Toward Diagnostics and Therapies of Molecular Subcategories of CAD

CAD 分子亚类的诊断和治疗

• 批准号: 9278295
• 负责人: JOHAN M BJORKEGREN
• 金额: $ 80.29万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9278295
• 关键词: Abdomen; Achievement; Address; Alleles; Angiography; Animal Model; Area; Atherosclerosis; Benchmarking; Biochemical Pathway; Biological Markers; Biological Models; Biology; Biopsy; Blood; Blood Vessels; Cardiology; Cardiovascular system; Cause of Death; Cell Differentiation process; Cell model; Chest; Clinical; Clinical Research; Complex; Coronary; Coronary Arteriosclerosis; Coronary Artery Bypass; DNA; DNA analysis; Data; Data Set; Development; Diagnosis; Diagnostic; Disease; Disease Pathway; Engineering; Event; Family; Fatty acid glycerol esters; Foam Cells; Foundations; Future; Genes; Genetic; Genetic Models; Genomics; Genotype; Goals; Hereditary Disease; Heritability; Hospitals; In Vitro; Individual; Inherited; Institutes; Investments; Lead; Lesion; Link; Liver; Machine Learning; Maps; Meta-Analysis; Metabolic; Modeling; Molecular; Myocardial Infarction; New York; Operative Surgical Procedures; Outcome; Pathway Analysis; Pathway interactions; Patients; Pharmacotherapy; Phenotype; Plasma; Plasma Proteins; Preventive; Preventive care; Preventive therapy; Prospective Studies; Quantitative Trait Loci; RNA; RNA Sequences; Recruitment Activity; Regulator Genes; Research; Research Proposals; Risk; Sampling; Single Nucleotide Polymorphism; Skeletal Muscle; Subcategory; System; Techniques; Testing; Tissues; Translating; Twin Multiple Birth; Twin Studies; Variant; Whole Blood; abdominal fat; biological systems; clinical predictors; clinical risk; computer based statistical methods; disorder risk; follow-up; genetic analysis; genome wide association study; in vivo Model; macrophage; medical schools; molecular phenotype; monocyte; multidisciplinary; novel; novel diagnostics; novel therapeutics; percutaneous coronary intervention; personalized care; personalized diagnostics; personalized medicine; predictive marker; prospective; protein biomarkers; public health relevance; risk variant; subcutaneous; therapeutic biomarker; therapeutic target; trait; transcriptome sequencing

 DESCRIPTION (provided by applicant): Coronary artery disease (CAD) is a leading cause of death worldwide and in the US. While the genetics of this disease are intrinsically complex, thanks to huge research investments during the last 5-10 years, particularly in genome-wide association studies (GWAS), a more unbiased, data-driven and realistic view of CAD has been achieved. As part of this achievement, ~160 common risk loci for CAD/myocardial infarction (MI) have been identified. An important task is now to understand the molecular mechanisms/pathways by which these loci exert risk for CAD/MI allowing to translating the initial findings into new therapies and diagnostics. However, since the loci identified thus far explain only ~10% of variation in CAD/MI risk, it is also essential to define additional CAD pathways operating in parallel with GWA loci. In recent years, clinical studies that consider intermediate phenotypes (between DNA and disease) have greatly enhanced interpretations of risk loci identified in GWA datasets. In addition, disease networks that can be identified from intermediate molecular phenotypes provide an essential framework to identify novel CAD pathways and targets for new CAD therapies. Over the last 6 years, we have performed a clinical study considering many intermediate phenotypes in CAD patients (the STARNET study). In this proposal we intend to use newly generated DNA genotype and RNA sequence data from the STARNET study to identify atherosclerosis and metabolic networks underlying CAD. We then propose a new prospective study of CAD (the NGS-PREDICT study) with the main purpose of validating findings from the STARNET study. We hypothesize that the extent and stability of coronary lesions, thus clinical outcomes can be accurately assessed by defining the status of key atherosclerosis gene networks. In turn, metabolic networks active in liver, abdominal fat, and skeletal muscle influence the status of the atherosclerosis gene networks. In addition, molecular data isolated from easily obtainable tissues (e.g., blood, subcutaneous fat and plasma) can be used to identify biomarkers that can predict risk for clinical events caused by CAD. To test these hypotheses, we propose the following specific aims. Aim 1: To identify regulatory Bayesian gene networks causally linked to CAD and/or CAD sub-phenotypes using the STARNET datasets and the CARDIoGRAM meta-analysis GWA datasets. Aim 2: Identify biomarkers predicting clinical events of CAD (reflected in SYNTAX score) by applying machine learning on DNA genotype, RNA sequence and CAD plasma protein data from easily obtainable tissues of the STARNET cases. Aim 3: To validate the identified causal CAD eQTLs/networks and the biomarkers using the NGS-PREDICT study performed at the Mt. Sinai Hospital, the Swedish Twin study and CAD cell and animal models. We believe the proposed studies can lead to a significantly better molecular understanding of CAD and thus, serve the more long-term goal of preventive and personalized therapies of CAD patients diagnosed in well-defined molecular subcategories.

 描述（由申请人提供）：冠状动脉疾病（CAD）是全球和美国的主要死亡原因。虽然这种疾病的遗传学本质上是复杂的，但由于过去5-10年的巨大研究投资，特别是在全基因组关联研究（GWAS）中，已经实现了对CAD的更公正，数据驱动和现实的看法。作为这一成就的一部分，已经确定了约160个CAD/心肌梗死（MI）的常见风险位点。现在的一项重要任务是了解这些基因座对CAD/MI产生风险的分子机制/途径，从而将初步发现转化为新的治疗和诊断方法。然而，由于迄今为止确定的基因座仅解释了CAD/MI风险变化的约10%，因此定义与GWA基因座平行操作的其他CAD途径也是必要的。近年来，考虑中间表型（DNA和疾病之间）的临床研究大大增强了对GWA数据集中识别的风险基因座的解释。此外，可以从中间分子表型中识别的疾病网络提供了一个必要的框架，以识别新的CAD途径和新的CAD治疗靶点。在过去的6年中，我们进行了一项考虑CAD患者中许多中间表型的临床研究（STARNET研究）。在这个提议中，我们打算使用STARNET研究中新产生的DNA基因型和RNA序列数据来识别CAD的动脉粥样硬化和代谢网络。然后，我们提出了一项新的CAD前瞻性研究（NGS-PREDICT研究），主要目的是验证STARNET研究的结果。我们假设冠状动脉病变的程度和稳定性，从而临床结果可以通过定义关键动脉粥样硬化基因网络的状态进行准确评估。反过来，在肝脏、腹部脂肪和骨骼肌中活跃的代谢网络影响动脉粥样硬化基因网络的状态。此外，从容易获得的组织（例如，血液、皮下脂肪和血浆）可用于鉴定可预测由CAD引起的临床事件的风险的生物标志物。为了验证这些假设，我们提出了以下具体目标。目标1：使用STARNET数据集和CARDIoCast荟萃分析GWA数据集识别与CAD和/或CAD亚表型因果相关的调控贝叶斯基因网络。目标二：通过对来自STARNET病例的易于获得的组织的DNA基因型、RNA序列和CAD血浆蛋白数据应用机器学习，识别预测CAD临床事件的生物标志物（反映在SYNTAX评分中）。目的3：使用在Mt.西奈医院，瑞典双胞胎研究和CAD细胞和动物模型。我们相信，拟议的研究可以导致更好地了解CAD的分子，从而为更长期的目标，即在明确定义的分子亚类中诊断的CAD患者的预防性和个性化治疗。