Skip to main content

Advertisement

Log in

Clinical target sequencing for precision medicine of breast cancer

  • Review Article
  • Published:
International Journal of Clinical Oncology Aims and scope Submit manuscript

Abstract

Precision medicine can be defined as the customization of medical treatment based on the individual genetic profile, which enables one to identify patients who respond to therapies while sparing side effects for those who do not. Breast cancer patients have been treated based on subtyping, which is considered a prototype of precision medicine. Furthermore, the development of multigene panel testing has resulted in a paradigm shift in the treatment of breast cancer. The knowledge generated from the Human Genome Project, and subsequently The Cancer Genome Atlas, has provided the concept of precision medicine, in which cancer patients can be sub-classified based on actionable driver mutations that can be selectively targeted by molecular targeted drugs and treated by appropriate molecular targeted therapies. Development of next-generation sequencing has both dramatically advanced genomic sequencing technology and revealed actionable driver mutations for individual cancer patients when applied to a clinical setting. Clinical target sequencing by next-generation sequencing enables one to formulate treatment strategies, not only by selecting a subgroup of patients who are expected to experience more effectiveness of each drug, but also by revealing patients with drug resistance based on their actionable driver mutations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA Cancer J Clin 67:7–30

    Article  PubMed  Google Scholar 

  2. Early Breast Cancer Trialists’ Collaborative Group (EBCTCG) (2005) Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 365:1687–1717

    Article  CAS  Google Scholar 

  3. Katsnelson A (2013) Momentum grows to make ‘personalized’ medicine more ‘precise’. Nat Med 19:249

    Article  CAS  PubMed  Google Scholar 

  4. Yurkiewicz S (2010) The prospects for personalized medicine. Hastings Cent Rep 40:14–16

    Article  PubMed  Google Scholar 

  5. The White House Office of the Press Secretary (2015) Fact sheet: President Obama’s precision medicine initiative. Available via DIALOG. https://obamawhitehouse.archives.gov/the-press-office/2015/01/30/fact-sheet-president-obama-s-precision-medicine-initiative. Accessed July 2018

  6. Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372:793–795

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lowy DR, Collins FS (2016) Aiming high–changing the trajectory for cancer. N Engl J Med 374:1901–1904

    Article  PubMed  PubMed Central  Google Scholar 

  8. Mata DA, Katchi FM, Ramasamy R (2017) Precision medicine and men’s health. Am J Mens Health 11:1124–1129

    Article  PubMed  Google Scholar 

  9. Jain KK (2005) Personalised medicine for cancer: from drug development into clinical practice. Expert Opin Pharmacother 6:1463–1476

    Article  CAS  PubMed  Google Scholar 

  10. Carels N, Spinasse LB, Tilli TM et al (2016) Toward precision medicine of breast cancer. Theor Biol Med Model 13:7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Jackson SE, Chester JD (2015) Personalised cancer medicine. Int J Cancer 137:262–266

    Article  CAS  PubMed  Google Scholar 

  12. Perou CM, Sorlie T, Eisen MB et al (2000) Molecular portraits of human breast tumours. Nature 406:747–752

    Article  CAS  PubMed  Google Scholar 

  13. Perou CM, Jeffrey SS, van de Rijn M et al (1999) Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. Proc Natl Acad Sci USA 96:9212–9217

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bonadonna G, Brusamolino E, Valagussa P et al (1976) Combination chemotherapy as an adjuvant treatment in operable breast cancer. N Engl J Med 294:405–410

    Article  CAS  PubMed  Google Scholar 

  15. Bonadonna G, Moliterni A, Zambetti M et al (2005) 30 years’ follow up of randomised studies of adjuvant CMF in operable breast cancer: cohort study. BMJ 330:217

    Article  PubMed  PubMed Central  Google Scholar 

  16. Goldhirsch A, Wood WC, Coates AS et al (2011) Strategies for subtypes–dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the primary therapy of early breast cancer 2011. Ann Oncol 22:1736–1747

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Prat A, Fan C, Fernandez A et al (2015) Response and survival of breast cancer intrinsic subtypes following multi-agent neoadjuvant chemotherapy. BMC Med 13:303

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chia SK, Bramwell VH, Tu D et al (2012) A 50-gene intrinsic subtype classifier for prognosis and prediction of benefit from adjuvant tamoxifen. Clin Cancer Res 18:4465–4472

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Cheang MC, Voduc KD, Tu D et al (2012) Responsiveness of intrinsic subtypes to adjuvant anthracycline substitution in the NCIC.CTG MA.5 randomized trial. Clin Cancer Res 18:2402–2412

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Prat A, Galvan P, Jimenez B et al (2016) Prediction of response to neoadjuvant chemotherapy using core needle biopsy samples with the prosigna assay. Clin Cancer Res 22:560–566

    Article  CAS  PubMed  Google Scholar 

  21. Prat A, Ellis MJ, Perou CM (2011) Practical implications of gene-expression-based assays for breast oncologists. Nat Rev Clin Oncol 9:48–57

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Myers MB (2016) Targeted therapies with companion diagnostics in the management of breast cancer: current perspectives. Pharmgenomics Pers Med 9:7–16

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Cronin M, Sangli C, Liu ML et al (2007) Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer. Clin Chem 53:1084–1091

    Article  CAS  PubMed  Google Scholar 

  24. Paik S (2007) Development and clinical utility of a 21-gene recurrence score prognostic assay in patients with early breast cancer treated with tamoxifen. Oncologist 12:631–635

    Article  CAS  PubMed  Google Scholar 

  25. Paik S, Shak S, Tang G et al (2004) A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817–2826

    Article  CAS  PubMed  Google Scholar 

  26. Paik S, Tang G, Shak S et al (2006) Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 24:3726–3734

    Article  CAS  PubMed  Google Scholar 

  27. Harris L, Fritsche H, Mennel R et al (2007) American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J Clin Oncol 25:5287–5312

    Article  CAS  PubMed  Google Scholar 

  28. van ‘t Veer LJ, Dai H, van de Vijver MJ et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536

    Article  PubMed  Google Scholar 

  29. van de Vijver MJ, He YD, van’t Veer LJ et al (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999–2009

    Article  PubMed  Google Scholar 

  30. Parker JS, Mullins M, Cheang MC et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27:1160–1167

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  32. Dowsett M, Sestak I, Lopez-Knowles E et al (2013) Comparison of PAM50 risk of recurrence score with oncotype DX and IHC4 for predicting risk of distant recurrence after endocrine therapy. J Clin Oncol 31:2783–2790

    Article  PubMed  Google Scholar 

  33. Beahrs OH, Carr DT, Rubin P et al (1977) AJCC cancer staging manual, 1st edn. American Joint Committee, Illinois

    Google Scholar 

  34. Beahrs OH, Myers MH (1983) AJCC cancer staging manual, 2nd edn. J B Lippincott Company, Philadelphia

    Google Scholar 

  35. Beahrs OH, Henson DE, Hutter RVP et al (1988) AJCC cancer staging manual, 3rd edn. J B Lippincott Company, Philadelphia

    Google Scholar 

  36. Beahrs OH, Henson DE, Hutter RVP et al (1992) AJCC cancer staging manual, 4th edn. J B Lippincott Company, Philadelphia

    Google Scholar 

  37. Fleming ID, Cooper JS, Henson DE et al (1997) AJCC cancer staging manual, 5th edn. Lippincott - Raven, Philadelphia

    Google Scholar 

  38. Greene FL, Page DL, Fleming ID et al (2002) AJCC cancer staging manual, 6th edn. Springer, New York et al

    Book  Google Scholar 

  39. Edge SB, Byrd DR, Compton CC et al (2009) AJCC cancer staging manual, 7th edn. Springer, New York

    Google Scholar 

  40. Amin MB, Edge SB, Greene FL et al (2016) AJCC cancer staging manual, 8th edn. Springer, New York

    Google Scholar 

  41. Cardoso F, van’t Veer LJ, Bogaerts J et al (2016) 70-gene signature as an aid to treatment decisions in early-stage breast cancer. N Engl J Med 375:717–729

    Article  CAS  PubMed  Google Scholar 

  42. Su Y, Subedee A, Bloushtain-Qimron N et al (2015) Somatic cell fusions reveal extensive heterogeneity in basal-like breast cancer. Cell Rep 11:1549–1563

    Article  CAS  PubMed  Google Scholar 

  43. Lehmann BD, Bauer JA, Chen X et al (2011) Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest 121:2750–2767

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Marotti JD, de Abreu FB, Wells WA et al (2017) Triple-negative breast cancer: next-generation sequencing for target identification. Am J Pathol 187:2133–2138

    Article  CAS  PubMed  Google Scholar 

  45. Collins FS, Green ED, Guttmacher AE et al (2003) A vision for the future of genomics research. Nature 422:835–847

    Article  CAS  PubMed  Google Scholar 

  46. Adams MD, Sutton GG, Smith HO et al (2003) The independence of our genome assemblies. Proc Natl Acad Sci USA 100:3025–3026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Margulies M, Egholm M, Altman WE et al (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437:376–380

    Article  PubMed  PubMed Central  Google Scholar 

  48. Shendure J, Porreca GJ, Reppas NB et al (2005) Accurate multiplex polony sequencing of an evolved bacterial genome. Science 309:1728–1732

    Article  CAS  PubMed  Google Scholar 

  49. Varmus H, Stillman B (2005) Support for the human cancer genome project. Science 310:1615

    Article  CAS  PubMed  Google Scholar 

  50. Ramanathan R, Olex AL, Dozmorov M et al (2017) Angiopoietin pathway gene expression associated with poor breast cancer survival. Breast Cancer Res Treat 162:191–198

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kawaguchi T, Yan L, Qi Q et al (2017) Overexpression of suppressive microRNAs, miR-30a and miR-200c are associated with improved survival of breast cancer patients. Sci Rep 7:15945

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Young J, Kawaguchi T, Yan L et al (2017) Tamoxifen sensitivity-related microRNA-342 is a useful biomarker for breast cancer survival. Oncotarget 8:99978–99989

    PubMed  PubMed Central  Google Scholar 

  53. Kim SY, Kawaguchi T, Yan L et al (2017) Clinical relevance of microRNA expressions in breast cancer validated using the Cancer Genome Atlas (TCGA). Ann Surg Oncol 10:2943–2949

    Article  Google Scholar 

  54. Ellis MJ, Perou CM (2013) The genomic landscape of breast cancer as a therapeutic roadmap. Cancer Discov 3:27–34

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Gagan J, Van Allen EM (2015) Next-generation sequencing to guide cancer therapy. Genome Med 7:80

    Article  PubMed  PubMed Central  Google Scholar 

  56. Arnedos M, Vicier C, Loi S et al (2015) Precision medicine for metastatic breast cancer–limitations and solutions. Nat Rev Clin Oncol 12:693–704

    Article  CAS  PubMed  Google Scholar 

  57. Kummar S, Williams PM, Lih CJ et al (2015) Application of molecular profiling in clinical trials for advanced metastatic cancers. J Natl Cancer Inst 107

  58. Renfro LA, Sargent DJ (2016) Statistical controversies in clinical research: basket trials, umbrella trials, and other master protocols: a review and examples. Ann Oncol 28:34–43

    PubMed Central  Google Scholar 

  59. Kim ES, Herbst RS, Wistuba II et al (2011) The BATTLE trial: personalizing therapy for lung cancer. Cancer Discov 1:44–53

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Willyard C (2013) ‘Basket studies’ will hold intricate data for cancer drug approvals. Nat Med 19:655

    Article  CAS  PubMed  Google Scholar 

  61. Schmidt KT, Chau CH, Price DK et al (2016) Precision oncology medicine: the clinical relevance of patient specific biomarkers used to optimize cancer treatment. J Clin Pharmacol 56:1484–1499

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Do K, O’Sullivan Coyne G, Chen AP (2015) An overview of the NCI precision medicine trials-NCI MATCH and MPACT. Chin Clin Oncol 4:31

    PubMed  Google Scholar 

  63. Hyman DM, Solit DB, Arcila ME et al (2015) Precision medicine at Memorial Sloan Kettering Cancer Center: clinical next-generation sequencing enabling next-generation targeted therapy trials. Drug Discov Today 20:1422–1428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Lih CJ, Harrington RD, Sims DJ et al (2017) Analytical validation of the next-generation sequencing assay for a nationwide signal-finding clinical trial: Molecular Analysis for Therapy Choice clinical trial. J Mol Diagn 19:313–327

    Article  PubMed  PubMed Central  Google Scholar 

  65. Mullard A (2015) NCI-MATCH trial pushes cancer umbrella trial paradigm. Nat Rev Drug Discov 14:513–515

    Article  CAS  PubMed  Google Scholar 

  66. Brower V (2015) NCI-MATCH pairs tumor mutations with matching drugs. Nat Biotechnol 33:790–791

    Article  CAS  PubMed  Google Scholar 

  67. National Cancer Institute (2018) NCI-MATCH trial (Molecular Analysis for Therapy Choice). Available via DIALOG. https://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/nci-match. Accessed July 2018

  68. Lipson D, Capelletti M, Yelensky R et al (2012) Identification of new ALK and RET gene fusions from colorectal and lung cancer biopsies. Nat Med 18:382–384

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Redig AJ, Janne PA (2015) Basket trials and the evolution of clinical trial design in an era of genomic medicine. J Clin Oncol 33:975–977

    Article  CAS  PubMed  Google Scholar 

  70. Meric-Bernstam F, Johnson A, Holla V et al (2015) A decision support framework for genomically informed investigational cancer therapy. J Natl Cancer Inst 107

  71. Pritchard CC, Salipante SJ, Koehler K et al (2014) Validation and implementation of targeted capture and sequencing for the detection of actionable mutation, copy number variation, and gene rearrangement in clinical cancer specimens. J Mol Diagn 16:56–67

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Frampton GM, Fichtenholtz A, Otto GA et al (2013) Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnol 31:1023–1031

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Wagle N, Berger MF, Davis MJ et al (2012) High-throughput detection of actionable genomic alterations in clinical tumor samples by targeted, massively parallel sequencing. Cancer Discov 2:82–93

    Article  CAS  PubMed  Google Scholar 

  74. Nagahashi M, Wakai T, Shimada Y et al (2016) Genomic landscape of colorectal cancer in Japan: clinical implications of comprehensive genomic sequencing for precision medicine. Genome Med 8:136

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Shimada Y, Yagi R, Kameyama H et al (2017) Utility of comprehensive genomic sequencing for detecting HER2-positive colorectal cancer. Hum Pathol 66:1–9

    Article  CAS  PubMed  Google Scholar 

  76. Shimada Y, Kameyama H, Nagahashi M et al (2017) Comprehensive genomic sequencing detects important genetic differences between right-sided and left-sided colorectal cancer. Oncotarget 8:93567–93579

    Article  PubMed  PubMed Central  Google Scholar 

  77. Ichikawa H, Nagahashi M, Shimada Y et al (2017) Actionable gene-based classification toward precision medicine in gastric cancer. Genome Med 9:93

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Douillard JY, Oliner KS, Siena S et al (2013) Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer. N Engl J Med 369:1023–1034

    Article  CAS  PubMed  Google Scholar 

  79. Collins DC, Sundar R, Lim JS et al (2017) Towards precision medicine in the clinic: from biomarker discovery to novel therapeutics. Trends Pharmacol Sci 38:25–40

    Article  CAS  PubMed  Google Scholar 

  80. Bertotti A, Papp E, Jones S et al (2015) The genomic landscape of response to EGFR blockade in colorectal cancer. Nature 526:263–267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Lavaud P, Andre F (2014) Strategies to overcome trastuzumab resistance in HER2-overexpressing breast cancers: focus on new data from clinical trials. BMC Med 12:132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. de Oliveira Taveira M, Nabavi S, Wang Y et al (2017) Genomic characteristics of trastuzumab-resistant Her2-positive metastatic breast cancer. J Cancer Res Clin Oncol 143:1255–1262

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Andre F, Hurvitz S, Fasolo A et al (2016) Molecular alterations and everolimus efficacy in human epidermal growth factor receptor 2-overexpressing metastatic breast cancers: combined exploratory biomarker analysis from BOLERO-1 and BOLERO-3. J Clin Oncol 34:2115–2124

    Article  PubMed  Google Scholar 

  84. Goel S, Wang Q, Watt AC et al (2016) Overcoming therapeutic resistance in HER2-positive breast cancers with CDK4/6 inhibitors. Cancer Cell 29:255–269

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Malumbres M (2016) CDK4/6 Inhibitors resTORe therapeutic sensitivity in HER2+ breast cancer. Cancer Cell 29:243–244

    Article  CAS  PubMed  Google Scholar 

  86. Tan O, Shrestha R, Cunich M et al (2018) Application of next-generation sequencing to improve cancer management: a review of the clinical effectiveness and cost-effectiveness. Clin Genet 93:533–544

    Article  CAS  PubMed  Google Scholar 

  87. Li Y, Bare LA, Bender RA et al (2015) Cost effectiveness of sequencing 34 cancer-associated genes as an aid for treatment selection in patients with metastatic melanoma. Mol Diagn Ther 19:169–177

    Article  PubMed  PubMed Central  Google Scholar 

  88. Norum J, Hagen AI, Maehle L et al (2008) Prophylactic bilateral salpingo-oophorectomy (PBSO) with or without prophylactic bilateral mastectomy (PBM) or no intervention in BRCA1 mutation carriers: a cost-effectiveness analysis. Eur J Cancer 44:963–971

    Article  CAS  PubMed  Google Scholar 

  89. Muller D, Danner M, Rhiem K et al (2018) Cost-effectiveness of different strategies to prevent breast and ovarian cancer in German women with a BRCA 1 or 2 mutation. Eur J Health Econ 19:341–353

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This project was supported by funding from Denka Co., Ltd. This work was supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research Grant Numbers 16K19888 for J.T., 18K19576 for M.N., and 16K15610 for T.W. M.N. was also supported by Takeda Science Foundation. K.T. was supported by NIH/NCI grant R01CA160688 and Susan G. Komen Investigator Initiated Research Grant IIR12222224. J.T. and M.N. were supported by Tohoku Cancer Professional Training Promotion Plan.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Masayuki Nagahashi or Kazuaki Takabe.

Ethics declarations

Conflict of interest

The authors declare no potential conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tsuchida, J., Rothman, J., McDonald, KA. et al. Clinical target sequencing for precision medicine of breast cancer. Int J Clin Oncol 24, 131–140 (2019). https://doi.org/10.1007/s10147-018-1373-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10147-018-1373-5

Keywords

Navigation