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A relative ordering-based predictor for tamoxifen-treated estrogen receptor-positive breast cancer patients: multi-laboratory cohort validation

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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.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China [grant numbers 81071646 and 91029717 to ZG, 81201822 to YG]; and the Research Fund for the Doctoral Program of Higher Education of China [20112307110011 to ZG]. The funders had no role in study design, data collection and analysis, preparation of the manuscript, or decision to publish.

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The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Bioinformatics Centre, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

    Xianxiao Zhou, Tongwei Shi, Shan Li & Zheng Guo

  2. College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China

    Bailiang Li, Yuannv Zhang, Yunyan Gu, Beibei Chen, Lu Ao, Pengfei Li & Zheng Guo

  3. Genomics Research Center, Harbin Medical University, Harbin, China

    Bailiang Li

  4. Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China

    Lu Ao, Chunyang Liu & Zheng Guo

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  1. Xianxiao Zhou
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Correspondence to Zheng Guo.

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Zhou, X., Li, B., Zhang, Y. et al. A relative ordering-based predictor for tamoxifen-treated estrogen receptor-positive breast cancer patients: multi-laboratory cohort validation. Breast Cancer Res Treat 142, 505–514 (2013). https://doi.org/10.1007/s10549-013-2767-8

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