Multifactorial approach to predicting resistance to anthracyclines

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Abstract

Purpose Validated biomarkers predictive of response/resistance to anthracyclines in breast cancer are currently lacking. The neoadjuvant Trial of Principle (TOP) study, in which patients with estrogen receptor (ER) -negative tumors were treated with anthracycline (epirubicin) monotherapy, was specifically designed to evaluate the predictive value of topoisomerase II-α (TOP2A) and develop a gene expression signature to identify those patients who do not benefit from anthracyclines. Patients and Methods The TOP trial included 149 patients, 139 of whom were evaluable for response prediction analyses. The primary end point was pathologic complete response (pCR). TOP2A and gene expression profiles were evaluated using pre-epirubicin biopsies. Gene expression data from ER-negative samples of the EORTC (European Organisation for Research and Treatment of Cancer) 10994/BIG (Breast International Group) 00-01 and MDACC (MD Anderson Cancer Center) 2003-0321 neoadjuvant trials were used for validation purposes. Results A pCR was obtained in 14% of the evaluable patients in the TOP trial. TOP2A amplification, but not protein overexpression, was significantly associated with pCR (P ≤ .001 v P ≤ .33). We developed an anthracycline-based score (A-Score) combining three signatures: a TOP2A gene signature and two previously published signatures related to tumor invasion and immune response. The A-Score was characterized by a high negative predictive value ([NPV]; NPV, 0.98; 95% CI, 0.90 to 1.00) overall and in the human epidermal growth factor receptor 2 (HER2) -negative and HER2-positive subpopulations. Its performance was independently confirmed in the anthracycline-based arms of the two validation trials (BIG 00-01: NPV, 0.83; 95% CI, 0.64 to 0.94 and MDACC 2003-0321: NPV, 1.00; 95% CI, 0.80 to 1.00). © 2011 by American Society of Clinical Oncology.

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Desmedt, C., Di Leo, A., De Azambuja, E., Larsimont, D., Haibe-Kains, B., Selleslags, J., … Sotiriou, C. (2011). Multifactorial approach to predicting resistance to anthracyclines. Journal of Clinical Oncology, 29(12), 1578–1586. https://doi.org/10.1200/JCO.2010.31.2231

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