New Tools for Assessing Breast Cancer Recurrence

  • Phuong Dinh
  • Fatima Cardoso
  • Christos Sotiriou
  • Martine J. Piccart-Gebhart
Part of the Cancer Treatment and Research book series (CTAR, volume 141)

Breast cancer is the most common cancer in women in the Western world, and is essentially incurable when distant metastases are detected. Despite an increasing incidence, breast cancer mortality has fallen, largely due to the advent of widespread screening programs, but also partly due to the increasing use of adjuvant systemic treatment and advances in loco-regional control.

This chapter will review the advances in gene expression profiling, made possible with microarray technology, as new tools for assessing breast cancer recurrence. It will discuss the molecular classification of breast cancer subtypes, as well as the various molecular signatures with their prognostic and predictive implications. Two prospective randomized trials, MINDACT and TAILORx, designed to validate this new technology, will be briefly discussed.


Breast Cancer Estrogen Receptor National Comprehensive Cancer Network National Comprehensive Cancer Network Gene Expression Signature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Peto, R., J. Boreham, M. Clark et al. 2000. UK and USA breast cancer deaths down 25% in year 2000 at ages 20–69 years. Lancet 355: 1822.PubMedCrossRefGoogle Scholar
  2. 2.
    Early Breast Cancer Trialists’ Collaborative Group: 2005. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: An overview of the randomised trials. Lancet 265: 1687–1717.Google Scholar
  3. 3.
    National Comprehensive Cancer Network (NCCN) guidelines. V2.2007.
  4. 4.
    Goldhirsh, A., J. H. Glick, R. D. Gelber al. 2005. Meeting highlights: international expert consensus on the primary therapy of early breast cancer. Ann Oncol 16(10): 1569–83.CrossRefGoogle Scholar
  5. 5.
    Perou, C. M., T. Sorlie, M. B. Eisen et al. 2000. Molecular portraits of human breast tumors. Nature 406: 747–52.PubMedCrossRefGoogle Scholar
  6. 6.
    Sorlie, T., C. M. Perou, R. Tibshirani et al. 2001. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98: 10869–74.PubMedCrossRefGoogle Scholar
  7. 7.
    Carey, L. A., C. M. Perou, L. G. Dressler et al. 2004. Race and the poor prognosis basal-like breast cancer (BBC) phenotype in the population-based Carolina Breast Cancer Study. J Clin Oncol suppl: abstract 9510.Google Scholar
  8. 8.
    Rouzier, R., K. Anderson, K. R. Hess et al. 2004. Basal and luminal types of breast cancer defined by gene expression patterns respond differently to neoadjuvant chemotherapy. San Antonio Breast Cancer Symposium. San Antonio, TX abstract 1026.Google Scholar
  9. 9.
    Sotiriou, C., S. Y. Neo, L. M. McShane et al. 2003. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 100 (18): 10393–8.PubMedCrossRefGoogle Scholar
  10. 10.
    Pusztai, L., C. Mazouni, K. Anderson et al. 2006. Molecular classification of breast cancer: limitations and potential. Oncologist 11: 868–77.PubMedCrossRefGoogle Scholar
  11. 11.
    Gelber, R. D., M. Bonetti, M. Castiglione-Gertsch et al. 2003. Tailoring adjuvant treatments for the individual breast cancer patient. The Breast 12: 558–68.PubMedCrossRefGoogle Scholar
  12. 12.
    van’t Veer, L. J., H. Dai, M. J. van de Vijver et al. 2002. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871): 530–6.CrossRefGoogle Scholar
  13. 13.
    van de Vijver, M. J., Y. D. He, L. J. van’t Veer et al. 2002. A gene-expression signature as a predictor of survival in breast cancer. Nature 347(25): 1999–2009.Google Scholar
  14. 14.
    Eifel, P., J. A. Axelson, J. Costa et al. 2001. National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1–3, 2000. J Natl Cancer Inst. 93(13): 979–89.PubMedCrossRefGoogle Scholar
  15. 15.
    Wang, Y., J. G. Klijn, Y. Zhang et al. 2005. Gene-expression profiles to predict distant metastases of lymph-node-negative primary breast cancer. Lancet 365(9460): 671–9.PubMedGoogle Scholar
  16. 16.
    Foekins, J. A., D. Atkins, Y. Zhang et al. 2006. Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. J Clin Oncol 24(11): 1665–71.CrossRefGoogle Scholar
  17. 17.
    Buyse, M., S. Loi, L. van’t Veer et al. 2006. Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. J Natl Cancer Inst 98(17):1183–92.PubMedCrossRefGoogle Scholar
  18. 18.
    Desmedt, C., F. Piette, S. Loi et al. Strong time-dependency of the 76-gene prognostic signature for node-negative breast cancer patients in the TRANSBIG multi-centre independent validation series. Late breaking abstract, Fifth European Breast Cancer Conference (2006).Google Scholar
  19. 19.
    Schmidt-Kittler, O., T. Ragg, A. Daskalakis et al. 2003. From latent disseminated cells to overt metastases: genetic analysis of systemic breast cancer progression. Proc Natl Acad Sci USA 100(13); 7737–42.PubMedCrossRefGoogle Scholar
  20. 20.
    Paik, S., S. Shak, G. Tan et al. 2004. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351: 2817–26.PubMedCrossRefGoogle Scholar
  21. 21.
    Esteban, J., J. Baker, M. Cronin et al. 2003. Tumor gene expression and prognosis in breast cancer: multi-gene RT-PCR assay of paraffin-embedded tissue (abstract). Prog Proc Am Soc Clin Oncol 22: 850.Google Scholar
  22. 22.
    Cobleigh, M. A., P. Bitterman, J. Baker et al. 2003. Tumor gene expression predicts distant disease-free survival (DDFS) in breast cancer patients with 10 or more positive nodes: high throughout RT-PCR assay of paraffin-embedded tumor tissues (abstract). Prog Proc Am Soc Clin Oncol 22: 850.Google Scholar
  23. 23.
    Paik, S., S. Shak, G. Tang et al. 2005. Multi-gene RT-PCR assay for predicting recurrence in node-negative breast cancer patients – NSABP studies B-20 and B-14. Breast Cancer Research and Treatment 2003; 82( Suppl 1): S10 abstract 16.Google Scholar
  24. 24.
    Cardoso F. Show me the genes- I will tell you who / how to treat! Breast Cancer Res 7: 77–9.Google Scholar
  25. 25.
    Habel, L. A., C. P. Quesenberry, K. Jacobs et al. 2003. A large case-control study of gene expression and breast cancer death in Northern California Kaiser Permanent population. Breast Cancer Research and Treatment 88 ( Suppl 1): S118 abstract 3019.Google Scholar
  26. 26.
    Chang, H. Y., J. B. Sneddon, A. A. Alizadeh et al. 2004. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol. 2(2): E7.PubMedCrossRefGoogle Scholar
  27. 27.
    Chang, H. Y., D. S. A. Nuyten, J. B. Sneddon et al. 2005. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc Natl Acad Sci USA 102; 3738–43.PubMedCrossRefGoogle Scholar
  28. 28.
    Sotiriou, C., P. Wirapati, S. Loi et al. 2006. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst. 98(4): 262–72.PubMedGoogle Scholar
  29. 29.
    Sotiriou, C., P. Wirapati, S. Loi et al. 2005. Better characterization of estrogen receptor (ER) positive luminal subtypes using genomic grade. General Session 6, San Antonio Breast Cancer Symposium Dec 8–11, 2005, San Antonio, Texas.Google Scholar
  30. 30.
    Miller, L. D. et al. 2005. An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci USA 102: 13550–55.PubMedCrossRefGoogle Scholar
  31. 31.
    Reya, T., Morrison SJ, Clarke MF et al. Stem cells, cancer, and cancer stem cells. Nature 2001; 414: 105–11.PubMedCrossRefGoogle Scholar
  32. 32.
    Al-Hajj, M., M. S. Wicha, A. Benito-Hernandez, et al. 2003. Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci USA 100: 3983–8.PubMedCrossRefGoogle Scholar
  33. 33.
    Liu, R., X. Wang, G. Chen et al. 2007. The prognostic role of a gene signature from tumorigenic breast-cancer cells. N Engl J Med 356: 217–26.PubMedCrossRefGoogle Scholar
  34. 34.
    Kang, Y., P. M. Siegel, W. Shu et al. 2003. A multigenic program mediating breast cancer metastasis to bone. Cancer Cell. 3: 537–49.PubMedCrossRefGoogle Scholar
  35. 35.
    Minn, A. J., Y. Kang, I. Serganova et al. 2005. Distinct organ-specific metastatic potential of individual breast cancer cells and primary tumours. J Clin Invest. 115: 44–55.PubMedGoogle Scholar
  36. 36.
    Minn, A. J., G. P. Gupta, P. M. Siegel et al. 2005. Genes that mediate breast cancer metastasis to lung. Nature 436; 518–24.PubMedCrossRefGoogle Scholar
  37. 37.
    Gianni, L., M. Zambetti, K. Clark et al. 2005. Gene expression profiles in paraffin-embedded core biopsy tissue predict response to chemotherapy in women with locally advanced breast cancer. J Clin Oncol 23: 7265–77.PubMedCrossRefGoogle Scholar
  38. 38.
    Hess, K. R., K. Anderson, W. F. Symmans et al. 2006. Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. J Clin Oncol 24: 4236–44.PubMedCrossRefGoogle Scholar
  39. 39.
    Potti, A., H. K. Dressman, A. Bild et al. 2006. Genomic signatures to guide the use of chemotherapeutics. Nat Med 12(11): 1294–1300.PubMedCrossRefGoogle Scholar
  40. 40.
    Jansen, M. P., J. Foekens et al. 2005. Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling. J Clin Oncol 23: 732–40.PubMedCrossRefGoogle Scholar
  41. 41.
    Ma, X. J., Z. Wang, P. Ryan et al. 2004. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5: 607–16.PubMedCrossRefGoogle Scholar
  42. 42.
    Sotiriou, C., M. Paesmans, A. Harris., et al. 2004. Cyclin E1 (CCNE1) and E2 (CCNE2) as prognostic and predictive markers for endocrine therapy (ET) in early breast cancer. Proc Am Soc Clin Oncol 23: 831 (abstract 9504).Google Scholar
  43. 43.
    Bogaerts, J., F. Cardosa, M. Buyse et al. 2006. Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial. Nature Clinical Practice – Oncology 3(10): 540–51.PubMedCrossRefGoogle Scholar
  44. 44.
    Michielis, S., S. Koscielny, C. Hill. 2005. Prediction of cancer outcome with microarrays: A multiple random validation strategy. Lancet 365: 488–92.CrossRefGoogle Scholar
  45. 45.
    Ein-Dor, L., I. Kela, G. Getz et al. 2005. Outcome signature genes in breast cancer: is there a unique set? Bioinformatics 21: 171–8.PubMedCrossRefGoogle Scholar
  46. 46.
    Stec, J., J. Wang, K. Coombes et al. 2005. Comparison of the predictive accuracy of DNA array-based multigene classifiers across cDNA arrays and Affymetrix GeneChips. J Mol Diagn 7: 357–67.PubMedGoogle Scholar
  47. 47.
    Cronin, M., M. Pho, D. Dutta et al. 2004. Measurement of gene expression in archival paraffin-embedded tissues: development and performance of a 92-gene reverse transcriptase-polymerase chain reaction assay. Am J Pathol 164(1): 35–42.PubMedGoogle Scholar
  48. 48.
    Loi, S., C. Desmedt, F. Cardoso et al. 2005. Breast cancer gene expression profiling: clinical trial and practice implications. Pharmacogenomics. 6(1): 49–58.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Phuong Dinh
    • 1
  • Fatima Cardoso
    • 1
  • Christos Sotiriou
    • 1
  • Martine J. Piccart-Gebhart
    • 1
  1. 1.Department of Medical OncologyUniversite Libre de BruxellesBelgium

Personalised recommendations