Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study.

Journal: PloS one
PMID:

Abstract

The purpose of this study was 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 Women's 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 (AUC) 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.

Authors

  • Sangkyu Lee
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Xiaolin Liang
    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America.
  • Meghan Woods
    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America.
  • Anne S Reiner
    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America.
  • Patrick Concannon
    Genetics Institute and Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States of America.
  • Leslie Bernstein
    Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, United States of America.
  • Charles F Lynch
    Department of Epidemiology, The University of Iowa, Iowa City, IA, United States of America.
  • John D Boice
    Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States of America.
  • Joseph O Deasy
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Jonine L Bernstein
    Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America.
  • Jung Hun Oh
    Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.