Risk and Resilience in Pulmonary Arterial Hypertension and Genetically Susceptible Individuals

摘要 肺动脉高压（PAH）是一种罕见疾病，诊断延迟， 右心衰竭的死亡率尽管FDA批准了近十几种治疗PAH的药物，但中位生存期仅为 七年所有批准的治疗都针对三种血管舒张途径之一，没有一种是疾病 修改。这个应用程序有两个目标：1）了解动态和静态之间的关系， 分子标志物和PAH进展和恢复; 2）确定PAH风险的分子特征， 携带PAH致病突变的个体的恢复力。目前尚不清楚为什么一些处于风险中的人会发展成 PAH和其他没有。约30%的PAH患者存在BMPR 2突变，但临床 只有20%的几率。未受影响的BMPR 2突变携带者（UMCs）是一种独特的、研究不足的疾病。 这也可能为临床PAH患者的疾病轨迹提供线索。纵向自然 缺乏PAH分子谱的历史研究。PAH的大多数分子谱研究是交叉的- 这限制了对疾病进展和疾病标志物如何随时间推移而相关的理解。我们 提出了在多个时间点进行密集临床和分子表型分型的策略，以克服推论 横向研究的局限性。此应用程序将利用构建的临床和研究基础设施 在我们对PAH患者的研究中，在过去的35年里，范德比尔特。调查人员分享了一个广泛的 已发表的招募这种罕见疾病和相关UMC患者的记录。我们假设 全面了解患者和遗传易感者随时间推移的风险和恢复能力 个人将提供对疾病严重程度的深入了解，并确定PAH患者的新治疗靶点。 目标1将确定疾病进展和恢复力的静态和动态分子特征。1a：执行 在HPAH、IPAH和健康对照中进行了4次以上的系列临床、蛋白质组学和基因表达谱分析 年生物信息学和网络医学分析将识别与细胞变化相关的蛋白质和RNA 母队列和两个外部验证队列中的临床结局、功能能力和RV功能。 1b：测试添加分子风险/弹性标记物是否会改善广泛使用的PAH的性能 风险预测工具（REVEAL 2.0风险评分）。目的2将确定促进肿瘤的临床和分子因素， UMC纵向队列中PAH的弹性和易感性。UMC将接受系列临床和 分子表型如目标1中所述。反映PAH的蛋白质/基因是“风险因素”，而反映PAH的蛋白质/基因是“风险因素”。 健康人口是“恢复力因素”。将在验证中开发和测试解释模型 同伙我们将检测UMC风险和弹性特征与PAH患者临床结局的相关性 和风险预测性能。这些研究将确定PAH风险和恢复力的特征 进展和治疗，提供了个性化护理和监测的第一步， 生物数据。

ABSTRACT Pulmonary arterial hypertension (PAH) is an orphan disease with a delayed diagnosis and markedly elevated mortality from right heart failure. Despite nearly a dozen FDA-approved drugs for PAH, median survival is only seven years. All approved therapies target one of three vasodilatory pathways, and none are disease modifying. This application has two objectives: 1) Understand dynamic and static relationships between molecular markers and PAH progression and resilience; 2) Identify molecular features of PAH risk and resilience in individuals harboring a PAH-causing mutation. It is unknown why some at risk individuals develop PAH and others do not. BMPR2 mutations are present in about 30% of patients with PAH but clinical penetrance is only 20%. Unaffected BMPR2 mutation carriers (UMCs) are a unique and understudied population that may also provide clues to disease trajectory in patients with clinical PAH. Longitudinal natural history studies with molecular profiling in PAH are lacking. Most molecular profiling studies in PAH are cross- sectional which limits understanding of how disease progression and disease markers relate over time. We propose a strategy of dense clinical and molecular phenotyping at multiple timepoints to overcome inferential limitations of cross-sectional studies. This application will leverage the clinical and research infrastructure built at Vanderbilt over the past 35 years in our study of PAH patients. The investigators share an extensive published record of recruiting patients with this rare disease and related UMCs. We hypothesize that a comprehensive understanding of risk and resilience over time in patients and genetically susceptible individuals will provide insight into disease severity and identify novel therapeutic targets in patients with PAH. Aim 1 will identify static and dynamic molecular features of disease progression and resilience. 1a: Perform serial clinical, proteomic, and gene expression profiling in HPAH, IPAH, and healthy controls 3 times over 4 years. Bioinformatic and network medicine analyses will identify proteins and RNAs associated with changes in clinical outcomes, functional capacity, and RV function in the parent cohort and two external validation cohorts. 1b: Test whether adding molecular risk/resilience markers will improve the performance of a widely used PAH risk prediction tool (REVEAL 2.0 Risk Score). Aim 2 will identify the clinical and molecular factors that promote resilience and susceptibility to PAH in a longitudinal cohort of UMCs. UMCs will undergo serial clinical and molecular phenotyping as in Aim 1. Proteins/genes that mirror PAH are “risk factors” and those that mirror a healthy population are “resilience factors”. Explanatory models will be developed and tested in validation cohorts. We will test UMC risk and resilience features for associations with clinical outcomes in PAH patients and risk prediction performance. These studies will identify signatures of risk and resilience to PAH progression and penetrance, offering an initial step toward personalizing care and surveillance guided by biologic data.