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

项目摘要/摘要 阿尔茨海默病(AD)是最常见的神经退行性疾病。大脑中的病理变化可以 至少在临床症状出现前15年观察(临床前期)。一种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成本。此外，有效的治疗策略可以改善 如果及早分娩，临床结果。显然有必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人和在早期症状阶段识别患者 疾病的威胁。这些新的生物标志物也可以被用来监测疾病的进展和对 治疗。无细胞核酸诊断测试给产前筛查和癌症研究带来了革命性的变化， 诊断和治疗。此外，还对从无细胞rna中确定的特定转录本进行了评估。 作为AD的生物标志物，但到目前为止，还没有尝试高通量的方法。这项提议的目标是 利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。我假设在无浆细胞的核酸中有可检测到的变化 与AD相关的基因。在K99阶段，我的目标是使用无细胞核酸准确预测AD病例 以及生物信息学工具，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 采集AD患者和对照组的血浆样本，建立预测模型。我将在一个 临床前样本的独立数据集。我将包括突变携带者和非欧洲人的样本 先辈来验证模型。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产物来确定它是否是AD特有的。 我的初步数据表明，这种方法是可行的。我用公元10年设计了一个初步的预测模型 ROC曲线下的面积为1的病例和10个对照；然后我在独立的样本中进行复制 20例，ROC曲线下面积为0.84。在4个临床前样本中，ROC为0.86，表明 我的模型还可以识别出有症状的个体。可以通过使用更多的 强大的信息学方法。使用深度神经网络，我在发现数据集中的ROC为1 和0.94在复制数据集中。在R00阶段，我计划在其他 神经退行性疾病设计特定的预测模型。我将在RNA上生成序列数据 帕金森病和路易痴呆患者和对照纵向血浆样本中的存在 为每一个身体构建特定的预测模型。然后我会在临床前复制这些模型 这些疾病的样本。综合所有神经退行性疾病的信息也将使我能够 改进预测模型并执行综合分析以描述机械洞察力。我的终极目标 是能够将预测模型用作诊断工具，并在可能的情况下用作早期诊断测试。这个 初步数据令人鼓舞，并为进行基于血浆的神经退化测试打开了可能性。

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.