Review Article | Published:

Molecular classification of breast cancer: implications for selection of adjuvant chemotherapy

Nature Clinical Practice Oncology volume 3, pages 621632 (2006) | Download Citation

Subjects

Abstract

Adjuvant chemotherapy improves survival of patients with stage I–III breast cancer but it is being increasingly recognized that the benefit is not equal for all patients. Molecular characteristics of the cancer affect sensitivity to chemotherapy. In general, estrogen-receptor-negative disease is more sensitive to chemotherapy than estrogren-receptor-positive disease. Large-scale genomic analyses of breast cancer suggest that further molecular subsets may exist within the categories defined by hormone receptor status. It is hoped that the new molecular classification schemes might improve patient selection for therapy. Before any new molecular classification (or predictive test) is adopted for routine clinical use, however, several criteria need to be met. There must be an agreed and reproducible method by which to assign molecular class to a new case. Cancers that belong to different molecular classes must show differences in disease outcome and treatment efficacy that affect management and treatment selection. Also desirable are results from prospective clinical trials that demonstrate improved patient outcome when the new test is used in decision-making, compared with the current standard of care. This Review describes the current limitations and future promises of gene-expression-based molecular classification of breast cancer and how it might impact on selection of adjuvant therapy for individual patients.

Key points

  • Gene expression profiling of breast cancer has revealed large-scale molecular differences between ER-positive, ER-negative and HER2-amplified cancers

  • It is more appropriate to think of breast cancer as at least two to three distinct diseases than as a single disease with heterogeneous ER and HER expression

  • Molecular classification of breast cancer provides a new framework for the study of breast cancer, but how many robust molecular subtypes exist and how best to assign a molecular class to new cases is currently unknown; standard methods for molecular class determination are needed

  • Multigene signatures can be used to help guide therapy and predict prognosis and response to preoperative chemotherapy

  • The extent to which multigene signatures improve patient outcome compared with current clinicopathologic variable-based predictions is yet to be determined in prospective clinical trials

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    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

  2. 2.

    et al. (2005) Secondary leukemia after epirubicin-based adjuvant chemotherapy in operable breast cancer patients: 16 years experience of the French Adjuvant Study Group. Ann Oncol 16: 1343–1351

  3. 3.

    et al. (2005) Chemotherapy and cardiotoxicity in older breast cancer patients: a population-based study. J Clin Oncol 23: 8597–8605

  4. 4.

    et al. (2005) Selection of adjuvant chemotherapy for treatment of node-positive breast cancer. Lancet Oncol 6: 886–898

  5. 5.

    et al. (2004) Systematic review of taxane-containing versus non-taxane-containing regimens for adjuvant and neoadjuvant treatment of early breast cancer. Lancet Oncol 5: 372–380

  6. 6.

    et al. (2001) 2000 Update of recommendations for the use of tumor markers in breast and colorectal cancer: clinical practice guidelines of the American Society of Clinical Oncology. J Clin Oncol 19: 1865–1878

  7. 7.

    et al. (2001) Prognostic factors in breast cancer: the predictive value of the Nottingham Prognostic Index in patients with a long-term follow-up that were treated in a single institution. Eur J Cancer 37: 591–596

  8. 8.

    et al. (2005) Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol 23: 2716–2725

  9. 9.

    et al. (2005) Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer. J Clin Oncol 23: 8331–8339

  10. 10.

    et al. (2006) Estrogen-receptor status and outcomes of modern chemotherapy for patients with node-positive breast cancer. JAMA 295: 1658–1667

  11. 11.

    et al. (2006) Prognostic value of pathologic complete response after primary chemotherapy in relation to hormone receptor status and other factors. J Clin Oncol 24: 1037–1044

  12. 12.

    et al. (2003) The effect on tumor response of adding sequential preoperative docetaxel to preoperative doxorubicin and cyclophosphamide: preliminary results from National Surgical Adjuvant Breast and Bowel Project Protocol B-27. J Clin Oncol 21: 4165–4174

  13. 13.

    et al. (2003) Gene expression profiles obtained from single passage fine needle aspirations (FNA) of breast cancer reliably identify prognostic/predictive markers such as estrogen (ER) and HER-2 receptor status and reveal large scale molecular differences between ER-negative and ER-positive tumors. Clin Cancer Res 9: 2406–2415

  14. 14.

    et al. (2001) Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res 61: 5979–5984

  15. 15.

    et al. (2000) Reliability of immunohistochemical demonstration of oestrogen receptors in routine practice: interlaboratory variance in the sensitivity of detection and evaluation of scoring systems. J Clin Pathol 53: 125–130

  16. 16.

    et al. (2000) Anastrozole versus tamoxifen as first-line therapy for advanced breast cancer in 668 postmenopausal women: results of the Tamoxifen or Arimidex Randomized Group Efficacy and Tolerability Study. J Clin Oncol 18: 3748–3757

  17. 17.

    et al. (2001) Superior efficacy of letrozole versus tamoxifen as first-line therapy for postmenopausal women with advanced breast cancer: results of a phase III study of the International Letrozole Breast Cancer Group. J Clin Oncol 19: 2596–2606

  18. 18.

    et al. (2001) Estrogen receptor analysis in primary breast tumors by ligand-binding assay, immunocytochemical assay, and northern blot: a comparison. Breast Cancer Res Treat 67: 263–271

  19. 19.

    et al. (2004) A multi gene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351: 2817–2826

  20. 20.

    et al. (2005) Gene expression and breast cancer mortality in Northern California Kaiser Permanente patients: a large population-based case control study [abstract]. Proc Am Soc Clin Oncol 24: 603a

  21. 21.

    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

  22. 22.

    et al. (2004) A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer Cell 5: 607–616

  23. 23.

    et al. (2005) Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling. J Clin Oncol 23: 732–740

  24. 24.

    et al. (2005) Measurements of estrogen receptor and reporter genes from micro arrays determine receptor status and time to recurrence following adjuvant tamoxifen therapy [abstract]. Breast Cancer Res Treat 94 (Suppl 1): S308a

  25. 25.

    et al. (2005) Prediction of early relapses on tamoxifen in early-stage breast cancer (BC): a potential tool for adjuvant aromatase inhibitor (AI) tailoring [abstract #509]. Proc Am Soc Clin Oncol

  26. 26.

    et al. (2002) A paradigm for class prediction using gene expression profiles. J Comput Biol 9: 505–511

  27. 27.

    et al. (2000) Molecular portraits of human breast tumours. Nature 406: 747–752

  28. 28.

    et al. (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98: 10869–10874

  29. 29.

    et al. (2003) Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA 100: 8418–8423

  30. 30.

    et al. (2006) The molecular portraits of breast tumors are conserved across microarray platfroms. BMC Genomics 7: 96

  31. 31.

    et al. (2003) Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 100: 10393–10398

  32. 32.

    et al. (2003) Gene expression profiles obtained from single passage fine needle aspirations (FNA) of breast cancer reliably identify prognostic/predictive markers such as estrogen (ER) and HER-2 receptor status and reveal large scale molecular differences between ER-negative and ER-positive tumors. Clin Cancer Res 9: 2406–2415

  33. 33.

    et al. (2005) Breast cancer molecular subtypes respond differently to preoperative chemotherapy. Clin Cancer Res 11: 5678–5685

  34. 34.

    et al. (2002) Methods for assessing reproducibility of clustering patterns observed in analyses of microarray data. Bioinformatics 18: 1462–1469

  35. 35.

    and (1985) An examination of procedures for determining the number of clusters in a data set. Psychometrika 50: 159–179

  36. 36.

    et al. (2001) Estimating the number of clusters in a data set via the gap statistic. J R Stat Soc B 63: 411–423

  37. 37.

    et al. (2004) Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma. Clin Cancer Res 10: 5367–5374

  38. 38.

    et al. (2005) High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses. Int J Cancer 116: 340–350

  39. 39.

    et al. (2002) Expression of cytokeratins 17 and 5 identifies a group of breast carcinomas with poor clinical outcome. Am J Pathol 161: 1991–1996

  40. 40.

    et al. (2002) A paradigm for class prediction using gene expression profiles. J Comput Biol 9: 505–511

  41. 41.

    et al. (2001) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415: 530–536

  42. 42.

    et al. (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347: 1999–2009

  43. 43.

    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: 1183–1192

  44. 44.

    et al. (2005) Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 365: 671–679

  45. 45.

    et al. (2006) Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. J Clin Oncol 24: 1665–1671

  46. 46.

    et al. (2003) Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet 362: 362–369

  47. 47.

    et al. (2005) Prediction of docetaxel response in human breast cancer by gene expression profiling. J Clin Oncol 23: 422–431

  48. 48.

    et al. (2006) Pharmacogenomic predictor of sensitivity to preoperative paclitaxel and 5-fluorouracil, doxorubicin, cyclophosphamide chemotherapy in breast cancer. J Clin Oncol 24: 4236–4244

  49. 49.

    et al. (2004) Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. J Clin Oncol 22: 2284–2293

  50. 50.

    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: 7265–7277

  51. 51.

    et al. (2004) Prediction of the therapeutic response to paclitaxel by gene expression profiling in neoadjuvant chemotherapy for breast cancer. 40th Annual ASCO Meeting Proceeding [abstract #500]. J Clin Oncol 22 (Suppl): a14S

  52. 52.

    et al. (2005) Gene expression profile associated with response to doxorubicin-based therapy in breast cancer. Clin Cancer Res 11: 7434–7443

  53. 53.

    et al. (2005) Predictors of primary breast cancers responsiveness to preoperative epirubicin/cyclophosphamide-based chemotherapy: translation of microarray data into clinically useful predictive signatures. J Translational Med 3: 32

  54. 54.

    et al. (2004) Comparative value of tumour grade, hormonal receptors, Ki-67, HER-2 and topoisomerase II alpha status as predictive markers in breast cancer patients treated with neoadjuvant anthracycline-based chemotherapy. Eur J Cancer 40: 205–211

  55. 55.

    et al. (2002) HER-2 amplification and topoisomerase IIalpha gene aberrations as predictive markers in node-positive breast cancer patients randomly treated either with an anthracycline-based therapy or with cyclophosphamide, methotrexate, and 5-fluorouracil. Clin Cancer Res 8: 1107–1116

  56. 56.

    et al. (2005) Topoisomerase II-alpha gene amplification as a predictor of responsiveness to anthracycline-containing chemotherapy in the Breast Cancer International Research Group 006 clinical trial of trastuzumab (Herceptin) in the adjuvant setting [abstract #1045]. Breast Cancer Res Treat 94 (Suppl 1): S32

  57. 57.

    et al. (2003) p53-deficient cells display increased sensitivity to anthracyclines after loss of the catalytic subunit of the DNA-dependent protein kinase. Int J Oncol 23: 1431–1437

  58. 58.

    et al. (2001) Influence of TP53 gene alterations and c-erbB-2 expression on the response to treatment with doxorubicin in locally advanced breast cancer. Cancer Res 61: 2505–2512

  59. 59.

    et al. (2002) Effect of mutated TP53 on response of advanced breast cancers to high-dose chemotherapy. Lancet 360: 852–854

Download references

Author information

Affiliations

  1. F Andre is Assistant Professor and L Pusztai is Associate Professor of Medicine, in the Department of Breast Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA.

    • Fabrice Andre
    •  & Lajos Pusztai

Authors

  1. Search for Fabrice Andre in:

  2. Search for Lajos Pusztai in:

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Lajos Pusztai.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/ncponc0636

Further reading