Molecular classification of breast cancer: limitations and potential.

Reverse transcription polymerase chain reaction and DNA microarrays are increasingly used in the clinic and in clinical research as prognostic or predictive tests. Results from these tests led to novel risk stratification methods and to new molecular classification of breast cancer. Some of these tools already complement existing diagnostic tests and can aid medical decision making in some situations. Better understanding of the molecular classes of breast cancer, independent of their prognostic and predictive values, may also lead to new biological insights and eventually to better therapies that are directed toward particular molecular subsets. However, there is substantially less experience with these emerging technologies than with the more established methods, the accuracy of which is often overestimated. This review discusses some of the limitations and strengths of current gene expression-based molecular classification of breast cancer. To provide context for this discussion, we also briefly examine the performance of estrogen receptor immunohistochemistry, which represents an essential part of the routine diagnostic workup for all breast cancer patients.

[1]  Peter A Kaufman,et al.  Concordance between local and central laboratory HER2 testing in the breast intergroup trial N9831. , 2002, Journal of the National Cancer Institute.

[2]  R. Tibshirani,et al.  Repeated observation of breast tumor subtypes in independent gene expression data sets , 2003, Proceedings of the National Academy of Sciences of the United States of America.

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

[4]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

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

[6]  M. Daly,et al.  PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes , 2003, Nature Genetics.

[7]  Roman Rouzier,et al.  Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[8]  Lajos Pusztai,et al.  Reproducibility of Gene Expression Signature–Based Predictions in Replicate Experiments , 2006, Clinical Cancer Research.

[9]  Richard M. Simon,et al.  Methods for assessing reproducibility of clustering patterns observed in analyses of microarray data , 2002, Bioinform..

[10]  J. Ross,et al.  Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  R. Bast,et al.  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. , 2001, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  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.

[13]  C K Osborne,et al.  Estrogen receptor status by immunohistochemistry is superior to the ligand-binding assay for predicting response to adjuvant endocrine therapy in breast cancer. , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[14]  C. Sotiriou,et al.  Measurements of estrogen receptor and reporter genes from microarrays determine receptor status and time to recurrence following adjuvant tamoxifen therapy. , 2005 .

[15]  C. Boni,et al.  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. , 2001, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[16]  S. Shak,et al.  Gene expression and breast cancer mortality in Northern California Kaiser Permanente Patients: A large population-based case control study , 2005 .

[17]  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.

[18]  J. Robertson,et al.  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. , 2000, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[19]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[20]  A. Nobel,et al.  The molecular portraits of breast tumors are conserved across microarray platforms , 2006, BMC Genomics.

[21]  Stan Pounds,et al.  Estimating the Occurrence of False Positives and False Negatives in Microarray Studies by Approximating and Partitioning the Empirical Distribution of P-values , 2003, Bioinform..

[22]  Thomas Rüdiger,et al.  Quality Assurance in Immunohistochemistry: Results of an Interlaboratory Trial Involving 172 Pathologists , 2002, The American journal of surgical pathology.

[23]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[24]  Roman Rouzier,et al.  Breast Cancer Molecular Subtypes Respond Differently to Preoperative Chemotherapy , 2005, Clinical Cancer Research.

[25]  D. Larsimont,et al.  Estrogen receptor analysis in primary breast tumors by ligand-binding assay, immunocytochemical assay, and northern blot: a comparison , 2001, Breast Cancer Research and Treatment.

[26]  M. Cronin,et al.  Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[27]  N. Goldstein,et al.  Minimum formalin fixation time for consistent estrogen receptor immunohistochemical staining of invasive breast carcinoma. , 2003, American journal of clinical pathology.

[28]  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.

[29]  E. Perez,et al.  HER2 testing by local, central, and reference laboratories in the NCCTG N9831 Intergroup Adjuvant Trial. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[30]  J. Stec,et al.  Gene expression profiles predict complete pathologic response to neoadjuvant paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide chemotherapy in breast cancer. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[31]  A. Rhodes Quality assurance in immunohistochemistry. , 2003, The American journal of surgical pathology.

[32]  J. Foekens,et al.  Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[33]  G. W. Milligan,et al.  An examination of procedures for determining the number of clusters in a data set , 1985 .

[34]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[35]  C. Giardina,et al.  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. , 2001, European journal of cancer.

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

[37]  J. Stec,et al.  Gene expression profiles obtained from fine-needle aspirations of breast cancer reliably identify routine prognostic markers and reveal large-scale molecular differences between estrogen-negative and estrogen-positive tumors. , 2003, Clinical cancer research : an official journal of the American Association for Cancer Research.

[38]  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.

[39]  D. Barnes,et al.  Reliability of immunohistochemical demonstration of oestrogen receptors in routine practice: interlaboratory variance in the sensitivity of detection and evaluation of scoring systems , 2000, Journal of clinical pathology.