Breast cancer heterogeneity represents a major hurdle to improve patient survival. Notwithstanding its potential curability due to the availability of treatment modalities that are effective in the presence of favourable clinical or patho-biologic features, there is still a great deal of controversy in its clinical management. In the last decades, tumour biomarkers that are indicative of or related to cell traits characterising malignancy, such as self-sufficiency in proliferative growth signals, insensitivity to growth inhibitory signals, evasion of apoptosis, limitless replicative potential, activation of pathways leading to neo-angiogenesis, invasion and metastasis, have provided information that have proved to be associated with disease progression. However, when singly analysed, their prognostic relevance was modest, and the only clinically useful biomarkers that remained are cell proliferation and plasminogen activationrelated factors for prognosis, steroid hormone receptors and HER2/neu for prediction of response to hormonal or to the novel targeted anti-HER2/neu therapy, respectively. It therefore remains necessary to reduce the intrinsic complexity of breast cancer in order to improve its clinical outcome. One way to achieve this objective derives directly from the concept that cancer is a genetic disease at the somatic level and from the recent availability of high-throughput post-genomic analytical tools such as gene and protein expression techniques for global gene expression analysis. The knowledge derived from gene expression-profiling studies is impressive and challenges currently used breast cancer classification and existing theories about metastatic progression and breast cancer biology. Several studies employing this technology have been consistent in reproducing a molecular classification for breast cancer in which: (1) oestrogen receptor status and tumour grade are the most important discriminators of gene expression subgroups; (2) tumours can be grouped into at least four subsets according to steroid receptor and HER2/neu status; (3) each subset of tumours has a distinct clinical outcome and may therefore respond differentially to various treatments. Additionally, prognostic gene expression signatures have been proposed that outperform traditional clinical risk classification systems, suggesting the possibility to reduce over-treatment in early breast cancer, notwithstanding that the identification of high-risk patients still needs to be improved. A number of recent studies have been directed to answer different clinical and biological questions. However, despite initial enthusiasm doubts have been raised recently regarding the reliability of gene expression profiling for clinical applications, and the outcome of these novel studies still needs to be validated with the cooperation of different specialists and the integration between all the different skills involved in translational research in oncology.
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