Biomarkers of Cerebral Cavernous Angioma with Symptomatic Hemorrhage (CASH) - Supplemental

伴有症状性出血的脑海绵状血管瘤 (CASH) 的生物标志物 - 补充

• 批准号: 10841770
• 负责人: ISSAM A AWAD
• 金额: $ 20.18万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-15 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10841770
• 关键词: Address; Affect; Age; American; Artificial Intelligence; Authorization documentation; Benchmarking; Benign; Biological Assay; Biological Markers; Blood Tests; Blood capillaries; Brain hemorrhage; Cavernous Hemangioma; Cerebrovascular Disorders; Cerebrum; Clinical; Clinical Trials; Code; Communication; Complement; Complex; Computational Biology; Data; Data Analyses; Data Element; Data Science; Data Set; Databases; Diagnosis; Diagnostic; Disease; Doctor of Philosophy; Elements; Enrollment; Funding; Future; Goals; Grant; Hemorrhage; Image; Intuition; Lesion; Link; Machine Learning; Methods; MicroRNAs; Multiomic Data; Names; National Institute of Neurological Disorders and Stroke; Paper; Patients; Plasma; Plasma Proteins; Proteins; Publishing; RNA; Readiness; Research; Research Personnel; Risk; Sampling; Site; Standardization; Structure; Subgroup; Testing; Uncertainty; United States National Institutes of Health; Validation; Vascular Diseases; Work; accurate diagnosis; authority; biomarker development; biomarker discovery; candidate marker; clinical application; clinical biomarkers; clinical center; clinically relevant; cohort; data harmonization; data sharing; data structure; deep learning; design; extracellular; high risk; learning community; machine learning algorithm; metabolome; metabolomics; microbiome; multiple omics; nervous system disorder; novel; novel marker; parent project; permissiveness; premature; prognostic; prognostication; programs; recruit; repository; repository infrastructure; response; sex; shared repository; statistics; supervised learning; symposium; therapeutic development; transcriptome; trial readiness; usability

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 (CASH) are most likely to re-bleed again with serious clinical sequelae, and are the primary focus of trial readiness and therapeutic development. Candidate biomarkers are emerging from ongoing mechanistic and differential transcriptome studies, which enhance the diagnosis and prediction of CASH, influence clinical decisions, and help stratify high-risk cases in clinical trials. An ongoing project (R01NS114552) has assembled leading clinical CA researchers to deploy advanced computational biology approaches, including supervised machine learning (ML), to discover and validate novel candidate biomarkers. It aims to determine the best clustering and weighing of combined biomarker contributions for optimal diagnostic and predictive accuracy. Initially aimed at combining levels of candidate proteins and microRNAs, recent discoveries have compelled the inclusion of circulating metabolites with mechanistic links, which demonstrate strong diagnostic and prognostic associations in discovery cohorts. The best weighted combination of plasma molecules will be tested in a large validation cohort already recruited, to assess their relevance in sex, age, relevant clinical subgroups and multiple recruitment sites. The project tests a novel integrational approach of biomarker development in a mechanistically defined cerebrovascular disease with a relevant context of use. While applicable to other neurological diseases, the implementation of our data for ML analyses, and its use and usability by other investigators, are limited by inconsistent structure of shared data in current repositories. Furthermore, there is a lack of harmonization of data elements or intuitive connectivity of multiomic data elements from the same patients. This problem is amplified with the addition of metabolites to our multiomic biomarker candidates. In response to NOSI NOT-OD-23-082, we have assembled a team with expertise in computational biology, clinical biomarker research, data science and statistics, who will work on solutions to address these issues. First, we will share each type of raw data in structured repositories that match each type of assay and data type. We also designed a database under best practices for data structure, standardization, and naming conventions that we will share through Dryad, which will include data that is ready for ML and other AI and Deep learning implementations. We further propose creating a GitHub repository that will include a detailed description of the data in Dryad and the codes used for the ML implementation in our study. We will connect the different submissions to the repositories to facilitate the use of more than one data type from the same subjects. Lastly, in addition to publishing the primary results of ML multiomic data analyses from this project, we propose to publish a methods paper on connected shared data structure for readiness in future ML, artificial intelligence and deep learning analyses by other investigators.

概括 脑海绵状血管瘤（CA）是一种影响超过一百万美国人的毛细血管病， 使他们过早出现脑出血的风险。遭受感染的病例不到20万 近期症状性出血 (CASH) 最有可能再次出血并出现严重的临床后遗症，并且 试验准备和治疗开发的主要焦点。候选生物标志物不断涌现 正在进行的机制和差异转录组研究，增强了诊断和预测 CASH，影响临床决策，并帮助在临床试验中对高风险病例进行分层。正在进行的项目 (R01NS114552) 召集了领先的临床 CA 研究人员来部署先进的计算生物学 包括监督机器学习 (ML) 在内的方法来发现和验证新型候选生物标志物。 其目的是确定组合生物标志物贡献的最佳聚类和权重，以实现最佳诊断 和预测的准确性。最初旨在结合候选蛋白质和 microRNA 的水平，最近 发现迫使循环代谢物包含了机械联系，这表明 发现队列中具有很强的诊断和预后相关性。等离子的最佳加权组合 分子将在已经招募的大型验证队列中进行测试，以评估它们在性别、年龄、 相关的临床亚组和多个招募地点。该项目测试了一种新颖的集成方法 具有相关使用背景的机械定义的脑血管疾病的生物标志物开发。 虽然适用于其他神经系统疾病，但我们的 ML 分析数据的实施及其使用和 其他研究人员的可用性受到当前存储库中共享数据不一致结构的限制。 此外，缺乏数据元素的协调或多组数据元素的直观连接 来自同一位患者。随着我们的多组学生物标志物中添加代谢物，这个问题变得更加严重 候选人。为了响应 NOSI NOT-OD-23-082，我们组建了一支具有计算专业知识的团队 生物学、临床生物标志物研究、数据科学和统计学，他们将致力于解决这些问题的解决方案 问题。首先，我们将在与每种分析类型相匹配的结构化存储库中共享每种类型的原始数据 数据类型。我们还根据数据结构、标准化和命名的最佳实践设计了一个数据库 我们将通过 Dryad 分享的约定，其中包括为 ML 和其他 AI 和 Deep 做好准备的数据 学习实施。我们进一步建议创建一个 GitHub 存储库，其中包含详细描述 Dryad 中的数据以及我们研究中用于 ML 实现的代码。我们将连接不同的 向存储库提交内容，以方便使用来自同一主题的多种数据类型。最后， 除了发布该项目的 ML 多组学数据分析的主要结果之外，我们还建议 发表一篇关于互联共享数据结构的方法论文，为未来的机器学习、人工智能和 其他研究人员的深度学习分析。

项目成果

Perfusion and Permeability MRI Predicts Future Cavernous Angioma Hemorrhage and Growth.

灌注和渗透性 MRI 可预测未来海绵状血管瘤的出血和生长。

• 发表时间: 2022-05
• 作者: Sone, Je Yeong;Hobson, Nicholas;Srinath, Abhinav;Romanos, Sharbel G;Li, Ying;Carrión;Shkoukani, Abdallah;Stadnik, Agnieszka;Piedad, Kristina;Lightle, Rhonda;Moore, Thomas;DeBiasse, Dorothy;Bi, Dehua;Shenkar, Robert;Carroll, T
• 通讯作者: Carroll, T

A Roadmap for Developing Plasma Diagnostic and Prognostic Biomarkers of Cerebral Cavernous Angioma With Symptomatic Hemorrhage (CASH).

• DOI: 10.1093/neuros/nyaa478
• 发表时间: 2021-01-19
• 期刊: Neurosurgery
• 影响因子: 4.8
• 作者: R. Girard;Yan Li;Agnieszka Stadnik;R. Shenkar;Nicholas Hobson;Sharbel Romanos;Abhinav Srinath;Th
• 通讯作者: Th