Skip to main content
Log in

Strategies to Incorporate Translational Research Science into Clinical Trials in Breast Cancer

  • Published:
Current Breast Cancer Reports Aims and scope Submit manuscript

Abstract

The advent of molecular oncology, accompanied by a whole new range of high-throughput experimental methods and their related technologies, has revealed that breast cancer is a complex disease and has led researchers to realize that the current approach to its treatment needs to be restructured. With clinical trials being the medium through which new knowledge generated by basic research can be applied to the management of cancer patients, efforts must be made to integrate translational research into clinical trial design and conduct. This article reports on different ways translational research can contribute to improving the structure and effectiveness of clinical trials, with the main aim to rapidly optimize and individualize the treatment of breast cancer patients.

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

Access this article

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

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

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. • Stratton MR, Campbell PJ, Futreal PA: The cancer genome. Nature 2009, 458:719–724. In this article, the authors review the principles of our current understanding of cancer genomes and provide an overview of the insights that will be generated by the imminent explosion of information about cancer genomes.

    Article  CAS  PubMed  Google Scholar 

  2. Korkaya H, Wicha MS: Cancer stem cells: nature versus nurture. Nat Cell Biol 2010, 12:419–421.

    Article  CAS  PubMed  Google Scholar 

  3. Kolata G: Forty years’ war: advances elusive in the drive to cure cancer. New York Times. April 24, 2009.

  4. Chabner BA, Boral AL, Multani P: Translational research: walking the bridge between idea and cure--seventeenth Bruce F. Cain Memorial Award lecture. Cancer Res 1998, 58:4211–4216.

    CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  6. Sorlie T, Perou CM, Tibshirani R, et al. : Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci 2001, 10869–10874.

  7. Sorlie T, Tibshirani R, Parker J, et al.: Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci 2003, 8418–8423.

  8. Sotiriou C, Neo SY, McShane LM, et al.: Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci 2003, 10393–10398.

  9. Rouzier R, Perou CM, Symmans WF, et al.: Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res 2005, 11: 5678–5685.

    Article  CAS  PubMed  Google Scholar 

  10. Betensky RA, Louis DN, Cairncross JG: Influence of unrecognized molecular heterogeneity on randomized clinical trials. J Clin Oncol 2002, 20:2495–2499.

    Article  PubMed  Google Scholar 

  11. Slamon DJ, Leyland-Jones B, Shak S, et al.: Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N Engl J Med 2001, 344:783–792.

    Article  CAS  PubMed  Google Scholar 

  12. Vogel CL, Cobleigh MA, Tripathy D, et al.: Efficacy and safety of trastuzumab as a single agent in first-line treatment of HER2-overexpressing metastatic breast cancer. J Clin Oncol 2002, 20:719–726.

    Article  CAS  PubMed  Google Scholar 

  13. Cobleigh MA, Vogel CL, Tripathy D, et al.: Multinational study of the efficacy and safety of humanized anti-HER2 monoclonal antibody in women who have HER2-overexpressing metastatic breast cancer that has progressed after chemotherapy for metastatic disease. J Clin Oncol 1999, 17:2639–2648.

    CAS  PubMed  Google Scholar 

  14. Romond EH, Perez EA, Bryant J, et al. Trastuzumab plus adjuvant chemotherapy for operable HER2-positive breast cancer. N Engl J Med 2005, 353:1673–1684.

    Article  CAS  PubMed  Google Scholar 

  15. Piccart-Gebhart MJ, Procter M, Leyland-Jones B, et al.: Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med 2005, 353:1659–1672.

    Article  CAS  PubMed  Google Scholar 

  16. Slamon D, Eiermann W, Robert N, et al.: Phase III randomized trial comparing doxorubicin and cyclophosphamide followed by docetaxel (AC → T) with doxorubicin and cyclophosphamide followed by docetaxel and trastuzumab (AC → TH) with docetaxel, carboplatin and trastuzumab (TCH) in HER2 positive early breast cancer patients: BCIRG 006 study. Breast Cancer Res Treat 2005, 94(Suppl 1):S5.

    Google Scholar 

  17. Joensuu H, Kellokumpu-Lehtinen PL, Bono P, et al.: Adjuvant docetaxel or vinorelbine with or without trastuzumab for breast cancer. N Engl J Med 2006, 354:809–820.

    Article  CAS  PubMed  Google Scholar 

  18. Sorlie T, Tibshirani R, Parker J, et al.: Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci U S A 2003, 100:8418–8423.

    Article  CAS  PubMed  Google Scholar 

  19. Hu Z, Fan C, Oh DS, et al.: The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 2006; 27:96.

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  21. • Weigelt B, Mackay A, Ahern R, et al.: Breast cancer molecular profiling with single sample predictors: a retrospective analysis. Lancet Oncol 2010, 11:339–349. The aims of this article are to assess the clinical usefulness of the currently available single sample predictors (SSPs) by establishing agreement between different methods of breast cancer molecular subtype assignment and to ascertain whether each SSP identifies molecular subtypes with similar associations with outcome.

    Article  CAS  PubMed  Google Scholar 

  22. van't Veer LJ, Dai H, van de Vijver MJ, et al.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415:530–536.

    Google Scholar 

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

    Article  PubMed  Google Scholar 

  24. Bueno-de-Mesquita JM, van Harten WH, Retel VP, et al.: Use of 70-gene signature to predict prognosis of patients with node-negative breast cancer: a prospective community-based feasibility study (RASTER). Lancet Oncol 2007, 8:1079–1087.

    Article  CAS  PubMed  Google Scholar 

  25. Buyse M, Loi S, van’t Veer L, et al.: Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 2006, 98:1183–1192.

    Article  CAS  PubMed  Google Scholar 

  26. • Mook S, Schmidt MK, Viale G, et al.: The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1-3 positive lymph nodes in an independent validation study. Breast Cancer Res Treat 2008, 116:295–302. The authors show that the 70-gene prognosis signature, which has been demonstrated to be a valid prognostic tool in node-negative breast cancer, also outperforms traditional prognostic factors in predicting disease outcome in patients with 1 to 3 positive nodes. Moreover, they show that the signature can accurately identify patients with an excellent disease outcome in node-positive breast cancer who may be safely spared adjuvant chemotherapy.

    Article  PubMed  Google Scholar 

  27. Goldhirsch A, Ingle JN, Gelber RD, et al., Panel members: Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the primary therapy of early breast cancer 2009. Ann Oncol 2009, 20:1319–1329.

    Article  CAS  PubMed  Google Scholar 

  28. Liu ET: Mechanism-derived gene expression signatures and predictive biomarkers in clinical oncology. Proc Natl Acad Sci U S A 2005, 102:3531–3532.

    Article  CAS  PubMed  Google Scholar 

  29. Cardoso F, van't Veer L, Rutgers E, et al.: Clinical application of the 70-gene profile: the MINDACT trial. J Clin Oncol 2008, 26:729–735.

    Article  PubMed  Google Scholar 

  30. Slodkowska EA, Ross JS: MammaPrint 70-gene signature: another milestone in personalized medical care for breast cancer patients. Expert Rev Mol Diagn 2009, 9:417–422.

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  32. Habel LA, Shak S, Jacobs MK, et al.: A population based study of tumor gene expression and risk of breast cancer death among lymph node-negative patients. Breast Cancer Res 2006, 8:R25.

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  34. • Goldstein LJ, Gray R, Badve S, et al.: Prognostic utility of the 21-gene assay in hormone receptor-positive operable breast cancer compared with classical clinicopathologic features. J Clin Oncol 2008, 26:4063–4071. The authors show that the recurrence score is a more accurate predictor of relapse than standard clinical features for individual patients with hormone receptor–positive operable breast cancer treated with chemohormonal therapy. They also provides information that is complementary to features typically used in anatomic staging, such as tumor size and lymph node involvement.

    Article  PubMed  Google Scholar 

  35. National Comprehensive Cancer Network: NCCN Clinical Practice Guidelines in Oncology. Available at http://www.nccn.org/professionals/physician_gls/f_guidelines.asp. Accessed July 14, 2010.

  36. Horlings HM, Lai C, Nuyten DS, et al.: Integration of DNA copy number alterations and prognostic gene expression signatures in breast cancer patients. Clin Cancer Res 2010, 16:651–663. The authors demonstrate there is a strong correlation between gene expression signatures and underlying genomic changes, showing a link between genomic changes and gene expression signatures and enabling a better understanding of the tumor biology that causes poor prognosis.

    Article  CAS  PubMed  Google Scholar 

  37. Komaki K, Sano N, Tangoku A: Problems in histological grading of malignancy and its clinical significance in patients with operable breast cancer. Breast Cancer 2006, 13:249–253.

    Article  PubMed  Google Scholar 

  38. Sotiriou C, Wirapati P, Loi S, et al.: Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 2006, 98:262–272.

    Article  CAS  PubMed  Google Scholar 

  39. Loi S, Haibe-Kains B, Desmedt C, et al.: Definition of clinically distinct molecular subtypes in oestrogen receptor-positive breast carcinomas through genomic grade. J Clin Oncol 2007, 25:1239–1246. [Erratum in: J Clin Oncol 2007, 25:3790.]

    Google Scholar 

  40. Galea MH, Blarney RW, Elston CE, et al.: The Nottingham Prognostic Index in primary breast cancer. Breast Cancer Res Treat 1992, 22:207–219.

    Article  CAS  PubMed  Google Scholar 

  41. Kola I, Landis J: Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 2004, 3:711–715.

    Article  CAS  PubMed  Google Scholar 

  42. DiMasi JA, Grabowski HG: Economics of new oncology drug development. J Clin Oncol 2007, 25:209–216.

    Article  PubMed  Google Scholar 

  43. Ratain MJ, Sargent DJ: Optimising the design of phase II oncology trials: the importance of randomisation. Eur J Cancer 2009, 45:275–280.

    Article  PubMed  Google Scholar 

  44. • Di Cosimo S, Baselga J: Management of breast cancer with targeted agents: importance of heterogeneity. Nat Rev Clin Oncol 2010, 7:139–147. [Erratum in: Nat Rev Clin Oncol 2010, 7:184.] In this review, the authors describe how the overall management of a complex disease such as breast cancer will increasingly require an understanding of its heterogeneity, the biological nature of any given tumor, as well the existence of increased personalized treatment options.

  45. Pusztai L: Limitations of pharmacogenomic predictor discovery in Phase II clinical trials. Pharmacogenomics 2007, 8:1443–1448.

    Article  CAS  PubMed  Google Scholar 

  46. Mauri D, Pavlidis N, Ioannidis JP: Neoadjuvant versus adjuvant systemic treatment in breast cancer: a meta-analysis. J Natl Cancer Inst 2005, 97:188–194.

    Article  PubMed  Google Scholar 

  47. Wolmark N, Wang J, Mamounas E, et al.: Preoperative chemotherapy in patients with operable breast cancer: Nine-years results from National Surgical Adjuvant Breast and Bowel Project B-18. J Natl Cancer Inst Monogr 2001, 96–102.

  48. Breast International Group: Available at http://www.breastinternationalgroup.org/. Accessed July 15, 2010.

  49. Dowsett M, Ebbs SR, Dixon JM, et al.: Biomarkers changes during neoadjuvant anastrozole, tamoxifen or the combination: influence of hormonal status and HER-2 in breast cancer- a study from the IMPACT trialists. J Clin Oncol 2005, 23:2477–2492.

    Article  CAS  PubMed  Google Scholar 

Download references

Disclosure

Christos Sotiriou and Martine Piccart are named inventors on a patent application for the Gene expression Grade Index (GGI). Debora Fumagalli and Christine Desmedt report no potential conflicts of interest relevant to this article.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christos Sotiriou.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fumagalli, D., Desmedt, C., Piccart, M. et al. Strategies to Incorporate Translational Research Science into Clinical Trials in Breast Cancer. Curr Breast Cancer Rep 2, 208–213 (2010). https://doi.org/10.1007/s12609-010-0028-y

Download citation

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1007/s12609-010-0028-y

Keywords