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

• Published in: PloS one Vol. 15; no. 2; p. e0226157
• Main Authors: Lee, Sangkyu, Liang, Xiaolin, Woods, Meghan, Reiner, Anne S, Concannon, Patrick, Bernstein, Leslie, Lynch, Charles F, Boice, John D, Deasy, Joseph O, Bernstein, Jonine L, Oh, Jung Hun
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.02.2020; Public Library of Science (PLoS)
• Subjects: Adult; Age; Algorithms; Anopheles; Bioinformatics; Biological analysis; Biology and Life Sciences; Breast cancer; Breast Neoplasms - epidemiology; Breast Neoplasms - genetics; Breast Neoplasms - radiotherapy; Cancer diagnosis; Cancer genetics; Cancer research; Cancer Survivors; Cancer therapies; Carcinogens; Case-Control Studies; Cell cycle; Cell survival; Chemotherapy; Chromosomes; Cohort Studies; Computer and Information Sciences; Cyclic adenosine monophosphate; Cyclic AMP; Deoxyribonucleic acid; DNA; Dosimetry; Drug dosages; Epidemiology; Family medical history; Female; Genes; Genetic polymorphisms; Genome-Wide Association Study - methods; Genomes; Genomics; Genotype; Genotyping; Germ Cells; Health physics; Health risks; Humans; Learning algorithms; Machine Learning; Medical diagnosis; Medical research; Medicine and Health Sciences; Neoplasms, Radiation-Induced - epidemiology; Neoplasms, Radiation-Induced - genetics; Nucleotides; Physical Sciences; Physics; Polymorphism, Single Nucleotide; Population; Proteins; Radiation; Radiation (Physics); Radiation dosage; Radiation therapy; Regression analysis; Research and Analysis Methods; Risk; Risk Factors; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Studies; Young Adult; Denmark; New York; California; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0226157

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.