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Cancer classification aims to provide an accurate diagnosis of the disease and prediction of tumor behavior to facilitate oncologic decision making. Traditional breast cancer classification, mainly based on clinicopathologic features and assessment of routine biomarkers, may not capture the varied clinical courses of individual breast cancers. The underlying biology in cancer development and progression is complicated. Recent findings from high-throughput technologies added important information with regard to the underlying genetic alterations and the biological events in breast cancer. The information provides insights into new treatment strategies and patient stratifications that impact on the management of breast cancer patients. This review provides an overview of recent data on high throughput analysis of breast cancers, and it analyzes the relationship of these findings with traditional breast cancer classification and their clinical potentials.
Tsang et al. (Tue,) studied this question.
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