Individualization of therapy using Mammaprint: from development to the MINDACT Trial.

To date, most treatment decisions for adjuvant chemotherapy in breast cancer are sed on conventional clinicopathological criteria. Since breast cancer tumors with similar clinicopathological characteristics can have strikingly different outcomes, the current selection for adjuvant chemotherapy is far from accurate. Using high-throughput microarray analysis, a 70-gene signature was identified which can accurately select early stage breast cancer patients who are highly likely to develop distant metastases, and therefore, may benefit the most from adjuvant chemotherapy. This review describes the development of the 70-gene profile (Mammaprint), its retrospective validation and feasibility studies, and its prospective validation in the large adjuvant MINDACT (Microarray In Node-negative Disease may Avoid ChemoTherapy) clinical trial.

[1]  R. Gelber,et al.  Meeting highlights: international consensus panel on the treatment of primary breast cancer. , 2002, Journal of the National Cancer Institute.

[2]  Karen A Gelmon,et al.  Population-based validation of the prognostic model ADJUVANT! for early breast cancer. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[3]  M. V. Vijver.  Gene-Expression Profiling and the Future of Adjuvant Therapy , 2005 .

[4]  Van,et al.  A gene-expression signature as a predictor of survival in breast cancer. , 2002, The New England journal of medicine.

[5]  Y Wang,et al.  Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials , 2005, The Lancet.

[6]  M. Radmacher,et al.  Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. , 2003, Journal of the National Cancer Institute.

[7]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Yudong D. He,et al.  Expression profiling using microarrays fabricated by an ink-jet oligonucleotide synthesizer , 2001, Nature Biotechnology.

[9]  J. Bergh,et al.  Strong Time Dependence of the 76-Gene Prognostic Signature for Node-Negative Breast Cancer Patients in the TRANSBIG Multicenter Independent Validation Series , 2007, Clinical Cancer Research.

[10]  L. V. van't Veer,et al.  Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer. , 2006, Journal of the National Cancer Institute.

[11]  J. Foekens,et al.  Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.

[12]  M. J. van de Vijver,et al.  Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. , 2006, Journal of the National Cancer Institute.

[13]  R. Spang,et al.  Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[14]  J. Couzin.  Amid Debate, Gene-Based Cancer Test Approved , 2007, Science.

[15]  M. Cronin,et al.  A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. , 2004, The New England journal of medicine.

[16]  Robert J. Mayer,et al.  National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1-3, 2000. , 2001, Journal of the National Cancer Institute.

[17]  Syed Mohsin,et al.  Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer , 2003, The Lancet.

[18]  M. West,et al.  Gene expression predictors of breast cancer outcomes , 2003, The Lancet.

[19]  M Markman,et al.  National Institutes of Health Consensus Development Conference Statement: adjuvant therapy for breast cancer, November 1-3, 2000. , 2001, Journal of the National Cancer Institute.

[20]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[21]  Maurice P H M Jansen,et al.  Molecular classification of tamoxifen-resistant breast carcinomas by gene expression profiling. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[22]  R. Gelber,et al.  Meeting highlights: International Consensus Panel on the Treatment of Primary Breast Cancer. Seventh International Conference on Adjuvant Therapy of Primary Breast Cancer. , 2001, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[23]  Philip M. Long,et al.  Breast cancer classification and prognosis based on gene expression profiles from a population-based study , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Marc Buyse,et al.  Gene signature evaluation as a prognostic tool: challenges in the design of the MINDACT trial , 2006, Nature Clinical Practice Oncology.

[25]  Catherine Legrand.  1687Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials : Early Breast Cancer Trialists' Collaborative Group (EBCTCG) , 2005 .

[26]  Wim H van Harten,et al.  Methodology of constructive technology assessment in health care , 2007, International Journal of Technology Assessment in Health Care.

[27]  R. Gelber,et al.  Meeting highlights: international expert consensus on the primary therapy of early breast cancer 2005. , 2005, Annals of oncology : official journal of the European Society for Medical Oncology.

[28]  P. Ravdin,et al.  Computer program to assist in making decisions about adjuvant therapy for women with early breast cancer. , 2001, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.