Background: Addition of taxanes to preoperative chemotherapy in breast cancer increases the proportion of patients who have a pathological complete response (pCR). However, a substantial proportion of patients do not respond, and the prognosis is particularly poor for patients with oestrogen-receptor (ER)/progesterone-receptor (PR)/human epidermal growth factor receptor 2 (HER2; ERBB2)-negative (triple-negative) disease who do not achieve a pCR. Reliable identification of such patients is the first step in determining who might benefit from alternative treatment regimens in clinical trials. We previously identified genes involved in mitosis or ceramide metabolism that influenced sensitivity to paclitaxel, with an RNA interference (RNAi) screen in three cancer cell lines, including a triple-negative breast-cancer cell line. Here, we assess these genes as a predictor of pCR to paclitaxel combination chemotherapy in triple-negative breast cancer.
Methods: We derived a paclitaxel response metagene based on mitotic and ceramide genes identified by functional genomics studies. We used area under the curve (AUC) analysis and multivariate logistic regression to retrospectively assess the metagene in six cohorts of patients with triple-negative breast cancer treated with neoadjuvant chemotherapy; two cohorts treated with paclitaxel (n=27, 30) and four treated without paclitaxel (n=88, 28, 48, 39).
Findings: The metagene was associated with pCR in paclitaxel-treated cohorts (AUC 0.79 [95% CI 0.53-0.93], 0.72 [0.48-0.90]) but not in non-paclitaxel treated cohorts (0.53 [0.31-0.77], 0.59 [0.22-0.82], 0.53 [0.36-0.71], 0.64 [0.43-0.81]). In multivariate logistic regression, the metagene was associated with pCR (OR 19.92, 2.62-151.57; p=0.0039) with paclitaxel-containing chemotherapy.
Interpretation: The paclitaxel response metagene shows promise as a paclitaxel-specific predictor of pCR in patients with triple-negative breast cancer. The metagene is suitable for development into a reverse transcription-PCR assay, for which clinically relevant thresholds could be established in randomised clinical trials. These results highlight the potential for functional genomics to accelerate development of drug-specific predictive biomarkers without the need for training clinical trial cohorts.
Funding: UK Medical Research Council; Cancer Research UK; the National Institute for Health Research (UK); the Danish Council for Independent Research-Medical Sciences (FSS); Breast Cancer Research Foundation (New York); Fondation Luxembourgeoise contre le Cancer; the Fonds National de la Recherche Scientifique; Brussels Region (IRSIB-IP, Life Sciences 2007) and Walloon Region (Biowin-Keymarker); Sally Pearson Breast Cancer Fund; and the European Commission.
2010 Elsevier Ltd. All rights reserved.