Molecular Profiling Of Breast Cancer Research Articles

Expression Profiling of Purified Normal Human Luminal and Myoepithelial Breast Cells

The normal duct-lobular system of the breast is lined by two epithelial cell types, inner luminal secretory cells and outer contractile myoepithelial cells. We have generated comprehensive expression profiles of the two normal cell types, using immunomagnetic cell separation and gene expression microarray analysis. The cell-type specificity was confirmed at the protein level by immunohistochemistry in normal breast tissue. New prognostic markers for survival were identified when the luminal- and myoepithelial-specific molecules were evaluated on breast tumor tissue microarrays. Nuclear expression of luminal epithelial marker galectin 3 correlated with a shorter overall survival in these patients, and the expression of SPARC (osteonectin), a myoepithelial marker, was an independent marker of poor prognosis in breast cancers as a whole. These data provide a framework for the interpretation of breast cancer molecular profiling experiments, the identification of potential new diagnostic markers, and development of novel indicators of prognosis.

Molecular profiling of breast cancer: clinical implications.

Breast cancers are routinely subcategorised on the basis of clinical stage, cellular morphology and immunohistochemical analysis of a small number of markers. The recent development of gene expression microarray and related technologies provides an opportunity to perform more detailed profiling of the disease. It is anticipated that the molecular classification arising from such studies will be more powerful than its pathological predecessor at confining treatment to those patients who are most likely to benefit. It is likely that this will result in a much less frequent use of adjuvant chemotherapy. Furthermore, of those who do receive it, a higher proportion will benefit. If adopted, this will offer considerable patient benefits in terms of reducing unnecessary toxicity and have major health economic implications.

Molecular classification of breast carcinomas using tissue microarrays.

The histopathologic classification of breast cancer stratifies tumors based on tumor grade, stage, and type. Despite an overall correlation with survival, this classification is poorly predictive and tumors with identical grade and stage can have markedly contrasting outcomes. Recently, breast carcinomas have been classified by their gene expression profiles on frozen material. The validation of such a classification on formalin-fixed paraffin-embedded tumor archives linked to clinical information in a high-throughput fashion would have a major impact on clinical practice. The authors tested the ability of tumor tissue microarrays (TMAs) to sub-classify breast cancers using a TMA containing 107 breast cancers. The pattern of expression of 13 different protein biomarkers was assessed by immunohistochemistry and the multidimensional data was analyzed using an unsupervised two-dimensional clustering algorithm. This revealed distinct tumor clusters which divided into two main groups correlating with tumor grade (P<0.001) and nodal status (P = 0.04). None of the protein biomarkers tested could individually identify these groups. The biological significance of this classification is supported by its similarity with one derived from gene expression microarray analysis. Thus, molecular profiling of breast cancer using a limited number of protein biomarkers in TMAs can sub-classify tumors into clinically and biologically relevant subgroups.

Prostate-Specific Ets (PSE) factor: a novel marker for detection of metastatic breast cancer in axillary lymph nodes.

Prostate Specific Ets factor is a recently identified transcriptional activator that is overexpressed in prostate cancer. To determine whether this gene is overexpressed in breast cancer, we performed a virtual Northern blot using data available online at the Cancer Genome Anatomy Project website. Ninety-five SAGE libraries were probed with a unique sequence tag to the Prostate Specific Ets gene. The results indicate that Prostate Specific Ets is expressed in 14 out of 15 breast cancer libraries (93%), nine out of 10 prostate cancer libraries (90%), three out of 40 libraries from other cancers (7.5%), and four out of 30 normal tissue libraries (13%). To determine the possibility that the Prostate Specific Ets gene is a novel marker for detection of metastatic breast cancer in axillary lymph nodes, quantitative real-time RT–PCR analyses were performed. The mean level of Prostate Specific Ets expression in lymph nodes containing metastatic breast cancer (n=22) was 410-fold higher than in normal lymph node (n=51). A receiver operator characteristic curve analysis indicated that Prostate Specific Ets was overexpressed in 18 out of 22 lymph nodes containing metastatic breast cancer (82%). The receiver operator characteristic curve analysis also indicated that the diagnostic accuracy of the Prostate Specific Ets gene for detection of metastatic breast cancer in axillary lymph nodes was 0.949. These results provide evidence that Prostate Specific Ets is a potentially informative novel marker for detection of metastatic breast cancer in axillary lymph nodes, and should be included in any study that involves molecular profiling of breast cancer.British Journal of Cancer (2002) 86, 899–904. DOI: 10.1038/sj/bjc/6600190 www.bjcancer.com© 2002 Cancer Research UK

Identification of high risk breast-cancer patients by gene expression profiling

We previously used DNA array analyses in the molecular profiling of breast cancers. By cluster analysis of 55 patients, we identified a subpopulation of breast cancers-designated class A-that contained a high number of nodal-positive tumours and that had frequently developed distant metastases at the time of diagnosis. We have now analysed follow-up data from these patients. We found that, despite a median of only 23.5 months of follow-up, 11 of 22 patients in class A progressed to metastatic disease, and three of five patients classified as having a nodal status of N0 in this subpopulation developed distant metastases. Our analysis identifies breast-cancer patients with a high risk of disease recurrence, and could act as a first step towards improved patient-adapted therapy.

Molecular profiling of breast cancer: portraits but not physiognomy

Breast cancers differ in response to treatment and may have a divergent clinical course despite having a similar histopathological appearance. New technology using DNA microarrays provides a systematic method to identify key markers for prognosis and treatment response by profiling thousands of genes expressed in a single cancer. Microarray profiling of 38 invasive breast cancers now confirms striking molecular differences between ductal carcinoma specimens and suggests a new classification for oestrogen-receptor negative breast cancer. Future approaches will need to include methods for high-throughput clinical validation and the ability to analyze microscopic samples.