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Review article: Biomedical intelligence

Vol. 149 No. 1314 (2019)

High-throughput sequencing in clinical oncology: from past to present

  • Benoîte Mery
  • Alexis Vallard
  • Elise Rowinski
  • Nicolas Magne
Cite this as:
Swiss Med Wkly. 2019;149:w20057
Published
04.04.2019

Summary

The war on cancer remains a major challenge. One of the obstacles to additional progress is the complexity of the mechanisms underlying the disease. Cutting-edge technologies and computing tools have led to whole genome sequencing and an integrated and inclusive omic approach to cancers, from accurate molecular signatures of tumours to impressive progress in the field of next-generation sequencing (NGS). Genomic data may foster strategies for new drug development in addition to a better understanding of cancer genesis, opening a new era in oncological clinical practice. This review discusses the development of genomics approaches in cancer research and the potential of genomics for precision medicine, as well as clinical implications and remaining challenges.

References

  1. Bray F, Soerjomataram I. The Changing Global Burden of Cancer: Transitions in Human Development and Implications for Cancer Prevention and Control, The International Bank for Reconstruction and Development / The World Bank, 2015. doi:https://doi.org/10.1596/978-1-4648-0349-9_CH2
  2. Sporn MB. The war on cancer. Lancet. 1996;347(9012):1377–81. doi:.https://doi.org/10.1016/S0140-6736(96)91015-6
  3. Garland J. Unravelling the complexity of signalling networks in cancer: A review of the increasing role for computational modelling. Crit Rev Oncol Hematol. 2017;117:73–113. doi:.https://doi.org/10.1016/j.critrevonc.2017.06.004
  4. Azizi E, Clouthier SG, Wicha MS. The Promise of Single Cell Omics for Onco-therapy. J Mol Genet Med. 2014;8(03). doi:.https://doi.org/10.4172/1747-0862.1000121
  5. Epstein RJ, Lin FP. Cancer and the omics revolution. Aust Fam Physician. 2017;46(4):189–93.
  6. Hinkson IV, Davidsen TM, Klemm JD, Kerlavage AR, Kibbe WA. A Comprehensive Infrastructure for Big Data in Cancer Research: Accelerating Cancer Research and Precision Medicine. Front Cell Dev Biol. 2017;5:83. doi:.https://doi.org/10.3389/fcell.2017.00083
  7. Vucic EA, Thu KL, Robison K, Rybaczyk LA, Chari R, Alvarez CE, et al. Translating cancer ‘omics’ to improved outcomes. Genome Res. 2012;22(2):188–95. doi:.https://doi.org/10.1101/gr.124354.111
  8. Grossman RL, Heath AP, Ferretti V, Varmus HE, Lowy DR, Kibbe WA, et al. Toward a Shared Vision for Cancer Genomic Data. N Engl J Med. 2016;375(12):1109–12. doi:.https://doi.org/10.1056/NEJMp1607591
  9. Griffith OL, Gray JW. 'Omic approaches to preventing or managing metastatic breast cancer. Breast Cancer Res. 2011;13(6):230. doi:.https://doi.org/10.1186/bcr2923
  10. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5–29. doi:.https://doi.org/10.3322/caac.21254
  11. Coleman WB. Next-Generation Breast Cancer Omics. Am J Pathol. 2017;187(10):2130–2. doi:.https://doi.org/10.1016/j.ajpath.2017.07.011
  12. Wood SL, Westbrook JA, Brown JE. Omic-profiling in breast cancer metastasis to bone: implications for mechanisms, biomarkers and treatment. Cancer Treat Rev. 2014;40(1):139–52. doi:.https://doi.org/10.1016/j.ctrv.2013.07.006
  13. Yu K-H, Snyder M. Omics Profiling in Precision Oncology. Mol Cell Proteomics. 2016;15(8):2525–36. doi:.https://doi.org/10.1074/mcp.O116.059253
  14. Gonzalez-Angulo AM, Hennessy BTJ, Mills GB. Future of personalized medicine in oncology: a systems biology approach. J Clin Oncol. 2010;28(16):2777–83. doi:.https://doi.org/10.1200/JCO.2009.27.0777
  15. Kotelnikova EA, Pyatnitskiy M, Paleeva A, Kremenetskaya O, Vinogradov D. Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine. Oncotarget. 2016;7(32):52493–516. doi:.https://doi.org/10.18632/oncotarget.9370
  16. Home - The Cancer Genome Atlas - Cancer Genome - TCGA. (n.d.). https://cancergenome.nih.gov/ (accessed February 14, 2018).
  17. Giordano TJ. The cancer genome atlas research network: a sight to behold. Endocr Pathol. 2014;25(4):362–5. doi:.https://doi.org/10.1007/s12022-014-9345-4
  18. Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A, Meissner A, et al. The NIH Roadmap Epigenomics Mapping Consortium. Nat Biotechnol. 2010;28(10):1045–8. doi:.https://doi.org/10.1038/nbt1010-1045
  19. Vucic EA, Thu KL, Robison K, Rybaczyk LA, Chari R, Alvarez CE, et al. Translating cancer ‘omics’ to improved outcomes. Genome Res. 2012;22(2):188–95. doi:.https://doi.org/10.1101/gr.124354.111
  20. Zhang J, Baran J, Cros A, Guberman JM, Haider S, Hsu J, et al. International Cancer Genome Consortium Data Portal--a one-stop shop for cancer genomics data. Database (Oxford). 2011;2011(0):bar026–026. doi:.https://doi.org/10.1093/database/bar026
  21. Buescher JM, Driggers EM. Integration of omics: more than the sum of its parts. Cancer Metab. 2016;4(1):4. doi:.https://doi.org/10.1186/s40170-016-0143-y
  22. Bentley DR. Whole-genome re-sequencing. Curr Opin Genet Dev. 2006;16(6):545–52. doi:.https://doi.org/10.1016/j.gde.2006.10.009
  23. Git A, Dvinge H, Salmon-Divon M, Osborne M, Kutter C, Hadfield J, et al. Systematic comparison of microarray profiling, real-time PCR, and next-generation sequencing technologies for measuring differential microRNA expression. RNA. 2010;16(5):991–1006. doi:.https://doi.org/10.1261/rna.1947110
  24. Bahassi M, Stambrook PJ. Next-generation sequencing technologies: breaking the sound barrier of human genetics. Mutagenesis. 2014;29(5):303–10. doi:.https://doi.org/10.1093/mutage/geu031
  25. Ulahannan D, Kovac MB, Mulholland PJ, Cazier J-B, Tomlinson I. Technical and implementation issues in using next-generation sequencing of cancers in clinical practice. Br J Cancer. 2013;109(4):827–35. doi:.https://doi.org/10.1038/bjc.2013.416
  26. Chakravarthi BVSK, Nepal S, Varambally S. Genomic and Epigenomic Alterations in Cancer. Am J Pathol. 2016;186(7):1724–35. doi:.https://doi.org/10.1016/j.ajpath.2016.02.023
  27. Lee H, Lee K-W, Lee T, Park D, Chung J, Lee C, et al. Performance evaluation method for read mapping tool in clinical panel sequencing. Genes Genomics. 2018;40(2):189–97. doi:.https://doi.org/10.1007/s13258-017-0621-9
  28. Escalona M, Rocha S, Posada D. A comparison of tools for the simulation of genomic next-generation sequencing data. Nat Rev Genet. 2016;17(8):459–69. doi:. Correction published in: Nat Rev Genet. 2018;19:733. https://doi.org/10.1038/nrg.2016.57
  29. Daber R, Sukhadia S, Morrissette JJD. Understanding the limitations of next generation sequencing informatics, an approach to clinical pipeline validation using artificial data sets. Cancer Genet. 2013;206(12):441–8. doi:.https://doi.org/10.1016/j.cancergen.2013.11.005
  30. Molecular Oncology Tumor Boards | ASCO University, (n.d.). https://university.asco.org/motb (accessed February 21, 2018).
  31. Harada S, Arend R, Dai Q, Levesque JA, Winokur TS, Guo R, et al. Implementation and utilization of the molecular tumor board to guide precision medicine. Oncotarget. 2017;8(34):57845–54. doi:.https://doi.org/10.18632/oncotarget.18471
  32. Chen K, Meric-Bernstam F, Zhao H, Zhang Q, Ezzeddine N, Tang LY, et al. Clinical actionability enhanced through deep targeted sequencing of solid tumors. Clin Chem. 2015;61(3):544–53. doi:.https://doi.org/10.1373/clinchem.2014.231100
  33. Brennan CW, Verhaak RGW, McKenna A, Campos B, Noushmehr H, Salama SR, et al.; TCGA Research Network. The somatic genomic landscape of glioblastoma. Cell. 2013;155(2):462–77. doi:.. Erratum in: Cell. 2014;157(3):753. doi:https://doi.org/10.1016/j.cell.2013.09.034
  34. Verhaak RGW, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, et al.; Cancer Genome Atlas Research Network. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17(1):98–110. doi:.https://doi.org/10.1016/j.ccr.2009.12.020
  35. Sahm F, Schrimpf D, Jones DTW, Meyer J, Kratz A, Reuss D, et al. Next-generation sequencing in routine brain tumor diagnostics enables an integrated diagnosis and identifies actionable targets. Acta Neuropathol. 2016;131(6):903–10. doi:.https://doi.org/10.1007/s00401-015-1519-8
  36. Hallberg B, Palmer RH. Crizotinib--latest champion in the cancer wars? N Engl J Med. 2010;363(18):1760–2. doi:.https://doi.org/10.1056/NEJMe1010404
  37. Camidge DR, Pao W, Sequist LV. Acquired resistance to TKIs in solid tumours: learning from lung cancer. Nat Rev Clin Oncol. 2014;11(8):473–81. doi:.https://doi.org/10.1038/nrclinonc.2014.104
  38. Reiman A, Kikuchi H, Scocchia D, Smith P, Tsang YW, Snead D, et al. Validation of an NGS mutation detection panel for melanoma. BMC Cancer. 2017;17(1):150. doi:.https://doi.org/10.1186/s12885-017-3149-0
  39. Hodis E, Watson IR, Kryukov GV, Arold ST, Imielinski M, Theurillat J-P, et al. A landscape of driver mutations in melanoma. Cell. 2012;150(2):251–63. doi:.https://doi.org/10.1016/j.cell.2012.06.024
  40. Malapelle U, Vigliar E, Sgariglia R, Bellevicine C, Colarossi L, Vitale D, et al. Ion Torrent next-generation sequencing for routine identification of clinically relevant mutations in colorectal cancer patients. J Clin Pathol. 2015;68(1):64–8. doi:.https://doi.org/10.1136/jclinpath-2014-202691
  41. Tinhofer I, Budach V, Saki M, Konschak R, Niehr F, Jöhrens K, et al.; DKTK-ROG. Targeted next-generation sequencing of locally advanced squamous cell carcinomas of the head and neck reveals druggable targets for improving adjuvant chemoradiation. Eur J Cancer. 2016;57:78–86. doi:.https://doi.org/10.1016/j.ejca.2016.01.003
  42. Peng C, Ma W, Xia W, Zheng W. Integrated analysis of differentially expressed genes and pathways in triple‑negative breast cancer. Mol Med Rep. 2017;15(3):1087–94. doi:.https://doi.org/10.3892/mmr.2017.6101
  43. Schwaederle M, Daniels GA, Piccioni DE, Fanta PT, Schwab RB, Shimabukuro KA, et al. On the Road to Precision Cancer Medicine: Analysis of Genomic Biomarker Actionability in 439 Patients. Mol Cancer Ther. 2015;14(6):1488–94. doi:.https://doi.org/10.1158/1535-7163.MCT-14-1061
  44. Wong SQ, Fellowes A, Doig K, Ellul J, Bosma TJ, Irwin D, et al. Assessing the clinical value of targeted massively parallel sequencing in a longitudinal, prospective population-based study of cancer patients. Br J Cancer. 2015;112(8):1411–20. doi:.https://doi.org/10.1038/bjc.2015.80
  45. Uzilov AV, Ding W, Fink MY, Antipin Y, Brohl AS, Davis C, et al. Development and clinical application of an integrative genomic approach to personalized cancer therapy. Genome Med. 2016;8(1):62. doi:.https://doi.org/10.1186/s13073-016-0313-0
  46. Lane BR, Bissonnette J, Waldherr T, Ritz-Holland D, Chesla D, Cottingham SL, et al.; Spectrum Health Tumor Sequencing Advisory Board. Development of a Center for Personalized Cancer Care at a Regional Cancer Center: Feasibility Trial of an Institutional Tumor Sequencing Advisory Board. J Mol Diagn. 2015;17(6):695–704. doi:.https://doi.org/10.1016/j.jmoldx.2015.07.003
  47. Boland GM, Piha-Paul SA, Subbiah V, Routbort M, Herbrich SM, Baggerly K, et al. Clinical next generation sequencing to identify actionable aberrations in a phase I program. Oncotarget. 2015;6(24):20099–110. doi:.https://doi.org/10.18632/oncotarget.4040
  48. Tran B, Brown AMK, Bedard PL, Winquist E, Goss GD, Hotte SJ, et al. Feasibility of real time next generation sequencing of cancer genes linked to drug response: results from a clinical trial. Int J Cancer. 2013;132(7):1547–55. doi:.https://doi.org/10.1002/ijc.27817
  49. Kim ST, Lee J, Hong M, Park K, Park JO, Ahn T, et al. The NEXT-1 (Next generation pErsonalized tX with mulTi-omics and preclinical model) trial: prospective molecular screening trial of metastatic solid cancer patients, a feasibility analysis. Oncotarget. 2015;6(32):33358–68. doi:.https://doi.org/10.18632/oncotarget.5188
  50. Lih C-J, Harrington RD, Sims DJ, Harper KN, Bouk CH, Datta V, et al. Analytical Validation of the Next-Generation Sequencing Assay for a Nationwide Signal-Finding Clinical Trial. J Mol Diagn. 2017;19(2):313–27. doi:.https://doi.org/10.1016/j.jmoldx.2016.10.007
  51. Lih C-J, Sims DJ, Harrington RD, Polley EC, Zhao Y, Mehaffey MG, et al. Analytical Validation and Application of a Targeted Next-Generation Sequencing Mutation-Detection Assay for Use in Treatment Assignment in the NCI-MPACT Trial. J Mol Diagn. 2016;18(1):51–67. doi:.https://doi.org/10.1016/j.jmoldx.2015.07.006
  52. Foekens JA, Atkins D, Zhang Y, Sweep FCGJ, Harbeck N, Paradiso A, et al. Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. J Clin Oncol. 2006;24(11):1665–71. doi:.https://doi.org/10.1200/JCO.2005.03.9115
  53. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005;365(9460):671–9. doi:.https://doi.org/10.1016/S0140-6736(05)70933-8
  54. van ’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AAM, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415(6871):530–6. doi:.https://doi.org/10.1038/415530a
  55. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817–26. doi:.https://doi.org/10.1056/NEJMoa041588
  56. Griffith OL, Gray JW. 'Omic approaches to preventing or managing metastatic breast cancer. Breast Cancer Res. 2011;13(6):230. doi:.https://doi.org/10.1186/bcr2923
  57. Schwaederle M, Parker BA, Schwab RB, Fanta PT, Boles SG, Daniels GA, et al. Molecular tumor board: the University of California-San Diego Moores Cancer Center experience. Oncologist. 2014;19(6):631–6. doi:.https://doi.org/10.1634/theoncologist.2013-0405
  58. Yap TA, Gerlinger M, Futreal PA, Pusztai L, Swanton C. Intratumor heterogeneity: seeing the wood for the trees. Sci Transl Med. 2012;4(127):127ps10. doi:.https://doi.org/10.1126/scitranslmed.3003854
  59. Basho RK, Eterovic AK. Meric-Bernstam F. Clinical Applications and Limitations of Next-Generation Sequencing. Am J Hematol Oncol. 2015;11. Available at: https://www.gotoper.com/publications/ajho/2015/2015mar/clinical-applications-and-limitations-of-next-generation-sequencing
  60. Nikiforov YE, Carty SE, Chiosea SI, Coyne C, Duvvuri U, Ferris RL, et al. Highly accurate diagnosis of cancer in thyroid nodules with follicular neoplasm/suspicious for a follicular neoplasm cytology by ThyroSeq v2 next-generation sequencing assay. Cancer. 2014;120(23):3627–34. doi:.https://doi.org/10.1002/cncr.29038
  61. Yadav NK, Shukla P, Omer A, Pareek S, Srivastava AK, Bansode FW, et al. Erratum to “Next Generation Sequencing: Potential and Application in Drug Discovery”. Sci World J. 2014;2014:1. doi:.https://doi.org/10.1155/2014/621354
  62. Kulski JK. Next-Generation Sequencing — An Overview of the History, Tools, and “Omic” Applications, in: Next Gener. Seq. - Adv. Appl. Challenges, InTech, 2016. doi:https://doi.org/10.5772/61964.
  63. Wheatley D. Cancer research: quo vadis-to war? Ecancermedicalscience. 2014;8:ed45. doi:.https://doi.org/10.3332/ecancer.2014.ed45
  64. Horak P, Fröhling S, Glimm H. Integrating next-generation sequencing into clinical oncology: strategies, promises and pitfalls. ESMO Open. 2016;1(5):e000094. doi:.https://doi.org/10.1136/esmoopen-2016-000094