Machine Learning Methods to Identify Genetic Correlates of Radiation-Associated Contralateral Breast Cancer in the WECARE Study

Lee, S.; Liang, X.; Woods, M.; Reiner, A.; Thomas, D.; Concannon, P.; Bernstein, L.; Lynch, C.; Boice, J.; Deasy, J.; Bernstein, J.; Oh, J. H.

doi:10.1101/547422

bioRxiv · 10.1101/547422

Machine Learning Methods to Identify Genetic Correlates of Radiation-Associated Contralateral Breast Cancer in the WECARE Study

Lee, S.·Liang, X.·Woods, M.·Reiner, A.·Thomas, D.·Concannon, P.·Bernstein, L.·Lynch, C.·Boice, J.·Deasy, J.·Bernstein, J.·Oh, J. H.

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Abstract

The purpose of this study is to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process. Fifty-two women with contralateral breast cancer and 153 women with unilateral breast cancer were identified within the Womens Environmental Cancer and Radiation Epidemiology (WECARE) Study who were at increased risk of RCBC because they were [≤] 40 years of age at first diagnosis of breast cancer and received a scatter radiation dose > 1 Gy to the contralateral breast. A previously reported algorithm, preconditioned random forest regression, was applied to predict the risk of developing RCBC. The resulting model produced an area under the curve of 0.62 (p=0.04) on hold-out validation data. The biological analysis identified the cyclic AMP-mediated signaling and Ephrin-A as significant biological correlates, which were previously shown to influence cell survival after radiation in an ATM-dependent manner. The key connected genes and proteins that are identified in this analysis were previously identified as relevant to breast cancer, radiation response, or both. In summary, machine learning/bioinformatics methods applied to genome-wide genotyping data have great potential to reveal plausible biological correlates associated with the risk of RCBC.

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Lee, S., Liang, X., Woods, M., Reiner, A., Thomas, D., Concannon, P., Bernstein, L., Lynch, C., Boice, J., Deasy, J., Bernstein, J., Oh, J. H.. 2019-02-12. Machine Learning Methods to Identify Genetic Correlates of Radiation-Associated Contralateral Breast Cancer in the WECARE Study. https://doi.org/10.1101/547422

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