Quantifying and improving radiotherapy outcomes among Veterans

一个多世纪以来，辐射一直被用作一种有效的治疗手段。 治疗许多不同的癌症和其他疾病。今天，放射治疗在临床上被认为是 超过一半的癌症患者，有能力提供治愈、局部或区域控制， 以及根据临床情况进行症状缓解。然而，辐射可能会留下 留下的正常组织上的持久印记。特别是，人们很早就知道电离 辐射可以促进正常组织中的癌症。虽然这是相对罕见的一个 个人发展为继发性恶性肿瘤(治疗后发生辐射诱发癌症) 作为一种单独的癌症)，根据不同的研究，对这一比率的实际估计相差很大。 此外，患者层面对继发性恶性肿瘤发生率的讨论也是可以理解的。 易变，忽视对辐射剂量或治疗部位的作用的任何了解，并且是 一般来说，普遍的假设不是针对疾病或患者本身量身定做的。 我的目标是更好地了解电离导致癌症的个体化风险。 辐射。我的中心假设是，个体的遗传变异可能会改变 辐射诱发的恶性肿瘤。然而，我们缺乏量化的洞察力和整体的 对这些风险的遗传决定因素的身份、性质和影响大小的描述不完整。 这项提议的最终目标是开发改进的风险预测框架 结合了预期的遗传分层。这将对与治疗相关的 临床决策、患者咨询和量身定做放射后筛查范例。 我计划通过追求以下三个具体目标来检验我的核心假设： 目标1.确定接受放射治疗的高信心退伍军人队列 目标2.确定退伍军人事务部内第二和继发性恶性肿瘤的比率 目的3.量化辐射诱发继发性恶性肿瘤的遗传危险因素 为了实现这些目标，我将首先实施、验证和应用自动剂量 来自退伍军人事务部数据仓库(CDW)的国家级队列数据的量化工具， 要提取放射治疗的详细信息，如日期、方式、剂量和分割等 临床上重要的放射治疗变量。然后我将确定新的癌症诊断 在最初的癌症治疗后，进行第二次癌症风险的倾向匹配 接受或未接受放射治疗的退伍军人。此外，我将量化第二个和 个体间推定的继发性恶性肿瘤发病率是估计积分的函数 辐射剂量。使用百万退伍军人计划(MVP)的基因数据，我将测量 退伍军人队列中基因变异的丰富及其潜在的功能意义 伴随着辐射引起的继发性恶性肿瘤。我还将确定可能的DNA修复缺陷和 患有第二癌症的退伍军人中已知的癌症易感基因的其他罕见变异。 我完成了目标1、2和3中描述的研究，预计将建立一个 详细了解辐射诱发恶性肿瘤的遗传危险因素。此外，这些 AIMS将建立一个非常有价值的退伍军人队列，接受精心策划的放射治疗和次要治疗 恶性肿瘤信息，以及相应的生殖系遗传数据。最终，这些 预计资源和结果将对当前的辐射风险产生深远影响 评估框架，通过加深我们对个体之间相互作用的理解 遗传和个性化风险。

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.