Quantifying and improving radiotherapy outcomes among Veterans

世纪以来，放射治疗一直是一种有效的治疗方法 for many许多different不同cancers癌症and other diseases疾病.今天，放射治疗在临床上适用于 超过一半的癌症患者，有能力提供治愈，局部或区域控制， 和症状缓解取决于临床背景。然而，辐射会留下一个 在正常组织上留下的持久印记。特别是，人们早就知道， 放射线可在正常组织中促进癌症。虽然这是相对罕见的一个 个体发展为继发性恶性肿瘤（放射性诱发的癌症， 另一种癌症），但根据不同的研究，对这一比率的实际估计差异很大。 此外，可以理解的是， 变量，忽略对辐射剂量或治疗部位的作用的任何理解， 一般普遍的假设不适合疾病或病人本身。 我的目标是更好地了解电离辐射诱发癌症的个体风险， 辐射我的中心假设是，个体遗传变异可能会改变 辐射诱发的恶性肿瘤然而，我们缺乏定量的见解， 这些风险的遗传决定因素的身份，性质和效应大小的不完整的图片。 该提案的最终目标是制定更好的风险预测框架 结合了预期的遗传分层。这对于治疗相关的 临床决策、患者咨询和定制放射后筛查范例。 我计划通过追求以下三个具体目标来检验我的中心假设： 目标1.确定接受放射治疗的退伍军人的高置信度队列 目标2.描述VA内的继发性和继发性恶性肿瘤发生率 目标3：量化辐射诱发继发性恶性肿瘤的遗传风险因素 为了实现这些目标，我将首先实现、验证和应用自动剂量 从VA公司数据仓库（CDW）的国家级队列数据的量化工具， 提取放射治疗细节，例如日期、模态、剂量和分次， 临床上重要的放射治疗变量。然后，我将确定新的癌症诊断（es） 在初始癌症治疗后， 接受或未接受放射治疗的退伍军人。此外，我将量化第二和 作为估计积分函数的个体间假定继发性恶性肿瘤发生率 辐射剂量使用百万退伍军人计划（MVP）的遗传数据，我将测量 退伍军人群体中遗传变异的富集和潜在功能意义 辐射诱发的继发性恶性肿瘤我还将确定推定的DNA修复缺陷， 其他罕见的变异，在已知的癌症易感基因退伍军人与第二癌症。 我完成了目标1、2和3中所述的研究，预计将建立一个 详细了解辐射诱发恶性肿瘤的遗传风险因素。而且这些 目标将建立一个非常有价值的退伍军人队列， 恶性信息，沿着相应的种系遗传数据。最终，这些 资源和结果预计将对当前的辐射风险产生深远的影响 评估框架，通过加深我们对个人之间相互作用的理解 基因和个人风险。

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 Veterans with second cancers. My completion of the research described in Aims 1, 2 and 3 is expected to establish a detailed understanding of genetic risk factors for radiation-induced malignancy. Moreover, these Aims will establish a highly valuable cohort of veterans with curated radiotherapy and secondary malignancy information, along with corresponding germline genetic data. Ultimately, these resources and results are expected to have a profound impact on current radiation risk assessment frameworks, by deepening our understanding of the interplay between individual genetics and personalized risks.