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.

Cancer genome-sequencing study design

Key Points

  • This article focuses on the aims, methodological requisites and study designs of cancer genome-sequencing studies. Thus far, most cancer genome-sequencing studies have had the aims of discovering driver mutations, identifying somatic mutational signatures, characterizing clonal evolution and/or advancing personalized medicine.

  • Key aspects of second-generation cancer genome-sequencing study design include full sequencing of the matched normal genome of the cancer type, at least 30-fold redundant sequence coverage for the detection of inherited and somatic single-nucleotide variation and verification resequencing to confirm the somatic status of acquired mutations.

  • Single-patient studies are hypothesis-generating and have the potential to inform clinical practice, but they do not allow for generalization of findings. The discovery cohort is a group of cancers of the same type, or subtype, subject to second-generation sequencing. Discovery cohort studies have the potential to detect recurrent somatic mutations of genes and pathways.

  • Multi-ome discovery cohorts use second-generation technology to sequence an assortment of genomes, exomes and/or transcriptomes in a group of cancers of the same type or subtype.

Abstract

Discoveries from cancer genome sequencing have the potential to translate into advances in cancer prevention, diagnostics, prognostics, treatment and basic biology. Given the diversity of downstream applications, cancer genome-sequencing studies need to be designed to best fulfil specific aims. Knowledge of second-generation cancer genome-sequencing study design also facilitates assessment of the validity and importance of the rapidly growing number of published studies. In this Review, we focus on the practical application of second-generation sequencing technology (also known as next-generation sequencing) to cancer genomics and discuss how aspects of study design and methodological considerations — such as the size and composition of the discovery cohort — can be tailored to serve specific research aims.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Cancer genome second-generation-sequencing study designs.
Figure 2: The integration of transcriptome and epigenome with whole-genome sequencing.

References

  1. Stratton, M. R., Campbell, P. J. & Futreal, P. A. The cancer genome. Nature 458, 719–724 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ley, T. J. et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 456, 66–72 (2008). This was first study to use second-generation technology to sequence a cancer genome. It established cancer genome sequencing as an unbiased method for discovering candidate driver mutations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature 431, 931–945 (2004).

  4. Lander, E. S. et al. Initial sequencing and analysis of the human genome. Nature 409, 860–921 (2001).

    Article  CAS  PubMed  Google Scholar 

  5. Battelle Technology Partnership Practice. Economic impact of the Human Genome Project: how a $3.8 billion investment drove $796 billion in economic impact, created 310,000 jobs, and launched the genomic revolution. battelle.org[online], (2011).

  6. Morin, R. D. et al. Somatic mutations altering EZH2 (Tyr641) in follicular and diffuse large B-cell lymphomas of germinal-center origin. Nature Genet. 42, 181–185 (2010) (2011).

    Article  CAS  PubMed  Google Scholar 

  7. Sneeringer, C. J. et al. Coordinated activities of wild-type plus mutant EZH2 drive tumor-associated hypertrimethylation of lysine 27 on histone H3 (H3K27) in human B-cell lymphomas. Proc. Natl Acad. Sci. USA 107, 20980–20985 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  8. McCabe, M. T. et al. EZH2 inhibition as a therapeutic strategy for lymphoma with EZH2-activating mutations. Nature 492, 108–112 (2012).

    Article  CAS  PubMed  Google Scholar 

  9. 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 

  10. Stephens, P. J. et al. Massive genomic rearrangement acquired in a single catastrophic event during cancer development. Cell 144, 27–40 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532–536 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Northcott, P. A. et al. Medulloblastomics: the end of the beginning. Nature Rev. Cancer 12, 818–834 (2012).

    Article  CAS  Google Scholar 

  13. 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 

  14. 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 

  15. Link, D. C. Identification of a novel TP53 cancer susceptibility mutation through whole-genome sequencing of a patient with therapy-related AML. JAMA 305, 1568 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Shah, S. P. et al. Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution. Nature 461, 809–813 (2009). This study used ultra-deep resequencing to characterize clonal evolution and showed that variable somatic mutation allele frequencies can reflect different subclones. Moreover, considerable evolution can occur over time.

    Article  CAS  PubMed  Google Scholar 

  17. Jones, S. J. et al. Evolution of an adenocarcinoma in response to selection by targeted kinase inhibitors. Genome Biol. 11, R82 (2010). This work incorporated second-generation sequencing into the personalized medicine framework. Specifically, the intent of the case study was to inform physician decision making with respect to treatment of a rare cancer.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ding, L. et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 464, 999–1005 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Pleasance, E. D. et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Pleasance, E. D. et al. A small-cell lung cancer genome with complex signatures of tobacco exposure. Nature 463, 184–190 (2009). This study highlighted that the distribution and composition of somatic mutations across a genome is not uniform. It showed that through examining the mutational signatures, researchers can gain insight into the mechanisms and processes that may have given rise to the mutations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lee, W. et al. The mutation spectrum revealed by paired genome sequences from a lung cancer patient. Nature 465, 473–477 (2010).

    Article  CAS  PubMed  Google Scholar 

  22. Levy, S. et al. The diploid genome sequence of an individual human. PLoS Biol. 5, e254 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Wheeler, D. A. et al. The complete genome of an individual by massively parallel DNA sequencing. Nature 452, 872–876 (2008).

    Article  CAS  PubMed  Google Scholar 

  24. Wang, J. et al. The diploid genome sequence of an Asian individual. Nature 456, 60–65 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bentley, D. R. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008). An accurate consensus sequence was built with second-generation technology from >30-fold redundant coverage of 35 bp paired-end reads.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Pelak, K. et al. The characterization of twenty sequenced human genomes. PLoS Genet. 6, e1001111 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 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 

  28. 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 

  29. 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 

  30. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ajay, S. S., Parker, S. C. J., Ozel Abaan, H., Fuentes Fajardo, K. V. & Margulies, E. H. Accurate and comprehensive sequencing of personal genomes. Genome Res. 21, 1498–1505 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Turajlic, S. et al. Whole genome sequencing of matched primary and metastatic acral melanomas. Genome Res. 22, 196–207 (2011).

    Article  CAS  PubMed  Google Scholar 

  35. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Peña-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 

  38. Ng, C. K. et al. The role of tandem duplicator phenotype in tumour evolution in high-grade serous ovarian cancer. J. Pathol. 226, 703–712 (2012).

    Article  CAS  PubMed  Google Scholar 

  39. Stephens, P. J. et al. Complex landscapes of somatic rearrangement in human breast cancer genomes. Nature 462, 1005–1010 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kloosterman, W. P. et al. Chromothripsis is a common mechanism driving genomic rearrangements in primary and metastatic colorectal cancer. Genome Biol. 12, R103 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. McBride, D. J. et al. Tandem duplication of chromosomal segments is common in ovarian and breast cancer genomes. J. Pathol. 227, 446–455 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Campbell, P. J. et al. Identification of somatically acquired rearrangements in cancer using genome-wide massively parallel paired-end sequencing. Nature Genet. 40, 722–729 (2008). By investigating the paired-end sequencing reads that did not align to the reference genome as expected with respect to each other, the authors were able to demonstrate a high-throughput and high-resolution bioinformatics method to characterize structural variation.

    Article  CAS  PubMed  Google Scholar 

  43. Muzny, D. M. et al. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012). With 97 colorectal cancer genomes sequenced to low-to-moderate redundant coverage, this discovery cohort is the largest to date.

    Article  CAS  Google Scholar 

  44. Korbel, J. O. et al. Paired-end mapping reveals extensive structural variation in the human genome. Science 318, 420–426 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Onishi-Seebacher, M. & Korbel, J. O. Challenges in studying genomic structural variant formation mechanisms: the short-read dilemma and beyond. BioEssays 33, 840–850 (2011).

    Article  CAS  PubMed  Google Scholar 

  46. Simpson, J. T. et al. ABySS: a parallel assembler for short read sequence data. Genome Res. 19, 1117–1123 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Fullwood, M. J., Wei, C.-L., Liu, E. T. & Ruan, Y. Next-generation DNA sequencing of paired-end tags (PET) for transcriptome and genome analyses. Genome Res. 19, 521–532 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Medvedev, P., Stanciu, M. & Brudno, M. Computational methods for discovering structural variation with next-generation sequencing. Nature Methods 6, S13–S20 (2009).

    Article  CAS  PubMed  Google Scholar 

  49. Hillmer, A. M. et al. Comprehensive long-span paired-end-tag mapping reveals characteristic patterns of structural variations in epithelial cancer genomes. Genome Res. 21, 665–675 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Boeva, V. et al. Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data. Bioinformatics 28, 423–425 (2012).

    Article  CAS  PubMed  Google Scholar 

  51. Druker, B. J. et al. Five-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N. Engl. J. Med. 355, 2408–2417 (2006).

    Article  CAS  PubMed  Google Scholar 

  52. Lee, E. et al. Landscape of somatic retrotransposition in human cancers. Science 337, 967–971 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Welch, J. S. Use of whole-genome sequencing to diagnose a cryptic fusion oncogene. JAMA 305, 1577 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Weiss, G. J. et al. Paired tumor and normal whole genome sequencing of metastatic olfactory neuroblastoma. PLoS ONE 7, e37029 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 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 

  56. Bueno, R. et al. Second generation sequencing of the mesothelioma tumor genome. PLoS ONE 5, e10612 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  58. Demeure, M. J. et al. Cancer of the ampulla of Vater: analysis of the whole genome sequence exposes a potential therapeutic vulnerability. Genome Med. 4, 56 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Muller, F. L. et al. Passenger deletions generate therapeutic vulnerabilities in cancer. Nature 488, 337–342 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. 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 

  61. Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012). This paper demonstrates the utility of characterizing the somatic mutational signature with the discovery of kataegis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. 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 

  63. Wu, G. et al. Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas and non-brainstem glioblastomas. Nature Genet. 44, 251–253 (2012).

    Article  CAS  PubMed  Google Scholar 

  64. 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 

  65. 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 

  66. Harbour, J. W. et al. Frequent mutation of BAP1 in metastasizing uveal melanomas. Science 330, 1410–1413 (2010). This study discovered a gene that was somatically mutated in an impressive number of metastasizing tumours using second-generation sequencing of exomes. This study highlights that there are novel and valuable candidate therapeutic targets that are yet to be discovered.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Yoshida, K. et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature 478, 64–69 (2011).

    Article  CAS  PubMed  Google Scholar 

  68. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Sathirapongsasuti, J. F. et al. Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV. Bioinformatics 27, 2648–2654 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Karakoc, E. et al. Detection of structural variants and indels within exome data. Nature Methods 9, 176–178 (2012).

    Article  CAS  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Ruan, Y. et al. Fusion transcripts and transcribed retrotransposed loci discovered through comprehensive transcriptome analysis using paired-end ditags (PETs). Genome Res. 17, 828–838 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-seq. Nature Methods 5, 621–628 (2008).

    Article  CAS  PubMed  Google Scholar 

  75. Roberts, K. G. et al. Genetic alterations activating kinase and cytokine receptor signaling in high-risk acute lymphoblastic leukemia. Cancer Cell 22, 153–166 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Jones, D. T. W. et al. Dissecting the genomic complexity underlying medulloblastoma. Nature 488, 100–105 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Hammerman, P. S. et al. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

    Article  CAS  Google Scholar 

  78. Morin, R. D. et al. Application of massively parallel sequencing to microRNA profiling and discovery in human embryonic stem cells. Genome Res. 18, 610–621 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Wang, Z., Gerstein, M. & Snyder, M. RNA-seq: a revolutionary tool for transcriptomics. Nature Rev. Genet. 10, 57–63 (2009).

    Article  CAS  PubMed  Google Scholar 

  80. 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 

  81. Vaske, C. J. et al. Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237–i245 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Hawkins, R. D., Hon, G. C. & Ren, B. Next-generation genomics: an integrative approach. Nature Rev. Genet. 11, 476–486 (2010).

    Article  CAS  PubMed  Google Scholar 

  83. 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 

  84. Ju, Y. S. et al. A transforming KIF5B and RET gene fusion in lung adenocarcinoma revealed from whole-genome and transcriptome sequencing. Genome Res. 22, 436–445 (2011).

    Article  CAS  PubMed  Google Scholar 

  85. Esteller, M. Cancer epigenomics: DNA methylomes and histone-modification maps. Nature Rev. Genet. 8, 286–298 (2007).

    Article  CAS  PubMed  Google Scholar 

  86. Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).

    Article  CAS  PubMed  Google Scholar 

  87. Lister, R. et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462, 315–322 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Zhang, J. et al. A novel retinoblastoma therapy from genomic and epigenetic analyses. Nature 481, 329–334 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Cheung, N.-K. V. et al. Association of age at diagnosis and genetic mutations in patients with neuroblastoma. JAMA 307, 1062–1071 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Molenaar, J. J. et al. Sequencing of neuroblastoma identifies chromothripsis and defects in neuritogenesis genes. Nature 483, 589–593 (2012). This is one of the largest discovery cohorts to date. Researchers sequenced the genomes of 87 tumour–normal pairs to at least 30-fold redundant coverage.

    Article  CAS  PubMed  Google Scholar 

  91. Collins, C. C. et al. Next generation sequencing of prostate cancer from a patient identifies a deficiency of methylthioadenosine phosphorylase, an exploitable tumor target. Mol. Cancer Ther. 11, 775–783 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  93. Rausch, T. et al. Genome sequencing of pediatric medulloblastoma links catastrophic DNA rearrangements with TP53 mutations. Cell 148, 59–71 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. 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 

  95. Klein, G. Lymphoma development in mice and humans: diversity of initiation is followed by convergent cytogenetic evolution. Proc. Natl Acad. Sci. USA 76, 2442–2446 (1979).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Castoe, T. A., De Koning, A. P. J. & Pollock, D. D. Adaptive molecular convergence: molecular evolution versus molecular phylogenetics. Commun. Integr. Biol. 3, 67–69 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Kimchi-Sarfaty, C. et al. A 'silent' polymorphism in the MDR1 gene changes substrate specificity. Science 315, 525–528 (2007).

    Article  CAS  PubMed  Google Scholar 

  99. Pagani, F., Raponi, M. & Baralle, F. E. Synonymous mutations in CFTR exon 12 affect splicing and are not neutral in evolution. Proc. Natl Acad. Sci. USA 102, 6368–6372 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Wu, C. et al. Integrated genome and transcriptome sequencing identifies a novel form of hybrid and aggressive prostate cancer. J. Pathol. 227, 53–61 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Jones, S. et al. Comparative lesion sequencing provides insights into tumor evolution. Proc. Natl Acad. Sci. USA 105, 4283–4288 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  105. 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 

Download references

Acknowledgements

J.C.M. thanks the Canadian Institutes of Health Research and the Michael Smith Foundation for Health Research for their support. M.A.M. is the University of British Columbia, Canada Research Chair in Genome Science.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marco A. Marra.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Supplementary information

Supplementary informtation S1 (table)

Second-generation sequencing cancer genomics study aims (PDF 155 kb)

Supplementary informtation S2 (table)

Second-generation sequencing cancer genomics methodological requisites (PDF 115 kb)

Supplementary informtation S3 (table)

Second-generation sequencing cancer genomics study design (PDF 141 kb)

Glossary

Driver mutations

Somatic mutations that have a role in creating, controlling and/or directing some aspect of the cancer phenotype.

Kataegis

From the Greek meaning 'thunderstorm', this refers to clusters of somatic single-nucleotide variants that often colocalize with somatic structural variants.

Chromothripsis

From the Greek meaning 'chromosome shattering', this refers to a single event of genome shattering and reassembly that results in complex somatic structural variations characterized by oscillating copy number and tens to hundreds of rearrangements that localize to one or a few chromosomes.

Redundant sequence coverage

The total number of bases sequenced divided by the total number of bases in the haploid genome.

B allele frequencies

Frequencies equal to B/(A+B), where A is the count for the reference nucleotide at an inherited single-nucleotide polymorphism (SNP) position, and B is the count for the alternate nucleotide at that same SNP position.

Paired-end reads

Sequencing reads from each end of the same DNA molecule. Knowing the sequence of both reads and the length of the DNA molecule improves mapping to a reference sequence, de novo assembly and detecting structural variations.

Chimeric genes

A combination of segments of two or more genes that forms a new gene.

Split reads

Sequencing reads that align to non-contiguous spans of the reference sequence owing to somatic structural variation.

Mass-spectrometric genotyping

A method that generates locus-specific amplicons followed by primer extension that incorporates mass-modified dideoxynucleotides at the single-nucleotide polymorphism position. A mass spectrometer then measures the differential mass of the products.

Multi-ome discovery cohort

A cohort of cancer genomes, exomes and/or transcriptomes; more than one omic measurement per sample is not necessary.

Allelic imbalances

Unequal transcript levels of the alleles of a gene.

Integration omics

Examining how somatic mutation or deregulation of a genome, transcriptome and/or epigenome converge on a pathway, process or gene; more than one omic measurement per sample is not necessary. For example, gene inactivation through single-nucleotide variants or epigenomic silencing.

Interaction omics

Examining how the somatic mutation or deregulation of the genome, transcriptome and/or epigenome affect one another; more than one omic measurement per sample is ideal. For example, somatic copy number variants can have effects on transcript levels.

Custom capture

Hybridization or amplification of selected regions of the genome to specifically capture loci for second-generation sequencing.

Ultra-deep second-generation resequencing

Greater than 100-fold redundant sequence coverage of a targeted selection of somatic mutations.

Multi-region sequencing

Sequencing of distinct regions of the same solid tumour, this allows for the examination of intra-tumour heterogeneity and clonal evolution.

Clinically actionable drug targets

Biological molecules or processes that can be targeted by an existing or experimental drug.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Mwenifumbo, J., Marra, M. Cancer genome-sequencing study design. Nat Rev Genet 14, 321–332 (2013). https://doi.org/10.1038/nrg3445

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrg3445

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