title: A relative ordering-based predictor for tamoxifen-treated estrogen receptor-positive breast cancer patients: multi-laboratory cohort validation.
abstract: Current predictors for estrogen receptor-positive (ER-positive) breast cancer patients receiving tamoxifen are often invalid in inter-laboratory validation. We aim to develop a robust predictor based on the relative ordering of expression measurement (ROE) in gene pairs. Using a large integrated dataset of 420 normal controls and 1,129 ER-positive breast tumor samples, we identified the gene pairs with stable ROEs in normal control and significantly reversed ROEs in ER-positive tumor. Using these gene pairs, we characterized each sample of a cohort of 292 ER-positive patients who received tamoxifen monotherapy for 5 years and then identified relapse risk-associated gene pairs. We extracted a gene pair subset that resulted in the largest positive and negative predictive values for predicting 10-year relapse-free survival (RFS) using a genetic algorithm. A predictor was developed based on the gene pair subset and was validated in 2 large multi-laboratory cohorts (N = 250 and 248, respectively) of ER-positive patients who received 5-year tamoxifen alone. In the first validation cohort, the patients predicted to be tamoxifen sensitive had a 10-year RFS of 91 % (95 % confidence interval [CI] 85-97 %) with an absolute risk reduction of 34 % (95 % CI 17-51 %). The patients predicted to be tamoxifen insensitive had a significantly higher relapse risk than the patients predicted to be tamoxifen sensitive (hazard ratio = 4.99, 95 % CI 2.45-10.17, P = 9.13 × 10(-7)). Similar performance was achieved for the second validation cohort. The predictor performed well in both node-negative and node-positive subsets and added significant predictive power to the clinical parameters. In contrast, 2 previously proposed predictors did not achieve significantly better performances than the baselines of the validation cohorts. In summary, the proposed predictor can accurately and robustly predict tamoxifen sensitivity of ER-positive breast cancer patients and identified patients with a high probability of 10-year RFS following tamoxifen monotherapy.
authors.affiliation: Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Xianxiao
authors.initials: X
authors.lastname: Zhou
authors.forename: Bailiang
authors.initials: B
authors.lastname: Li
authors.forename: Yuannv
authors.initials: Y
authors.lastname: Zhang
authors.forename: Yunyan
authors.initials: Y
authors.lastname: Gu
authors.forename: Beibei
authors.initials: B
authors.lastname: Chen
authors.forename: Tongwei
authors.initials: T
authors.lastname: Shi
authors.forename: Lu
authors.initials: L
authors.lastname: Ao
authors.forename: Pengfei
authors.initials: P
authors.lastname: Li
authors.forename: Shan
authors.initials: S
authors.lastname: Li
authors.forename: Chunyang
authors.initials: C
authors.lastname: Liu
authors.forename: Zheng
authors.initials: Z
authors.lastname: Guo
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D018931:Antineoplastic Agents, Hormonal / Q000627:therapeutic use*; D014408:Biomarkers, Tumor / Q000235:genetics; D001943:Breast Neoplasms / Q000188:drug therapy* / Q000235:genetics* / Q000401:mortality / Q000473:pathology; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D008875:Middle Aged; D060787:Neoplasm Grading; D011379:Prognosis; D011960:Receptors, Estrogen / Q000235:genetics*; D012008:Recurrence; D013629:Tamoxifen / Q000627:therapeutic use*; D047368:Tumor Burden
chemical_list: D018931:Antineoplastic Agents, Hormonal; D014408:Biomarkers, Tumor; D011960:Receptors, Estrogen; D013629:Tamoxifen