Biomarkers of Cerebral Cavernous Angioma with Symptomatic Hemorrhage (CASH)

脑海绵状血管瘤伴症状性出血的生物标志物 项目摘要 脑海绵状血管瘤（CA）是一种毛细血管微血管病，影响超过一百万美国人， 使他们有过早脑出血的危险只有不到20万人患有 近期症状性出血（SH）最有可能再次出血，并伴有严重的临床后遗症， 治疗发展的主要焦点。CA的遗传机制已被广泛研究， 神经血管单元中随之发生的信号畸变。这些包括增殖性血管生成障碍，血液- 脑屏障通透性过高，炎症和免疫介导的过程，抗凝血管域， 和肠道微生物群驱动机制。反映这些机制和措施的分子的血浆水平 磁共振成像（MRI）显示的血管通透性和出血渗漏与 初步研究中的CA出血。希望优化这些生物标志物以准确诊断CASH， 预测未来SH的风险，并监测出血后和治疗后的病例。这将 影响临床管理，选择风险较高的病例进行临床试验。其他候选生物标志物 正在进行的机制和差异转录组研究，这将有望 进一步提高CASH诊断和预测的敏感性和特异性。加权组合 血浆蛋白和特征性微核糖核酸（miRNA）水平可能进一步加强生物标志物 协会.血浆生物标志物可能反映（并可能取代）更麻烦和昂贵的成像 用于监测CA出血的生物标志物。我们在这里聚集了领先的临床CA研究人员，并建议 部署先进的统计和计算生物学方法（包括监督机器学习）， 整合新的候选生物标志物，拒绝不相关的候选物，并确定最佳的 组合生物标志物贡献的聚类和加权。在具体目标1中，我们评估了这些生物标志物， 来自多个研究中心的大型CA队列，以发现最佳血浆生物标志物，并在性别、年龄和 相关临床亚组。在特定目标2中，我们比较了MRI测量血管通透性的变化 和出血的血浆生物标志物。在具体目标3中，我们询问了非CA中的生物标志物 受试者，以确定临床背景下的潜在混杂因素。该项目利用现有的 CA研究联盟，并整合分析和计算生物学专业知识，开发血液检测， 更好的诊断和预后。该项目测试了一种新型生物标志物整合方法 在相关使用背景下发生机械定义的脑血管疾病。这种方法 适用于具有相似病理生物学特征的其他神经系统疾病。

Biomarkers of Cerebral Cavernous Angioma with Symptomatic Hemorrhage (CASH) PROJECT SUMMARY Cerebral cavernous angioma (CA) is a capillary microangiopathy affecting more than a million Americans, predisposing them to a premature risk of brain hemorrhage. Fewer than 200,000 cases who have suffered a recent symptomatic hemorrhage (SH) are most likely to re-bleed again with serious clinical sequelae, and are the primary focus of therapeutic development. Genetic mechanisms of CA have been extensively studied, and consequent signaling aberrations in the neurovascular unit. These include proliferative dysangiogenesis, blood- brain barrier hyperpermeability, inflammation and immune mediated processes, anticoagulant vascular domain, and gut microbiome-driven mechanisms. Plasma levels of molecules reflecting these mechanisms and measures of vascular permeability and hemorrhage leak on magnetic resonance imaging (MRI) have been correlated with CA hemorrhage in pilot studies. It would be desirable to optimize these biomarkers to accurately diagnose CASH, to prognosticate the risk of future SH, and to monitor cases after a bleed and in response to therapy. This would influence clinical management, and select higher risk cases for clinical trials. Additional candidate biomarkers are emerging from ongoing mechanistic and differential transcriptome studies, which would be expected to further enhance the sensitivity and specificity of diagnosis and prediction of CASH. Weighed combinations of levels of plasma proteins and characteristic micro-ribonucleic acids (miRNA) may further strengthen biomarker associations. Plasma biomarkers may reflect (and potentially replace) more cumbersome and expensive imaging biomarkers for monitoring CA hemorrhage. We here assemble leading clinical CA researchers and propose to deploy advanced statistical and computational biology approaches (including supervised machine learning) for the integration of novel candidate biomarkers, rejecting non-correlated candidates, and determining the best clustering and weighing of combined biomarker contributions. In Specific Aim 1 we assess these biomarkers in a large CA cohort from multiple sites, to discover the best plasma biomarkers and validate them in sex, age and relevant clinical subgroups. In Specific Aim 2 we compare changes in MRI measures of vascular permeability and hemorrhage with plasma biomarkers over time. In Specific Aim 3 we query the biomarkers in non-CA subjects, to identify potential confounders in the clinical context. This project leverages the synergy of established CA research consortia, and integrates analytic and computational biology expertise to develop blood tests for better CASH diagnosis and prognosis. The project tests a novel integrational approach of biomarker development in a mechanistically defined cerebrovascular disease with a relevant context of use. This approach is applicable to other neurological diseases with similar pathobiologic features.