Emerging biomarkers and new understanding of traditional markers in personalized therapy for breast cancer

Abstract

The era of personalized medicine is likely to see an escalation in the use of biomarkers to ensure breast cancer patients receive optimal treatment. A combination of prognostic and predictive biomarkers should enable better quantification of the residual risk faced by patients and indicate the potential value of additional treatment. Established biomarkers such as estrogen receptor and progesterone receptor already play a significant role in the selection of patients for endocrine therapy. Human epidermal growth factor receptor 2 (HER2) is recognized as a strong predictor of response to trastuzumab whereas, more recently, the role of estrogen receptor and HER2 as negative and positive indicators for chemotherapy has also been explored. Ki67 has traditionally been recognized as a modest prognostic factor, but recent neoadjuvant studies suggest that on-treatment measurement may be a more effective predictor of treatment efficacy for both endocrine treatment and chemotherapy. The last decade has seen the emergence of numerous multigene expression profiles that aim to outdo traditional predictive and prognostic factors. The Oncotype DX assay and the MammaPrint profile are currently undergoing prospective clinical trials to clearly define their role. Other gene expression-based assays also show potential but are yet to be tested clinically. Rigorous comparison of these emerging markers with current treatment selection criteria will be required to determine whether they offer significant benefit to justify their use.