Early CSF detection of FTLD

描述(由申请人提供)：额颞叶变性(FTLD)是65岁以下受试者中导致痴呆症的第二大常见原因。FTLD主要有两种亚型：TDP-43免疫反应阳性的FTLD(FTLD-TDP)和Tau免疫反应阳性的FTLD(FTLD-Tau)。目前，FTLD的确切病理直到尸检才能确定，迫切需要能够可靠预测FTLD病理的生前生物标志物。在过去的30个月里，我已经识别和验证了一组脑脊液(CSF)FTLD-TDP生物标志物，它们对FTLD-TDP患者的识别准确率为85%，包括Tau磷酸化、三肽基肽酶1(TPP1)水平和炎症蛋白水平(Fas、嗜酸性粒细胞趋化蛋白-3、IL-23)，在区分两种主要FTLD亚型方面的准确率为85%。这些脑脊液改变的识别和验证将作为当前提议的基础，该提议通过多点技术验证和基于机器学习的高级分类将这些标记转化为临床应用，并更好地表征FTLD-TDP中改变的生化途径。首先，我假设我们可以通过使用60个储存的脑脊液样本进行多点技术验证，将每个FTLD-TDP生物标记物的变异系数降低到15%以下。我将在目标1中通过确定降低检测精度的技术因素(冻融、血液污染、洗涤剂使用、蛋白酶抑制剂使用、缓冲条件)并验证宾夕法尼亚大学的检测来检验这一假设。其次，我假设基于机器的学习/支持向量机方法将通过考虑年龄、性别和病程的非线性影响来更好地诊断FTLD-TDP。我将在目标2中通过比较更成熟的算法和支持向量机算法的分类性能来测试这一假设。最后，我假设这些生物标记物的变化反映了FTLD-TDP中生化途径的改变。我将通过测量大脑和脑脊液中参与Tau磷酸化、TPP1成熟和炎症的蛋白质水平来检验这一假设。作为一个探索性的子目标3a，我将确定目标1和目标3的任何变化在携带家族性FTLD-TDP突变的无症状受试者中是否可检测到，以支持R01应用于前驱FTLD-TDP受试者的纵向脑脊液生物标记物变化。我的指导团队包括Allan Levey医学博士(Emory)、John Trojanowski医学博士(Penn)、James Lah医学博士(Emory)、Jonathan Glass医学博士(Emory)、Leslie Shaw博士(Penn)和Eva Lee博士(佐治亚理工学院)。我还将接受生物统计学、生物信息学、分析化学、质量控制、临床试验设计、老年病学和负责任的研究指导方面的正式培训。成功完成当前提案中的目标将为临床建立标准程序 翻译有希望的FTLD-TDP生物标记物，确定使用这些生物标记物诊断FTLD-TDP的最佳算法，并识别与这些生物标记物变化相关的脑脊液和脑通路的改变。

DESCRIPTION (provided by applicant): Frontotemporal lobar degeneration (FTLD) is the second most common cause of dementia among subjects under the age of 65. There are two main FTLD subtypes: FTLD associated with lesions immunoreactive to TDP-43 (FTLD-TDP), and FTLD associated with Tau-immunoreactive lesions (FTLD-Tau). Currently, the exact FTLD pathology cannot be confidently defined until autopsy, and there is an urgent need for ante- mortem biomarker which reliably predict FTLD pathology. Over the past 30 months, I have identified and validated a panel of cerebrospinal fluid (CSF) FTLD-TDP biomarkers that identified FTLD-TDP patients with 85% accuracy, including Tau phosphorylation, tripeptidyl peptidase 1 (TPP1) levels, and inflammatory protein levels (FAS, eotaxin-3, IL-23), with 85% accuracy in distinguishing between the two main FTLD subtypes. The identification and validation of these CSF alterations will serve as the basis of the current proposal to translate these markers towards clinical application through multi-site technical validation and advanced classification through machine-based learning, and to better characterize altered biochemical pathways in FTLD-TDP. First, I hypothesize that we can reduce the coefficient of variation for each FTLD-TDP biomarker to under 15% through a multi-site technical validation using 60 banked CSF samples. I will test this hypothesis in Aim 1 by determining technical factors (freeze-thawing, blood contamination, detergent use, protease inhibitor use, buffer condition) which reduce assay precision, and validating the assay at Penn. Second, I hypothesize that machine-based learning/support vector machine approach will better diagnose FTLD-TDP by taking into account non-linear effects of age, gender, and disease duration. I will test this hypothesis in Aim 2 by comparing the classification performance between more established algorithms and the support vector machine algorithm. Lastly, I hypothesize that these biomarker changes reflect altered biochemical pathways in FTLD-TDP. I will test this hypothesis by measuring brain and CSF levels of proteins involved in Tau phosphorylation, TPP1 maturation, and inflammation. As an exploratory Sub-aim 3a, I will determine if any of the changes from Aims 1 & 3 are detectable in asymptomatic subjects carrying familial FTLD-TDP mutations, to power an R01 application on longitudinal CSF biomarker changes in prodromal FTLD-TDP subjects. I will carry out this proposal with guidance from my mentoring team including Allan Levey, MD, PhD (Emory), John Trojanowski, MD, PhD (Penn), James Lah, MD, PhD (Emory), Jonathan Glass, MD (Emory), Leslie Shaw, PhD (Penn), and Eva Lee, PhD (Georgia Tech). I will also obtain formal training in biostatistics, bioinformatics, analytical chemistry, quality control, clinical trial design, geriatrics, and responsible conduct of research. Successful completion of the aims in the current proposal will establish the standard procedures to clinically translate promising FTLD-TDP biomarkers, determine the best algorithm to diagnose FTLD-TDP using these biomarkers, and identify altered CSF and brain pathways related to these biomarker alterations.