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Prognostic molecular markers in early breast cancer

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      Abstract

      A multitude of molecules involved in breast cancer biology have been studied as potential prognostic markers. In the present review we discuss the role of established molecular markers, as well as potential applications of emerging new technologies. Those molecules used routinely to make treatment decisions in patients with early-stage breast cancer include markers of proliferation (e.g. Ki-67), hormone receptors, and the human epidermal growth factor receptor 2. Tumor markers shown to have prognostic value but not used routinely include cyclin D1 and cyclin E, urokinase-like plasminogen activator/plasminogen activator inhibitor, and cathepsin D. The level of evidence for other molecular markers is lower, in part because most studies were retrospective and not adequately powered, making their findings unsuitable for choosing treatments for individual patients. Gene microarrays have been successfuly used to classify breast cancers into subtypes with specific gene expression profiles and to evaluate prognosis. RT-PCR has also been used to evaluate expression of multiple genes in archival tissue. Proteomics technologies are in development.

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      Molecular portraits of human breast tumours.

      Human breast tumours are diverse in their natural history and in their responsiveness to treatments. Variation in transcriptional programs accounts for much of the biological diversity of human cells and tumours. In each cell, signal transduction and regulatory systems transduce information from the cell's identity to its environmental status, thereby controlling the level of expression of every gene in the genome. Here we have characterized variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals, using complementary DNA microarrays representing 8,102 human genes. These patterns provided a distinctive molecular portrait of each tumour. Twenty of the tumours were sampled twice, before and after a 16-week course of doxorubicin chemotherapy, and two tumours were paired with a lymph node metastasis from the same patient. Gene expression patterns in two tumour samples from the same individual were almost always more similar to each other than either was to any other sample. Sets of co-expressed genes were identified for which variation in messenger RNA levels could be related to specific features of physiological variation. The tumours could be classified into subtypes distinguished by pervasive differences in their gene expression patterns.
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        Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications.

        The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.
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          Gene expression profiling predicts clinical outcome of breast cancer.

          Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.
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            Author and article information

            Affiliations
            [1 ]Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
            [2 ]Department of Molecular & Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
            Contributors
            Journal
            Breast Cancer Res
            Breast Cancer Research
            BioMed Central (London )
            1465-5411
            1465-542X
            2004
            11 March 2004
            : 6
            : 3
            : 109-118
            400674
            bcr777
            15084231
            10.1186/bcr777
            Copyright © 2004 BioMed Central Ltd
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            Review

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