosis: recommendations from an ad hoc international expert panel. Blood 110, 1092–1097 (2007).

    Article  CAS  PubMed  Google Scholar 

  120. Grimwade, D. et al. Refinement of cytogenetic classification in acute myeloid leukemia: determination of prognostic significance of rare recurring chromosomal abnormalities among 5876 younger adult patients treated in the United Kingdom Medical Research Council trials. Blood 116, 354–365 (2010).

    Article  CAS  PubMed  Google Scholar 

  121. Greenberg, P. L. et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood 120, 2454–2465 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Bejar, R. et al. Clinical effect of point mutations in myelodysplastic syndromes. N. Engl. J. Med. 364, 2496–2506 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Bejar, R. et al. Validation of a prognostic model and the impact of mutations in patients with lower-risk myelodysplastic syndromes. J. Clin. Oncol. 30, 3376–3382 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Patel, J. P. et al. Prognostic relevance of integrated genetic profiling in acute myeloid leukemia. N. Engl. J. Med. 366, 1079–1089 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Schlenk, R. F. et al. Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia. N. Engl. J. Med. 358, 1909–1918 (2008).

    Article  CAS  PubMed  Google Scholar 

  126. Rosenbloom, K. R. et al. ENCODE data in the UCSC genome browser: year 5 update. Nucleic Acids Res. 41, D56–D63 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Horn, S. et al. TERT promoter mutations in familial and sporadic melanoma. Science 339, 959–961 (2013).

    Article  CAS  PubMed  Google Scholar 

  128. Huang, F. W. et al. Highly recurrent TERT promoter mutations in human melanoma. Science 339, 957–959 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Yates, L. R. & Campbell, P. J. Evolution of the cancer genome. Nature Rev. Genet. 13, 795–806 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  131. Greenman, C., Wooster, R., Futreal, P. A., Stratton, M. R. & Easton, D. F. Statistical analysis of pathogenicity of somatic mutations in cancer. Genetics 173, 2187–2198 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Parmigiani, G. et al. Statistical methods for the analysis of cancer genome sequencing data. Johns Hopkins University, Dept. of Biostatistics Working Papers [online], (2007).

    Google Scholar 

  133. Youn, A. & Simon, R. Identifying cancer driver genes in tumor genome sequencing studies. Bioinformatics 27, 175–181 (2011).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Kan, Z. et al. Diverse somatic mutation patterns and pathway alterations in human cancers. Nature 466, 869–873 (2010).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  137. Dees, N. D. et al. MuSiC: identifying mutational significance in cancer genomes. Genome Res. 22, 1589–1598 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  139. Schuster-Bockler, B. & Lehner, B. Chromatin organization is a major influence on regional mutation rates in human cancer cells. Nature 488, 504–507 (2012).

    Article  CAS  PubMed  Google Scholar 

  140. Hellmann, I. et al. Why do human diversity levels vary at a megabase scale? Genome Res. 15, 1222–1231 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Stamatoyannopoulos, J. A. et al. Human mutation rate associated with DNA replication timing. Nature Genet. 41, 393–395 (2009).

    Article  CAS  PubMed  Google Scholar 

  142. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nature Methods 7, 248–249 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Reva, B., Antipin, Y. & Sander, C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39, e118 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Sim, N. L. et al. SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 40, W452–457 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Carter, H. et al. Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. Cancer Res. 69, 6660–6667 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Verhaak, R. G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Shibata, T. et al. Cancer related mutations in NRF2 impair its recognition by Keap1–Cul3 E3 ligase and promote malignancy. Proc. Natl Acad. Sci. USA 105, 13568–13573 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Ciriello, G., Cerami, E., Sander, C. & Schultz, N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 22, 398–406 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Vandin, F., Upfal, E. & Raphael, B. J. Algorithms for detecting significantly mutated pathways in cancer. J. Computat. Biol. 18, 507–522 (2011).

    Article  CAS  Google Scholar 

  150. Garraway, L. A. et al. Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma. Nature 436, 117–122 (2005).

    Article  CAS  PubMed  Google Scholar 

  151. Ruark, E. et al. Mosaic PPM1D mutations are associated with predisposition to breast and ovarian cancer. Nature 493, 406–410 (2013).

    Article  CAS  PubMed  Google Scholar 

  152. Heyer, J., Kwong, L. N., Lowe, S. W. & Chin, L. Non-germline genetically engineered mouse models for translational cancer research. Nature Rev. Cancer 10, 470–480 (2010).

    Article  CAS  Google Scholar 

  153. Kim, M. et al. Comparative oncogenomics identifies NEDD9 as a melanoma metastasis gene. Cell 125, 1269–1281 (2006).

    Article  CAS  PubMed  Google Scholar 

  154. Maser, R. S. et al. Chromosomally unstable mouse tumours have genomic alterations similar to diverse human cancers. Nature 447, 966–971 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Zender, L. et al. Identification and validation of oncogenes in liver cancer using an integrative oncogenomic approach. Cell 125, 1253–1267 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  156. Wartman, L. D. et al. Sequencing a mouse acute promyelocytic leukemia genome reveals genetic events relevant for disease progression. J. Clin. Invest. 121, 1445–1455 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012). This paper provided important insights into the evolution of clonal populations in AML throughout cancer treatment using longitudinal sampling and WGS analysis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012). This study examined intra-tumour heterogeneity by profiling spatially separated tumour biopsy samples from primary and associated metastatic samples with various genomic platforms, including WES. The analysis demonstrates how single tumour biopsy samples underestimated the genomic landscape and presents the challenges that intra-tumour heterogeneity pose to the advancement of personalized medicine.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Tao, Y. et al. Rapid growth of a hepatocellular carcinoma and the driving mutations revealed by cell-population genetic analysis of whole-genome data. Proc. Natl Acad. Sci. USA 108, 12042–12047 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  161. Govindan, R. et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 150, 1121–1134 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors apologize for the omission of any pertinent work related to this Review. They thank D. Spring and members of the Chin laboratory for their comments and feedback. I.R.W. is a recipient of the Canadian Institutes of Health Research Fellowship; K.T. is supported by the Kimberly Patterson Leukemia Fellowship and Celgene Future Leaders in Hematology Award; L.C. and P.A.F. are recipients of the Cancer Prevention and Research Institute of Texas (CPRIT) Established Investigator Recruitment Award. This project was supported by CPRIT and Grant Number U24CA143845 from the US National Cancer Institute (NCI). The content is solely the responsibility of the authors and does not necessarily represent the official view of the NCI or the US National Institutes of Health.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to P. Andrew Futreal or Lynda Chin.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Supplementary information

Supplementary information S1 (figure)

Recurrent and significantly mutated genes identified from analysis of whole-exome and whole-genome sequencing studies of solid tumours. (PDF 163 kb)

Supplementary information S2 (figure)

Recurrent and significantly mutated genes identified from analysis of whole-exome and whole-genome sequencing studies of haematologic malignancies. (PDF 140 kb)

Glossary

Next-generation sequencing

(NGS). All post-Sanger DNA sequencing methods, most commonly referring to massively parallel sequencing technology.

Hybrid-capture

A target enrichment approach in which custom oligonucleotides (the bait set) are designed and optimized to hybridize to specific regions of the genome so that specific fragments of DNA can be enriched by hybridization for next-generation sequencing.

Whole-exome sequencing

(WES). Next-generation sequencing of all protein- coding exomes after capture, using hybridization to a whole-exome bait set designed to enrich DNA in all protein-coding portions of the genome. The most common implementation also targets microRNA genes. The size of the captured DNA is approximately 40 Mb.

Whole-genome sequencing

(WGS). Sequencing of the entire genome, usually by random fragmentation (shotgun) and to sufficient coverage so as to ensure adequate representation of all alleles. A variation specifically using low-coverage WGS is sometimes used to assess rearrangements in the genome.

Cancer genes

Genes that harbour cancer-driving genetic aberrations (however, cancer genes may possess both driver and passenger somatic alterations) as defined by criteria including statistical evidence of selection and recurrence pattern, or by functional activity.

Chromothripsis

Greek for chromosome 'shattering', in which up to hundreds of genomic rearrangements take place in a single cellular crisis event that develops from errors in mitosis that occur in ~2–3% of cancers.

Chromoplexy

Greek for chromosome 'weave' or 'braid'. A process whereby a closed chain of chromosomes is formed by copy-neutral rearrangements that consist of 4–12 distinct breakpoint junctions, which tend to occur at transcriptionally active portions of chromatin.

Kategeis

Greek for 'shower' or 'thunderstorm'. A phenomenon, identified in breast cancers, of localized hypermutations almost exclusively involving cytosine base pair substitutions at TpC dinucleotides. This mutation pattern has been linked to the APOBEC family of cytidine deaminases.

Driver mutations

Somatic mutations in a gene that confer a selective advantage on cancer cells as reflected in the statistical evidence of positive selection. This is not a definition based on functional activity.

Significantly mutated genes

(SMGs). Genes that have a somatic mutation rate above the calculated background mutation rate as determined by a given statistical calculation.

Background mutation rate

(BMR). The rate of mutation in a tumour sample as a consequence of exposure to environmental mutagens (for example, ultraviolet radiation) and/or random generation and misrepair processes.

RNA sequencing

(RNA-seq). Whole-transcriptome shotgun sequencing of cDNA to determine the sequence of RNA; used for expression analysis and the identification of gene–gene fusions.

Two-hit tumour suppressor

Refers to the necessity to inactivate both alleles of a tumour suppressor gene, following the Knudson two-hit hypothesis, which was proposed to explain the early onset of cancer in hereditary syndromes in which the inheritance of one germline copy of a mutated gene in all cells substantially increases the likelihood of any cell acquiring a mutation in the other allele.

Passenger mutations

Neutral mutations in a gene that do not provide a selective advantage for cancer cells as reflected by the lack of statistical evidence for positive or negative selection. This is not a definition based on functional activity.

Hotspot mutation

A recurrent mutation resulting in the same amino acid change in a gene observed in cancer, signifying strong positive selection.

CpG island methylator phenotype

A classification of cancers by their degree of methylation at CpG-rich promoter regions, first characterized in human colorectal cancers; often associated with distinct epidemiological, histological and molecular features.

Microsatellite instability

(MSI). A hypermutable phenotype caused by germline, somatic or epigenetic inactivation in DNA mismatch repair activity.

Epstein–Barr virus

(EBV). A member of the Herpes virus family that is associated with the development of particular forms of cancer.

Triple-negative breast cancer

(TNBC). One of the subtypes of breast cancer that is defined by the absence of staining for oestrogen receptor, progesterone receptor and ERBB2 by immunohistochemistry.

Neoadjuvant aromatase inhibitors

Used to treat patients with oestrogen receptor-positive breast cancer before surgical resection and is applied in cases in which tumour size needs to be reduced for breast-conserving surgery. This treatment is not currently considered as a standard of care and is conducted under clinical trials.

Sleeping Beauty transposon mutagenesis

A genetically engineered insertional mutagenesis system involving synthetic DNA transposons, which can be applied to various model systems to ascertain gene function.

Aflatoxin B

One of the mycotoxins that are produced by Aspergillus Flavus. High-level exposure to aflatoxins is known to cause acute liver necrosis or cirrhosis, resulting in the development of hepatocellular carcinoma.

French–American–British (FAB) classification

First proposed in 1976 by the French–American–British cooperative group and updated in 1989, it classified acute myeloid leukaemia into eight different categories (M0–M7) and acute lymphoblastic leukaemia into three different categories (L1–L3) based on their morphological findings.

Actionable genetic alterations

Genetic alterations with sufficient scientific evidence supporting their use to inform treatment decisions.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Watson, I., Takahashi, K., Futreal, P. et al. Emerging patterns of somatic mutations in cancer. Nat Rev Genet 14, 703–718 (2013). https://doi.org/10.1038/nrg3539

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg3539

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer