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

项目总结/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）仍然是一个主要的 发病和死亡的原因。在美国，每年有近100万成年人患有MI，约1/3的 这是经常性的。首次MI存活的患者复发事件的风险较高。在那些发展出 复发性MI，年死亡率增加到10%，比首次MI后高两倍。虽然很多工作 已经做了确定常见的遗传变异的事件冠状动脉疾病，有一个很大的差距， 关于哪些变异导致复发性MI的文献。虽然可能存在重叠，但存在不同的生物学特征。 动脉粥样硬化的发展和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MI后人群中测试了一种经过验证的事件CAD全基因组多基因风险评分，发现它确实 不能预测心肌梗死复发这一发现形成了他的中心假设，即复发性MI的遗传预测因子不同， 从那些预测CAD事件的人身上。直到最近，还没有数据集来检验这一假设。但大型 来自MI后患者的随机临床试验的大量基因分型和测序数据现在是可用的， 允许发现和临床应用与复发性MI相关的生殖系和体细胞突变。 在这项提案中，马斯顿博士将利用TIMI研究组的9项心血管临床试验来测试他的 三个具体目标。首先，马斯顿博士将定义常见的遗传变异的作用， 使用14K例复发性MI和69K例MI对照预测复发性MI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进行鉴定，并验证 他们预测首次心肌梗死患者风险的能力。第二，马斯顿博士将确定是否罕见 使用9K中的外显子组测序数据，遗传变异或克隆造血与复发性MI相关 复发性MI病例和39K MI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 5种二级预防治疗之间的治疗相互作用（多基因风险评分）和2种 CHIP的二级预防治疗。基因x治疗相互作用的存在可以用来告知 治疗决策和定制治疗。这项工作将在心血管科进行。 医学在布里格姆妇女医院，一个核心教学医院的哈佛医学院。马斯顿医生 将在TIMI研究小组主席Marc Sabatine博士的指导下进行研究， 博士布罗德研究所心血管疾病倡议主任帕特里克埃林诺说。马斯顿医生的目标是 成为一名遗传流行病学家和临床试验家，有能力建立大型重要数据集并使用 最先进的计算方法，以确定预防和治疗CAD的新见解。他 致力于成为一名独立的调查员，并将他的K08工作作为R01的基础， 他将应用遗传风险标记来改善临床试验设计，并优化心血管疾病的精确度。 在乎

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.