Journal article
Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study
PloS one, Vol.15(2), pp.e0226157-e0226157
2020
DOI: 10.1371/journal.pone.0226157
PMCID: PMC7046218
PMID: 32106268
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.
Details
- Title: Subtitle
- Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study
- Creators
- Sangkyu Lee - Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of AmericaXiaolin Liang - Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of AmericaMeghan Woods - Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of AmericaAnne S Reiner - Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of AmericaPatrick Concannon - Genetics Institute and Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States of AmericaLeslie Bernstein - Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, United States of AmericaCharles F Lynch - Department of Epidemiology, The University of Iowa, Iowa City, IA, United States of AmericaJohn D Boice - Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States of AmericaJoseph O Deasy - Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of AmericaJonine L Bernstein - Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of AmericaJung Hun Oh - Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
- Resource Type
- Journal article
- Publication Details
- PloS one, Vol.15(2), pp.e0226157-e0226157
- DOI
- 10.1371/journal.pone.0226157
- PMID
- 32106268
- PMCID
- PMC7046218
- NLM abbreviation
- PLoS One
- ISSN
- 1932-6203
- eISSN
- 1932-6203
- Grant note
- R01 CA097397 / NCI NIH HHS P30 ES005605 / NIEHS NIH HHS U01 CA083178 / NCI NIH HHS P30 CA008748 / NCI NIH HHS P30 CA086862 / NCI NIH HHS R01 CA114236 / NCI NIH HHS
- Language
- English
- Date published
- 2020
- Academic Unit
- Epidemiology
- Record Identifier
- 9984214708702771
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