title: Rank-based predictors for response and prognosis of neoadjuvant taxane-anthracycline-based chemotherapy in breast cancer.
abstract: 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.
authors.affiliation: Bioinformatics Centre, School of Life Science, University of Electronic Science and Technology of China, Chengdu, 610054, China. linzhang.bioinformatics@gmail.com
authors.forename: Lin
authors.initials: L
authors.lastname: Zhang
authors.forename: Chunxiang
authors.initials: C
authors.lastname: Hao
authors.forename: Xiaopei
authors.initials: X
authors.lastname: Shen
authors.forename: Guini
authors.initials: G
authors.lastname: Hong
authors.forename: Hongdong
authors.initials: H
authors.lastname: Li
authors.forename: Xianxiao
authors.initials: X
authors.lastname: Zhou
authors.forename: Chunyang
authors.initials: C
authors.lastname: Liu
authors.forename: Zheng
authors.initials: Z
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D018943:Anthracyclines / Q000008:administration & dosage; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics* / Q000401:mortality; D001952:Bridged-Ring Compounds / Q000008:administration & dosage; D019295:Computational Biology / Q000379:methods; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008490:Medical Informatics / Q000379:methods; D020360:Neoadjuvant Therapy; D011379:Prognosis; D015203:Reproducibility of Results; D043823:Taxoids / Q000008:administration & dosage; D016896:Treatment Outcome
chemical_list: D018943:Anthracyclines; D001952:Bridged-Ring Compounds; D043823:Taxoids; C080625:taxane