Plasma Cell-Free RNA as Non-invasive Biomarker for Neurodegeneration

血浆游离 RNA 作为神经退行性疾病的非侵入性生物标志物

• 批准号: 10582001
• 负责人: Laura Ibanez
• 金额: $ 24.9万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10582001
• 关键词: African American; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease diagnostic; Alzheimer' s disease model; Alzheimer’s disease biomarker; Amyloid beta-Protein; Amyloid deposition; Area; Autopsy; Award; Bioinformatics; Biological; Biological Markers; Brain; Cells; Cerebrospinal Fluid; Cerebrospinal Fluid Proteins; Clinical; Complex; Custom; Data; Data Set; Dementia; Dementia with Lewy Bodies; Deposition; Diagnosis; Diagnostic tests; Differential Diagnosis; Disease; Disease Progression; Early Diagnosis; Economic Burden; Fetal Development; Forensic Medicine; Genes; Goals; High-Throughput Nucleotide Sequencing; Human; Imaging Techniques; Individual; Informatics; Intervention; Knowledge; Machine Learning; Malignant Neoplasms; Medical Care Costs; Mentors; Methods; Modeling; Monitor; Nerve Degeneration; Neurodegenerative Disorders; Nucleic Acids; Nucleotides; Outcome; Parkinson Disease; Parkinson' s Dementia; Participant; Pathogenesis; Pathologic; Pathway interactions; Patients; Phase; Plasma; Plasma Cells; RNA; ROC Curve; Reagent; Research Design; Sampling; Screening procedure; Specificity; Study Subject; Symptoms; Techniques; Testing; Time; Training; Transcript; abeta deposition; accurate diagnosis; alpha synuclein; anticancer research; base; bioinformatics tool; blood-based biomarker; burden of illness; case control; cohort; cost effective; cost effectiveness; deep neural network; design; diagnostic screening; diagnostic tool; digital; disease-causing mutation; disorder control; feature selection; improved; innovation; insight; longitudinal dataset; minimally invasive; mutation carrier; nano-string; neuropathology; novel diagnostics; novel marker; pre-clinical; predictive modeling; predictive tools; prenatal testing; presenilin-1; presenilin-2; prognostic tool; skills; therapeutically effective; tool; trait; transcriptome; transcriptome sequencing; transcriptomics; treatment response

Project Summary / Abstract Alzheimer disease (AD) is the most common neurodegenerative disorder. Pathological changes in the brain can be observed at least 15 years before clinical symptoms (preclinical stage). An early and accurate diagnosis tool could save $7.9 trillion in medical and care costs. Moreover, an effective therapeutic strategy could improve the clinical outcome if delivered early. There is a clear need to develop cost-effective and non-invasive biomarkers for AD that can be used to identify individuals before symptoms emerge and patients at early-symptomatic stages of disease. These novel biomarkers could be also leveraged to monitor disease progression and responses to therapies. Cell-free nucleic acids diagnostic tests have revolutionized prenatal screening, and cancer research, diagnosis and treatment. Furthermore, specific transcripts ascertained from cell-free RNA have been evaluated as biomarkers for AD, but so far, no high throughput approach has been attempted. The goal of this proposal is to use high throughput sequencing of cell-free nucleic acids from plasma to construct a prediction model for neurodegenerative diseases. I hypothesize that there are detectable changes in plasma cell-free nucleic acids that are related to AD. During the K99 phase, I aim to predict accurately AD cases using cell-free nucleic acid and bioinformatics tools, including machine learning. Briefly, I will sequence cell-free RNA present in longitudinal samples of plasma from AD cases and controls, then build a predictive model. I will replicate this model in an independent dataset of preclinical samples. I will include samples from mutation carriers and non-European ancestry to validate the model. I will also determine if the model can predict other neurodegenerative diseases or if it is specific to AD by quantifying plasma transcripts from patients with other neurodegenerative diseases. My preliminary data show that this approach is feasible. I designed a preliminary predictive model with 10 AD cases and 10 controls that has an area under the ROC curve of 1; then I replicated it in independent samples (n=20) with an area under the ROC curve of 0.84. In four preclinical samples the ROC was 0.86 suggesting that my model can also identify pre-symptomatic individuals. It is possible to improve this model by using more powerful informatics approaches. Using deep neural networks, I obtained a ROC of 1 in the discovery dataset and 0.94 in the replication dataset. During the R00 phase, I plan to use the same approach on other neurodegenerative diseases to design specific predictive models. I will generate sequence data on the RNA present in longitudinal plasma samples of cases and controls from Parkinson’s disease and dementia with Lewy bodies to construct specific predictive models for each of them. Then I will replicate the models in preclinical samples of these diseases. Combining the information on all neurodegenerative diseases will also allow me to refine the predictive model and perform integrative analyses to describe mechanistic insights. My ultimate goal is to be able to use the predictive models as diagnostic tools, and if possible, as early diagnostic tests. The preliminary data is encouraging and opens the possibility of having plasma-based tests for neurodegeneration.

项目概要/摘要 阿尔茨海默病（AD）是最常见的神经退行性疾病。大脑的病理变化可以 至少在出现临床症状之前 15 年进行观察（临床前阶段）。早期准确的诊断工具 可以节省 7.9 万亿美元的医疗和护理费用。此外，有效的治疗策略可以改善 早期分娩的临床结果。显然需要开发具有成本效益和非侵入性的生物标志物 对于 AD，可用于在症状出现之前识别个体以及处于早期症状阶段的患者 的疾病。这些新型生物标志物还可用于监测疾病进展和对疾病的反应 疗法。无细胞核酸诊断测试彻底改变了产前筛查和癌症研究， 诊断和治疗。此外，还评估了从无细胞 RNA 中确定的特定转录本 作为 AD 的生物标志物，但迄今为止，尚未尝试过高通量方法。该提案的目标是 使用血浆中无细胞核酸的高通量测序来构建预测模型 神经退行性疾病。我假设血浆游离核酸存在可检测到的变化 与 AD 有关的。在K99阶段，我的目标是使用无细胞核酸准确预测AD病例 和生物信息学工具，包括机器学习。简而言之，我将对纵向存在的无细胞 RNA 进行测序 从 AD 病例和对照中提取血浆样本，然后建立预测模型。我将在一个模型中复制这个模型 临床前样本的独立数据集。我将包括来自突变携带者和非欧洲人的样本 祖先来验证模型。我还将确定该模型是否可以预测其他神经退行性疾病 或者通过量化其他神经退行性疾病患者的血浆转录物来确定 AD 的特异性。 我的初步数据表明这个方法是可行的。我用10 AD设计了一个初步的预测模型 ROC 曲线下面积为 1 的病例和 10 个对照；然后我将其复制到独立样本中 (n=20)，ROC 曲线下面积为 0.84。在四个临床前样本中，ROC 为 0.86，表明 我的模型还可以识别出现症状前的个体。可以通过使用更多来改进该模型 强大的信息学方法。使用深度神经网络，我在发现数据集中获得了 1 的 ROC 复制数据集中为 0.94。在R00阶段，我计划在其他方面使用相同的方法 神经退行性疾病设计特定的预测模型。我将生成 RNA 的序列数据 存在于帕金森病和路易痴呆症病例和对照的纵向血浆样本中 机构为每个人构建特定的预测模型。然后我将在临床前复制模型 这些疾病的样本。结合所有神经退行性疾病的信息也将使我能够 完善预测模型并进行综合分析以描述机械见解。我的最终目标 是能够使用预测模型作为诊断工具，如果可能的话，作为早期诊断测试。这 初步数据令人鼓舞，并开启了基于血浆的神经变性测试的可能性。