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Genomic Predictors of Outcome and Treatment Response in Breast Cancer

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Abstract

Despite advances in breast cancer treatment and outcome over the last two decades, women continue to relapse and die of advanced disease. Historically, estrogen and progesterone receptor expression, HER2 overexpression and clinico-pathologic parameters have guided therapeutic decision making. However, there are limits to the risk estimation provided by these parameters, leading to potential overtreatment of low-risk disease and undertreatment of poor-risk disease. Genomic technologies now provide the opportunity to refine our therapeutic approach by individualizing treatment to patients’ individual tumor profiles. Gene profiles or signatures are groupings of genes that are differentially expressed between tumors, reflecting differences in biologic behavior. Prognostic gene signatures stratify breast cancer patients by tumor natural history, regardless of the treatment employed. Currently, there are three commercially available prognostic gene signatures: Oncotype DX® (Genomic Health, Inc.), MammaPrint® (Agendia BV), and the HOXB13/IL17BR (H/I) ratio; (Theros H/ISM; bioTheranostics). Others under development include the Intrinsic Gene Set, the Rotterdam Signature, the Wound Response Indicator, and the Invasive Gene Signature. Predicative signatures classify patients based on responsiveness to specific therapies. Of the prognostic signatures, Oncotype DX® has been shown to have predictive value for the incremental benefit of chemotherapy when added to a hormonal therapy regimen. Additional genetic profiles under development predict response to specific hormonal therapies, anthracyclines, and taxanes. Gene signatures have the potential to transform breast cancer treatment as it becomes tailored to each patient’s tumor expression profile and significantly improve the outcomes of this disease.

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Acknowledgments

Lara Dunn is funded through a FOCUS Medical Student Fellowship in Women’s Health, supported by a Bertha Dagan Berman Award. The authors have no conflicts of interest that are directly related to the content of this review.

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  1. Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Lara Dunn &  Angela DeMichele

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  1. Lara Dunn
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Correspondence to Angela DeMichele.

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Dunn, L., DeMichele, A. Genomic Predictors of Outcome and Treatment Response in Breast Cancer. Mol Diag Ther 13, 73–90 (2009). https://doi.org/10.1007/BF03256317

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