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

 描述(申请人提供)：在没有任何成功的治疗阿尔茨海默病(AD)的药物试验和被证明有效的AD疗法的情况下，在无症状或前驱症状阶段及早发现AD至关重要，以增加减少不可逆转的脑损伤、减缓疾病进展和在任何干预试验中产生更好结果的机会。来自MRI、PET、脑脊液和外周血的几种生物标记物显示了它们作为诊断、预后或治疗反应生物标记物的潜力。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，由于其创伤小，收集方法容易获得，作为AD筛查工具具有巨大的潜力，将对研究和最终临床治疗产生非凡的影响，促进早期干预的研究。在过去的几年里，许多研究利用各种检测技术来研究一组测量的血浆(血清)蛋白质组学分析物水平与一组AD相关表型之间的直接关系。由于样品采集、储存和检测方面的一些技术挑战，以前的研究不能完全调查血浆蛋白质组数据的潜力，导致重复性相对较弱。除了这些技术问题，到目前为止，以前的研究忽略了两个非常重要的因素：与每个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，我们发现遗传变异显著影响血浆蛋白质组分析物水平，而与疾病状态无关。因此，在评估蛋白质组数据的诊断和预后意义时，应该考虑基因变异。复制能力弱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于每个测量的蛋白分析物水平对一组给定表型的影响。在许多情况下，多分析物分子生物标记物相互关联，大多数使用血浆蛋白质组学分析器的研究在评估其诊断潜力时没有考虑分析物之间的关联结构。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能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、基因途径浓缩分析、成像遗传学以及监督和非监督学习，通过多变量方法解决这两个评估血浆蛋白质组分析潜力的知识差距。该项目的结果可能会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估策略产生革命性影响，所提出的先进分析策略可应用于除AD之外的各种神经退行性疾病的基于流体的多分析物数据。

 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.