CTRIP: Genetic Testing to Individualize Management of Common Heart Diseases

描述（由申请人提供）：心力衰竭影响超过500万美国人，每年有超过50万新患者。在大多数情况下，由于扩张性或缺血性心肌病，心力衰竭是一种典型的“复杂疾病”，即它是多因素的，具有环境和遗传成分。尽管估计遗传学对总体心力衰竭发病机制的贡献约为30%，但散发性心力衰竭的临床管理目前并不包含遗传信息，因为改变心力衰竭风险、预后和对治疗的反应的遗传因素在很大程度上是未知的。几个最近完成或正在进行的大规模心力衰竭微阵列分析已经确定，复制和验证与缺血性或非缺血性心肌病相关的遗传多态性（SNP）。我们假设这些SNP是易患心力衰竭的非同义基因变异的标记，因此将有助于风险人群中个体的风险分层和个性化管理。因此，我们成立了来自华盛顿大学、中美心脏研究所（密苏里州大学、堪萨斯城）和宾夕法尼亚大学的WUMAP联盟，以直接检查遗传风险、遗传修饰剂和药物基因组学相互作用。总的来说，我们现有的心脏病队列总计超过7,700名受试者，他们已经或正在进行微阵列分析，以确定候选基因-疾病关联。基于这些结果，并准备第2阶段临床试验，评估遗传因素是否改善疾病监测和管理中的临床特征分析，我们将：目标1，在我们现有的研究人群中对相关基因/位点进行深度重测序，以识别可能的因果变异，并选择前瞻性研究中那些在独立队列之间验证和复制的变异。目标2，通过协调我们现有的研究数据库，在WUMAP网络中建立有效的合作，建立集中的生物标志物和遗传学实验室，创建基于Web的WUMAP数据协调中心，超声心动图和统计基因组学核心，并创建数据安全监测委员会。因此，第一阶段将使我们真正地“转动钥匙”，并推进多中心临床干预试验，以前瞻性地评估个人遗传信息作为临床数据的辅助手段在心力衰竭管理中的安全性和有效性。 公共卫生相关性：有必要对基因检测进行前瞻性评估，以确定将遗传信息添加到传统临床数据中是否可以改善收缩性心力衰竭的诊断，诊断或管理。大规模基因组学研究的前景是，未被怀疑的基因-疾病关联将确定疾病风险的标志物，并将提供对疾病发病机制的新见解，从而导致创新的治疗策略。一个有趣的可能性是，遗传数据将导致现有疗法应用于新疾病，从而“快速跟踪”从发现到新疗法的时间。

DESCRIPTION (provided by applicant): Heart failure affects more than 5 million Americans, and strikes over 500,000 new patients each year. Due in the majority of instances to dilated or ischemic cardiomyopathies, heart failure is a classic "complex disease", i.e. it is multi-factorial and has both environmental and genetic components. Despite estimates that genetics contributes ~30% to overall heart failure pathogenesis, clinical management of sporadic heart failure does not currently incorporate genetic information because the genetic factors that modify heart failure risk, prognosis, and response to therapies are largely unknown. Several recently completed or ongoing large-scale microarray analyses of heart failure have identified, replicated, and validated genetic polymorphisms (SNPs) associated with ischemic or non-ischemic cardiomyopathy. We hypothesize that these SNPs are markers for non- synonymous gene variants that predispose to heart failure, and will therefore be useful to risk-stratify and personalize management of individuals in at-risk populations. Accordingly, we have formed the WUMAP consortium from Washington University, Mid America Heart Institute (University of Missouri, Kansas City), and University of Pennsylvania to directly examine genetic risks, genetic modifiers, and pharmacogenomic interactions. Collectively, we have existing heart disease cohorts totaling over 7,700 subjects that have undergone or are currently undergoing microarray analyses to identify candidate gene-disease associations. Based on these results, and to prepare for Stage 2 clinical trials evaluating whether genetic factors improve upon clinical profiling in disease surveillance and management, we will: Aim 1, perform deep resequencing of associated genes/loci in our existing study populations to identify likely causal variants, and select for prospective study those that validate and replicate between independent cohorts. And Aim 2, develop effective collaborations across the WUMAP network by harmonizing our existing research databases, establish centralized Biomarker and Genetics Laboratories, create a web-based WUMAP Data Coordinating Center, Echocardiography and Statistical Genomics Cores, and create a Data Safety Monitoring Board. Stage 1 will thus position us literally to "turn the key" and carry forward multicenter clinical interventional trials to prospectively assess the safety and efficacy of personal genetic information, as an adjunct to clinical data, in heart failure management. PUBLIC HEALTH RELEVANCE: There is a need for prospective evaluations of genetic testing to determine if addition of genetic information to traditional clinical data can improve diagnosis, prognostication, or management of systolic heart failure. The promise of large-scale genomics studies is that unsuspected gene-disease associations will identify markers of disease risk, and will provide novel insight into disease pathogenesis leading to innovative treatment strategies. An intriguing possibility is that genetic data will lead to application of existing therapeutics to new diseases, thus "fast-tracking" the time from discovery to new therapeutics.