Novel Strategies for Blood-based Biomarkers for AD: Role of Genetic Variation in a Multivariate Framework

 描述（由申请人提供）：在没有任何成功的阿尔茨海默病（AD）治疗药物试验和有效的AD治疗方法的情况下，在无症状或前驱期早期检测AD至关重要，以增加减少不可逆脑损伤的机会，减缓疾病进展，并在任何干预试验中产生更好的结果。来自MRI、PET、CSF和外周血的几种生物标志物显示其作为诊断、预后或治疗反应生物标志物的潜力。其中，基于血液的生物标志物，特别是来自外周血的蛋白质组分析物，由于其微创性和易于获得的收集方法，具有作为AD筛查工具的巨大潜力，并且将对研究和最终的临床护理产生非凡的影响，以促进早期干预的研究。在过去的几年中，已经进行了许多研究，以研究一组测量的血浆（血清）蛋白质组学分析物水平和一组AD相关表型之间的直接关系，使用各种测定技术。由于与样品收集、储存和分析相关的一些技术挑战，以前的研究不能充分研究血浆蛋白质组学数据的潜力，导致相对较弱的重复性。除了这些技术问题之外，迄今为止的研究忽略了两个非常重要的因素：与每种蛋白质分析物相关的遗传变异的影响和多分析物蛋白质组数据的相关结构。最近，我们已经表明，遗传变异显著影响血浆蛋白质组分析物水平的疾病状态无关。因此，在评估蛋白质组学数据的诊断和预后意义时，应考虑基因变异。弱复制的另一个潜在原因可能是单个分析物的诊断能力相对较弱，因为大多数研究集中在每个测量的蛋白质分析物水平对一组给定表型的影响上。在许多情况下，多分析物分子生物标志物彼此相关，并且大多数使用血浆蛋白质组学分析物的研究在评估其诊断潜力时没有考虑分析物之间的相关结构。尽管单个分析物的诊断能力可能较弱，但鉴定一组具有生物学意义的分析物集合的多变量方法可以更好地阐明血浆蛋白质组特征的诊断和预后能力。在这项拟议的研究中，我们将通过应用先进的分析方法，包括多维数据简化，基因途径富集分析，成像遗传学，监督和无监督学习，通过多变量方法来评估血浆蛋白质组分析物的潜力，以解决这两个知识差距。该项目的结果可能对蛋白质组分析物作为诊断、预后或治疗反应生物标志物的识别和评价策略产生变革性影响，并且提出的先进分析策略可应用于除AD外的各种神经退行性疾病的基于液体的多分析物数据。

 DESCRIPTION (provided by applicant): Without any successful drug trials for Alzheimer's disease (AD) treatment and proven efficient therapies for AD, early detection of AD in asymptomatic or prodromal stages is critically important in order to increase chances of reducing irreversible brain damages, slowing down disease progression, and producing better results in any intervention trials. Several biomarkers from MRI, PET, CSF, and peripheral blood show their potential as diagnostic, prognostic, or therapeutic response biomarkers. Among them, blood-based biomarkers, especially proteomic analytes from peripheral blood have great potential as AD screening tools due to its minimal invasiveness and easy access of collection method and would have an extraordinary impact on research and eventually clinical care to facilitate the investigation of early intervention. During the last couple of years, many studies have been performed to investigate the direct relationship between a set of measured plasma (serum) proteomics analyte levels and a set of AD relevant phenotypes using various assay technologies. Due to several technical challenges related to sample collection, storage and assay, previous studies couldn't fully investigate the potential of plasma proteomics data, resulting in relatively weak replications. Beyond these technical issues, previous studies to date overlooked two very important factors: influence of genetic variations associated with each protein analyte and correlational structure of multi-analyte proteomic data. Recently we have shown that genetic variation substantially influences plasma proteomic analyte levels independent of disease status. Therefore, in assessing the diagnostic and prognostic significance of proteomic data, gene variants should be taken into account. Another potential reason of weak replication could be relative weak diagnostic power of individual analytes because the majority of investigation focused on the effect of each measured protein analyte level on a set of given phenotype. In many cases, multianalyte molecular biomarkers are correlated with one another and most of studies using plasma proteomics analytes did not consider correlational structure among analytes when assessing their diagnostic potential. Although the diagnostic power of individual analyte can be weak, multivariate approaches to identify a set of biologically meaningful ensembles of analytes may better elucidate diagnostic and prognostic power of plasma proteomic signature. In this proposed study, we will address these two knowledge gaps in assessing potential of plasma proteomic analytes through a multivariate approach by applying advanced analytic methods including multi-dimensional data reduction, gene pathway-enrichment analysis, imaging genetics, and supervised and unsupervised learning. Results of this project could have a transformative impact on identification and evaluation strategies of proteomic analytes as diagnostic, prognostic, or therapeutic response biomarkers and proposed advanced analytical strategies can be applied to fluid-based multi-analyte data for various neurodegenerative disorders in addition to AD.