Genetic, Genomic, and Imaging Biomarkers in Degenerative Dementia

退行性痴呆的遗传、基因组和影像生物标志物

• 批准号: 7814082
• 负责人: Giovanni Coppola
• 金额: $ 45.5万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7814082
• 关键词: Address; Aging; Algorithms; Area; Biological Markers; Blood; Blood specimen; California; Central Nervous System Diseases; Classification; Clinical; Clinical Trials; Collaborations; Complex; Computer software; DNA; Data; Data Analyses; Data Set; Dementia; Diagnosis; Diagnostic; Dimensions; Disease; Early Diagnosis; Functional disorder; Funding Mechanisms; Gene Expression; Genes; Genetic; Genetic Markers; Genomics; Genotype; Goals; Grant; Hybrids; Image; Link; Liquid substance; Memory; Methods; Molecular; Network-based; Neurodegenerative Disorders; Occupations; Pathway Analysis; Patients; Performance; Peripheral; Phenotype; Process; Public Health; Recovery; Research Personnel; Running; SNP genotyping; Sampling; San Francisco; Series; Signal Transduction; Single Nucleotide Polymorphism; Source; Stratification; Study Subject; Therapeutic; Therapy Evaluation; Tissues; United States National Institutes of Health; Universities; Variant; Weight; Work; age related; base; cohort; comparative effectiveness; cost; effectiveness research; genetic association; genetic risk factor; genetic variant; improved; innovation; neuroimaging; neuropsychological; novel; peripheral blood; prognostic; public health relevance; tool; trait

DESCRIPTION (provided by investigator): This application addresses the Broad Challenge Area 05 (Comparative Effectiveness Research) and specific Challenge Topic 05-AG-103 (Imaging and Fluid Biomarkers for Early Diagnosis and Progression of Aging- related Diseases and Conditions including Neurodegenerative Diseases). In collaboration with the Memory and Aging Center at the University of California San Francisco (UCSF-MAC), we have been collecting - as part of ongoing studies - DNA, RNA, and gene expression data from peripheral blood in a large cohort of patients with degenerative dementia and controls. This series is extremely well characterized, with a large body of clinical, neuropsychological, and imaging data. Our approach is based on the hypothesis that the genetic component associated with degenerative dementia is reflected in peripheral tissues, such as peripheral blood, and can be captured by gene expression studies to be used as an intermediate phenotype for genetic association studies. Our group and others at UCLA are developing novel data analysis methods (iWGCNA), which provide a powerful framework for integrating genetic marker data, gene expression data, and complex phenotypes. Responding to the Challenge Areas and specific Challenge Topics identified by the NIH we propose to: 1) obtain genotyping data on a large series of patients with degenerative dementia and controls, where gene expression and longitudinal imaging and neuropsychological data are already available, and develop a biomarker set and classifier; 2) to build a classifier based on imaging data collected on the same subjects; 3) to compare the genetic/genomic and the imaging classifiers, and build a composite classifier and assess whether performance using the two different sources of information together is better than either alone. We expect that a composite, multi-modal biomarker set will perform better than either method alone, and will be the first multi-modal classifier in dementia, based solely on these biomarkers. The overarching goal of this work is to identify surrogate biomarkers in order to build a diagnostic classifier that would be invaluable in early and accurate diagnosis, as well as for sample stratification for clinical trials. This project leverages our collaboration with the UCSF-MAC by adding a new dimension to the phenotypic and genetic data obtained via other long-term funding mechanisms in one of the largest and best-characterized AD/FTLD series in the world. Since most of the phenotype, gene expression, and imaging data are already available, only a relatively marginal effort and cost is required to extend the genetic characterization of this cohort with genotyping data, providing a unique opportunity and niche for this Challenge Grant mechanism. The work proposed here is ready to begin, and will result in immediate job creation and retention consistent with the goals of the Recovery Act. PUBLIC HEALTH RELEVANCE: Identification of biomarkers enriching diagnostic, prognostic, and therapeutic capabilities is an important goal in dementia. We propose to build a molecular classifier based on peripheral blood samples and imaging data from demented patients. This would be a valuable tool for biomarker identification, improved patients classification, therapy evaluation, and to further our understanding of disease pathophysiology.

描述（由研究者提供）：本申请解决了广泛的挑战领域 05（比较有效性研究）和具体的挑战主题 05-AG-103（用于早期诊断和衰老相关疾病和病症（包括神经退行性疾病）进展的成像和流体生物标志物）。作为正在进行的研究的一部分，我们与加州大学旧金山分校记忆与衰老中心 (UCSF-MAC) 合作，从一大群退行性痴呆患者和对照患者的外周血中收集 DNA、RNA 和基因表达数据。该系列的特征非常明确，拥有大量临床、神经心理学和影像学数据。我们的方法基于这样的假设：与退行性痴呆相关的遗传成分反映在外周组织中，例如外周血，并且可以通过基因表达研究捕获，用作遗传关联研究的中间表型。我们的团队和加州大学洛杉矶分校的其他人正在开发新颖的数据分析方法（iWGCNA），该方法为整合遗传标记数据、基因表达数据和复杂表型提供了强大的框架。针对 NIH 确定的挑战领域和具体挑战主题，我们建议：1）获取大量退行性痴呆患者和对照患者的基因分型数据，其中基因表达、纵向成像和神经心理学数据已经可用，并开发生物标志物集和分类器； 2）根据同一对象收集的成像数据构建分类器； 3）比较遗传/基因组和成像分类器，并构建复合分类器并评估同时使用两种不同信息源的性能是否优于单独使用任一信息源。我们预计复合的多模态生物标志物集将比单独的任何一种方法表现更好，并且将成为第一个仅基于这些生物标志物的痴呆症多模态分类器。这项工作的总体目标是识别替代生物标志物，以建立一个诊断分类器，该分类器对于早期准确诊断以及临床试验的样本分层具有非常重要的价值。该项目利用我们与 UCSF-MAC 的合作，为通过其他长期资助机制获得的表型和遗传数据添加了新的维度，这是世界上最大、特征最好的 AD/FTLD 系列之一。由于大多数表型、基因表达和成像数据已经可用，因此只需相对少量的努力和成本即可通过基因分型数据扩展该队列的遗传特征，从而为该挑战资助机制提供了独特的机会和利基。这里提议的工作已经准备好开始，并将立即创造和保留就业机会，符合《复苏法案》的目标。 公共卫生相关性：识别生物标志物以丰富诊断、预后和治疗能力是痴呆症的一个重要目标。我们建议根据痴呆患者的外周血样本和成像数据建立一个分子分类器。这将是生物标志物识别、改进患者分类、治疗评估以及进一步了解疾病病理生理学的宝贵工具。