Discovery and Application of Germline and Somatic Mutations for Risk Prediction and Personalized Therapy to Prevent Recurrent Myocardial Infarction

项目摘要/摘要 尽管在二级预防方面取得了重大进展，但复发性心肌梗死(MI)仍然是主要的 致病和死亡的原因。在美国，每年有近100万成年人患有心肌梗死，约占 这些都是反复发生的。首次心肌梗死后存活的患者有很高的复发风险。在那些确实发展起来的人中 复发性心肌梗死，年死亡率增加到10%，是首次心肌梗死后的两倍。虽然有很多工作 已经完成了识别偶发冠状动脉疾病的常见基因变异的工作，但在 关于哪些变异导致复发性心肌梗死的文献。虽然可能存在重叠，但有不同的生物学 动脉粥样硬化的发展与复发性心肌梗死之间的特征。在马斯顿博士的初步工作中，他 在心肌梗死后人群中测试了经过验证的事件CAD全基因组多基因风险分数，并发现确实如此 不能预测复发的心肌梗死。这一发现形成了他的核心假设，即复发性心肌梗死的遗传预测因素不同 与预测事故CAD的人不同。直到最近，还没有数据集来检验这一假设。然而，大型 来自心肌梗塞后患者的随机临床试验的大量基因分型和测序数据现在可用， 允许发现和临床应用与复发性心肌梗死相关的胚系和体细胞突变。 在这项提案中，马斯顿博士将利用TIMI研究小组的9项心血管临床试验来测试他的 三个具体目标的假设。首先，马斯顿博士将定义常见基因变异的作用，并开发 使用14K例复发心肌梗死和69K例心肌梗死对照预测复发心肌梗死的多基因风险评分。小说 复发性心肌梗死的变异将通过大规模的全基因组关联研究来确定，并验证 他们预测首次心肌梗死后患者风险的能力。其次，马斯顿博士将确定是否罕见 用9K的外显子组测序数据研究基因变异或克隆性造血与复发心肌梗死的关系 复发性心肌梗死病例和39K心肌梗死对照。第三，马斯顿博士将使用随机临床试验数据来测试基因 X治疗交互作用，涉及5种具有多基因风险评分的二级预防疗法和2种 用芯片进行二级预防治疗。基因x处理交互作用的存在可以用来通知 治疗决策和量身定制的治疗。这项工作将在心血管科进行 布里格姆妇女医院是哈佛医学院的核心教学医院。马斯顿医生 将在TIMI学习小组主席Marc Sabatine博士的指导下进行研究，以及 布罗德研究所心血管疾病倡议主任帕特里克·埃利诺博士说。马斯顿博士的目标是 成为一名遗传流行病学家和临床试验者，有能力建立大型重要的数据集和使用 最先进的计算方法，以确定预防和治疗冠心病的新见解。他 致力于成为一名独立调查员，并将他的K08工作作为R01的基础，在 他将应用遗传风险标记来改进临床试验设计并优化心血管疾病的精确度 关心。

Project Summary/Abstract Despite significant advances in secondary prevention, recurrent myocardial infarction (MI) remains a major cause of morbidity and mortality. In the U.S., nearly 1 million adults have an MI each year, approximately 1/3 of which are recurrent. Patients surviving their first MI are at a high risk of recurrent events. In those who do develop recurrent MI, the annual mortality rate increases to 10%, two-fold higher than after their first MI. While much work has been done to identify common genetic variation for incident coronary artery disease, there is a large gap in the literature as to what variants contribute to recurrent MI. While overlap likely exists, there are distinct biological features between the development of atherosclerosis and recurrent MI. In Dr. Marston’s preliminary work, he tested a validated incident CAD genome-wide polygenic risk score in a post-MI population and found that it did not predict recurrent MI. This finding formed his central hypothesis that genetic predictors for recurrent MI differ from those that predict incident CAD. Until recently, datasets did not exist to test this hypothesis. However, large amounts of genotyped and sequenced data from randomized clinical trials in post-MI patients are now available, allowing both discovery and clinical application of germline and somatic mutations associated with recurrent MI. In this proposal, Dr. Marston will leverage 9 cardiovascular clinical trials from the TIMI Study Group to test his hypothesis in three specific aims. First, Dr. Marston will define the role of common genetic variation and develop a polygenic risk score for predicting recurrent MI using 14K cases of recurrent MI and 69K MI controls. Novel variants for recurrent MI will be identified through large-scale genome-wide association studies and validated for their ability to predict risk in patients following their first MI. Second, Dr. Marston will determine whether rare genetic variation or clonal hematopoiesis is associated with recurrent MI using exome sequencing data in 9K recurrent MI cases and 39K MI controls. Third, Dr. Marston will use randomized clinical trial data to test for gene x treatment interactions across 5 secondary prevention therapies with a polygenic risk score and across 2 secondary prevention therapies with CHIP. The presence of a gene x treatment interaction can be used to inform therapeutic decision making and tailored therapy. This work will take place in the Division of Cardiovascular Medicine at Brigham and Women’s Hospital, a core teaching hospital of Harvard Medical School. Dr. Marston will perform the research under the mentorship of Dr. Marc Sabatine, Chairman of the TIMI Study Group, and Dr. Patrick Ellinor, Director of the Cardiovascular Disease Initiative at the Broad Institute. Dr. Marston’s goal is to become a genetic epidemiologist and clinical trialist with the ability to build large important datasets and use state of the art computational approaches to identify novel insights into the prevention and treatment of CAD. He is dedicated to becoming an independent investigator and using his K08 work as a foundation for an R01, in which he will apply genetic risk markers to improve clinical trial design and optimize precision in cardiovascular care.