Risk of developing contralateral breast cancer is a major concern among breast cancer survivors, especially for those who received radiotherapy for a first primary breast cancer. The risk of developing radiation-associated contralateral breast cancer (RCBC) is further increased among those who were exposed to radiation at an early age. Several genotyping studies have shown that variation in the individual risk of developing RCBC is associated with single nucleotide polymorphism (SNP) genetic variants. However, these studies have mainly analyzed a limited set of target/candidate SNPs that had been associated with general primary breast cancer in prior studies. This approach, building predictive models based on a small set of SNPs, has made marginal progress in distinguishing individual risk of RCBC. To the contrary, complex phenotypes or traits are likely the result of interactions of many biological sub-systems, most of which individually provide small effect size to predictive models, incrementally improving risk prediction. We have recently developed novel machine learning methods that use genome-wide SNPs to build patient-specific risk models of radiation-induced toxicity. These models use hundreds of SNPs in a nonlinear fashion and can be used to identify key biological correlates. Our long-term goal is to develop a clinical decision support tool that can be used to guide radiotherapy treatment decisions based on individual risk of RCBC. To improve patient-specific risk prediction of RCBC, we propose to apply these innovative methods to a rich dataset of the Women?s Environmental Cancer and Radiation Epidemiology (WECARE) Study. Under SA1: Genome-wide genotyping of the WECARE Study II, as part of this grant, we will complete genome-wide association studies (GWAS) genotyping of 1626 samples from the WECARE Study II. Under SA2.1: Predictive modeling and biological analysis, we will apply our novel machine learning methods to the combined WECARE Study I and II to design a predictive model of RCBC risk in a young subpopulation treated with radiotherapy, using GWAS genotyping, clinical, and radiation data. We will also use bioinformatics methods to identify key biological correlates associated with RCBC risk. Under SA2.2: Comparison of biological correlates between subgroups, we will further investigate biological processes associated with radiation-unrelated contralateral breast cancer for the combined cohort in the WECARE Study I and II who did not receive radiotherapy. The resulting biological correlates will be compared with those found in SA2.1 for radiotherapy-treated women to better understand RCBC-specific biological mechanisms. Our model validation using an independent series of childhood cancer survivors who have developed radiation-associated breast cancer will enable us to examine the reliability and reproducibility of the model as a decision-making tool. If the RCBC risk model is validated, it will provide a clinical guide to identify high-risk patients who may need altered radiotherapy techniques (e.g., proton therapy), which offer reduced scatter dose.
Young women who receive radiotherapy are at increased risk for radiation-associated breast cancer. We have developed innovative modeling methods to build a predictive model of radiation-associated breast cancer risk based on patient genetic profiles using the unique, multi- center WECARE Study dataset for this study. If successful with rigorous validation on an independent dataset, this project will result in a clinical tool that could be used to inform breast cancer treatment decisions for young women at increased risk of radiation-associated breast cancer.