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.

摘要 脑海绵状血管瘤(CA)是一种毛细血管微血管病，影响着100多万美国人， 使他们有过早脑出血的风险。不到200,000例患者患有 最近的症状性出血(现金)最有可能再次出血，并有严重的临床后遗症， 试验准备和治疗开发的主要重点。候选生物标志物正在从 正在进行的机制和差异转录组研究，这些研究增强了对糖尿病的诊断和预测 现金，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)已召集领先的临床CA研究人员部署先进的计算生物学 发现和验证新的候选生物标志物的方法，包括有监督的机器学习(ML)。 它的目的是确定组合生物标记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microRNAs的水平，最近 这些发现迫使循环中的代谢物与机制联系在一起，这证明了 发现队列中强烈的诊断和预后关联。血浆的最佳加权组合 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、 相关临床亚组和多个招募地点。该项目测试了一种新的集成方法 生物标记物在机械定义的脑血管疾病中的发展与相关的使用背景。 虽然适用于其他神经疾病，但我们用于ML分析的数据的实施及其使用和 其他调查人员的可用性受到当前存储库中共享数据结构不一致的限制。 此外，缺乏数据元素的协调或多个数据元素的直观连接性 来自相同的病人。随着代谢产物加入我们的多组生物标志物，这个问题被放大了 候选人。为了响应NOSI NOT-OD-23-082，我们组建了一个具有计算专业知识的团队 生物学、临床生物标记物研究、数据科学和统计学，世卫组织将致力于解决这些问题 问题。首先，我们将在结构化存储库中共享每种类型的原始数据，这些数据与每种类型的分析和 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共享的约定，其中将包括为ML和其他AI和Deep准备好的数据 学习实施。我们还建议创建一个GitHub存储库，其中将包括详细的描述 Dryad中的数据和我们研究中用于ML实现的代码。我们将把不同的 向存储库提交数据，以便于使用来自同一主题的多种数据类型。最后， 除了发表这个项目的ML多组数据分析的初步结果外，我们还建议 发布一篇关于互联共享数据结构为未来ML、人工智能和 其他调查人员的深度学习分析。

项目成果

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

• DOI: 10.1002/jmri.27935
• 发表时间: 2022-05
• 期刊: Journal of magnetic resonance imaging : JMRI
• 作者: Sone JY;Hobson N;Srinath A;Romanos SG;Li Y;Carrión-Penagos J;Shkoukani A;Stadnik A;Piedad K;Lightle R;Moore T;DeBiasse D;Bi D;Shenkar R;Carroll T;Ji Y;Girard R;Awad IA
• 通讯作者: Awad IA