Rank-based predictors for response and prognosis of neoadjuvant taxane-anthracycline-based chemotherapy in breast cancer

摘要

For neoadjuvant taxane and anthracycline-based chemotherapy for breast cancer, patients with pathological complete response (pCR) have a favorable prognosis compared with patients with residual disease (RD). Although a number of pCR predictors based on microarray profiles have been proposed to guide neoadjuvant chemotherapy, most of these have not been independently validated in inter-laboratory datasets, possibly owing to the fact that microarray measurements are sensitive to experimental batch effects and inter-array data normalizations. In this study, we developed a rank-based method to tackle this difficulty. First, we extracted from two datasets a combination of gene pairs, each of which had opposing relative expression orders in patients with pCR and those with RD, and used these to build a pCR predictor. This pCR predictor was found to have sensitivities of 74 and 86 and specificities of 71 and 68 in another two independent datasets from multiple laboratories, and these results were better than the performances of three previously reported predictors. Considering that patients with minimal RD also tend to have a good prognosis, we then developed a prognosis predictor for RD as a complement to the pCR predictor, in order to identify a group of patients likely to have a good prognosis, taking into account both the RD level and the intrinsic risk factors. In the independent validation, there was a significant difference (P = 0.001) in distant relapse-free survival between the patients likely to and those not likely to have a good prognosis according to our prognosis predictor. In conclusion, the rank-based predictors for response and prognosis can accurately and robustly predict patients with improved prognosis who might benefit from neoadjuvant taxane and anthracycline-based chemotherapy.