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Molecular Subtypes In Breast Cancer Evaluation And Management: Divide And Conquer

J. Peppercorn, C. M. Perou, L. Carey
Published 2008 · Medicine

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In the 19th century, the Scottish surgeon, Dr. Thomas Beatson, recognized that some, but not all, cases of advanced breast cancer would regress in response to “hormonal therapy,” which he administered in 1896 through surgical removal of the ovaries (1). Though it was not recognized at the time, Dr. Beatson had produced the first evidence that, despite arising from the same anatomic area and having similar histological appearance, not all breast cancers were biologically the same. In the decades that followed, we have made many advances in breast cancer therapy but we have made inadequate progress in determining which patients are most likely to benefit from which therapies, and in identifying patients at highest risk for recurrence. Standard clinical prognostic features such as patient age, tumor size, nodal status, grade, and endocrine receptor or HER2 status provide valuable information about risk of relapse, however, these clinical risk estimates are crude. For example, using a conventional mathematical model using clinical features (2), a low-risk cancer (less than 1 cm, nodenegative, estrogen receptor-positive, and low grade occurring in a postmenopausal woman) will carry a 15% risk of recurrence, and a high-risk cancer (more than 5 cm, multimodepositive, estrogen receptor-negative, and high grade) will carry an 85% risk of recurrence. Thus, even in the most clinically compelling circumstances, conventional clinical prognosticators are inaccurate 15% of the time. As a result, many patients with early stage disease are treated with toxic therapies they may not need, and others are falsely reassured of a favorable prognosis based on clinical features that mask their true risk (3). Recent evidence suggests that we can do better. Gene expression profiling, which allows simultaneous assessment of the contribution of thousands of genes in a single tumor sample, reveals a biological diversity in breast cancer that mirrors the clinical diversity in outcomes. This technique reveals that regardless of clinical features, breast cancers are several different diseases on the molecular level (4). Differences in behavior and response that seem random on the basis of known prognostic factors can be predicted by gene expression profiles that reclassify breast tumors into distinct subtypes, which should be viewed as distinct entities and managed as such. Microarray analysis of gene expression thus represents a valuable tool for assessing potential biological differences between breast cancers that may otherwise seem similar, to identify additional molecular differences between tumors with different histological characteristics, to assess the potential biological basis for commonly observed differences in outcome, and to develop better predictive models for determination of prognosis and response to therapy based on tumor biology. This chapter explains how gene expression profiling has advanced our understanding of breast cancer biology, reviews the subtypes of breast cancer that have been identified through this new tool, and explores how these discoveries are helping us advance treatments for different classes of breast cancer. In addition, we identify some of the pitfalls of gene expression analysis and areas for future research in this field.
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