For more than a century, radiation has been used as an effective therapeutic modality for many different cancers and other diseases. Today, radiation therapy is clinically indicated for more than half of all cancer patients, with the ability to provide cure, local or regional control, and symptomatic palliation depending upon the clinical context. However, radiation can leave a lasting mark on the normal tissues left behind. In particular, it has long been known that ionizing radiation can promote cancer in otherwise normal tissue. While it is relatively rare for an individual to develop a secondary malignancy (radiation-induced cancer following treatment for a separate cancer), actual estimates of this rate vary widely according to different studies. Furthermore, patient-level discussions of secondary malignancy rates are understandably variable, neglect any understanding of the role of radiation dose or treatment site, and are generally universal assumptions not tailored to the disease or the patient themselves. My goal is to better understand the individualized risk of cancer induced by ionizing radiation. My central hypothesis is that individual genetic variability is likely to modify the risks of radiation-induced malignancy. However we have poor quantitative insights and an overall incomplete picture of the identity, nature, and effect size of genetic determinants of these risks. The ultimate goal of this proposal is to develop improved risk prediction frameworks incorporating prospective genetic stratification. This would be invaluable for treatment-related clinical decision-making, patient counseling, and tailoring post-radiation screening paradigms. I plan to test my central hypothesis by pursuing the following three Specific Aims: Aim 1. Identify a high-confidence cohort of Veterans receiving radiation therapy Aim 2. Characterize second and secondary malignancy rates within the VA Aim 3. Quantify genetic risk factors of radiation-induced secondary malignancies To accomplish these aims, I will first implement, validate, and apply automated dose quantification tools to national-level cohort data from the VA Corporate Data Warehouse (CDW), to extract radiotherapy details such as date, modality, dose, and fractionation, among other clinically important radiotherapy treatment variables. I will then identify new cancer diagnos(es) following initial cancer treatment and perform propensity matching of second cancer risk for Veterans exposed or unexposed to radiotherapy. Moreover, I will quantify second and presumed secondary malignancy rates among individuals as a function of estimated integral radiation dose. Using genetic data from the Million Veteran Program (MVP), I will measure enrichment and potential functional significance of genetic variants among a cohort of Veterans with radiation-induced secondary malignancy. I will also identify putative DNA repair defects and other rare variants in known cancer predisposition genes among Vetera...