Molecular Characterization Of A Large Cross-Sectional And Longitudinal Collection of Patients To Investigate Disease Progression in IC/BPS

项目总结 这里提出的研究代表了我们的分子和临床表型工作在很大程度上的继续 和不同的间质性膀胱炎/膀胱痛综合征(IC/BPS)患者群体。我们呈现的是初步的 数据表明，IC/BPS至少包括两个不同的表型亚群；其中一个亚群的特征是 以膀胱为中心的疾病过程和以其他为特征的全身性疼痛综合征。重要的 症状和相关症状的可变性(即疾病的异质性以及疾病的严重性) 在IC/BPS患者中，增加了开发靶向治疗的难度。因此，我们建议 利用大量回溯性和前瞻性收集的独特临床标本(膀胱 来自&gt；400名IC/BPS患者和&gt；100名非IC对照的活检组织和外周血)以确定分子 定义患者亚组的机制。这将通过使用一种复杂的分子来实现 分析方案(加权基因共表达分析网络，WGCNA)以将转录组与 个人患者临床数据作为一种不偏不倚的手段，将患者分成临床相关的亚组 在这一过程中将确定哪些潜在的治疗靶点。 这些研究的第二个目标是，通过纵向收集的患者的基因表达谱 样本(膀胱粘膜活检)与患者临床特征的变化/进展的比较(例如： 症状持续时间，有效问卷上的疼痛和症状评分，麻醉膀胱容量)，到 确定提示IC/BPS疾病进展的分子靶点和生物学途径。 为了实现这些目标，我们提出了三个具体目标。目标1：分子鉴定 IC/BPS中与疾病进展相关的特征。300例患者的组织学分析 将提供IC/BPS中广泛的表型变异性的代表性横截面。对.的使用 无偏加权基因共表达网络分析(WGCNA)将使我们能够识别基因表达 与疾病进展相关的临床特征相关的特征。目标2：确定 表明IC/BPS疾病进展的基于血液的分子特征。为了实现这一目标，我们将 评估从SA1中相同的300名IC/BPS患者(和100名对照)采集的血液，以确定血液- 基于分子标记，与疾病严重程度相关，并指示疾病进展。我们会 还确定血液分子特征是否与膀胱粘膜分子特征相关， 这可能比目前的临床方法提供更高分辨率的表型分类。目标3： 识别预测IC/BPS疾病进展的分子标记。我们 假设IC/BPS患者膀胱粘膜中的基因表达反映了疾病状态， 随着时间的推移，个体内的变化可能预示着疾病的进展。我们将确定一组基因， 使用来自纵向患者样本的RNA-Seq数据的无偏WGCNA来预测疾病进展。

PROJECT SUMMARY The studies proposed here represent a continuation of our molecular and clinical phenotyping work in a large and diverse interstitial cystitis/bladder pain syndrome (IC/BPS) patient population. We present preliminary data suggesting that IC/BPS comprises at least two distinct phenotypic subpopulations; one characterized as a bladder-centric disease process and the other characterized as a systemic pain syndrome. The significant variability in symptoms and associated syndromes (i.e. disease heterogeneity as well as disease severity) among IC/BPS patients contributes to the difficulty in developing targeted therapeutics. Therefore, we propose to leverage a large repository of retrospectively and prospectively collected unique clinical specimens (bladder biopsy tissue and peripheral blood from >400 IC/BPS patients and >100 non-IC controls) to identify molecular mechanisms that define patient subgroups. This will be accomplished by using a sophisticated molecular profiling scheme (weighted gene co-expression analysis network, WGCNA) to correlate the transcriptome with individual patient clinical data as an unbiased means to stratify patients into clinically relevant subgroups for which potential therapeutic targets will have been identified in the process. A second objective of these studies is, through gene expression profiling of longitudinally collected patient samples (bladder mucosal biopsies) compared with change/progression in patient clinical characteristics (e.g. duration of symptoms, pain and symptom scores on validated questionnaires, anesthetic bladder capacity), to identify molecular targets and biological pathways that are suggestive of disease progression in IC/BPS. To accomplish these objectives we propose three Specific Aims. Aim 1: Identification of molecular signatures that correlate with disease progression in IC/BPS. Analysis of tissues from 300 patients will provide a representative cross-section of the extensive phenotypic variability seen in IC/BPS. The use of unbiased weighted gene co-expression network analysis (WGCNA) will allow us to identify gene expression signatures that correlate with clinical features associated with disease progression. Aim 2: Identification of a blood-based molecular signature indicative of disease progression in IC/BPS. In this Aim we will evaluate blood collected from the same 300 IC/BPS patients (and 100 controls) in SA1 to identify a blood- based molecular signature that correlates with disease severity and is indicative of disease progression. We will also determine whether blood molecular signatures correlate with bladder mucosal molecular signatures, which may provide higher resolution phenotypic classification than current clinical measures. Aim 3: Identification of molecular signatures that predict disease progression in IC/BPS. We hypothesize that gene expression in the bladder mucosa of IC/BPS patients reflects disease status and that, over time, changes within an individual may indicate disease progression. We will identify groups of genes that predict disease progression using unbiased WGCNA of RNA-Seq data from longitudinal patient samples.