Identification of biomarker signatures of subtypes of LUTS and their response to treatments using samples from LURN study

项目摘要 下尿路症状（LUTS）治疗费用昂贵，并严重影响患者的生活质量。的 确定LUTS的亚型对于了解LUTS的病理生理学和有效的临床治疗至关重要。 患者的管理和治疗。新的工具，可以准确地识别存在，类型， 需要确定LUTS的严重程度，生物标记物就是这样一种工具。近期系统性综述 LUTS的生物标志物表明在已发表的研究中存在重要的知识差距： 重复性、LUTS患者的分类不明确以及缺乏对临床协变量的调整。的 下尿路功能障碍的症状研究网络（LURN）的目标是建立 识别和解释临床相关的LUTS患者亚型。超过75，000份生物样本 收集并储存在NIDDK储存库，包括超过1200份患者基线血浆样本， 对照，以及纵向收集的800多名患者的血清样品。我们建议分析 在LURN中采集血浆和血清样本，以鉴定LUTS亚型的生物标志物特征及其 对治疗的反应。我们假设：（a）LUTS的亚型将具有不同的生物标志物特征， 差异丰富的蛋白质;（B）生物标志物特征不仅在LUTS的亚型之间不同， 也是在改良剂和非改良剂的比较中;（c）生物标志物特征的纵向变化， 改善者和非改善者对治疗的反应不同。 我们的研究包括三个具体目标。目标1.为了鉴定包含在 患有LUTS的男性和女性亚组的血浆。我们建议使用该方法分析一组1306种蛋白质， 高度灵敏和可重复的SomaScan检测，我们成功地将其用于生物标志物试点项目 在卢恩岛这一目的是通过鉴别LUTS的差异， 在不同亚型的LUTS患者中，我们将在LURN I成果的基础上再接再厉， 并使用生物标志物数据来完善我们先前确定的基于性别的男性和女性亚型， LUTS。这对于更好地对LUTS患者进行分类、诊断和个性化治疗非常重要。 目标2.确定基线血浆样本中与LUTS改善相关的生物标志物。我们将使用 在目标1中进行的试验结果，以测试以下生物标志物特征之间的显著差异： “最佳改进者”和“最差非改进者”。这是有实际意义的，因为它将有助于早期选择 可能受益于LUTS的更强化或替代治疗的患者。目标3.量化 “最佳改善者”和“最差非改善者”血清中生物标志物特征的纵向变化 男性和女性的LUTS。结合现有的代谢生物学知识， 使用富集分析的信号传导途径，这将允许鉴定与改善的 症状，并将有助于了解LUTS的病理生理学和更好的治疗方法的发展。

Project Summary Lower urinary tract symptoms (LUTS) are costly to treat and significantly affect patients' quality of life. The identification of subtypes of LUTS is critical to the understanding of LUTS pathophysiology and effective clinical management and treatment of patients. Novel tools that can accurately identify the presence, types, and severity of LUTS are needed, and biological markers are one such type of tool. Recent systematic review of biomarkers of LUTS demonstrated the existence of important knowledge gap in published research: poor reproducibility, unclear classification of LUTS patients, and lack of adjustments for clinical covariates. The Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) was established with the goal of identifying and explaining clinically relevant subtypes of LUTS patients. Over 75,000 biosamples were collected and stored at NIDDK repository, including over 1200 baseline plasma samples for patients and controls, as well as serum samples collected longitudinally for more than 800 patients. We propose to analyze plasma and serum samples collected in LURN to identify biomarker signatures of subtypes of LUTS and their response to treatments. We hypothesize that: (a) subtypes of LUTS will have different biomarker signatures of differentially abundant proteins; (b) biomarker signatures will differ not only across the subtypes of LUTS but also in comparison of improvers and non-improvers; (c) longitudinal changes in biomarker signatures in response to treatment will be different in improvers and non-improvers. Our study consists of three specific aims. Aim 1. To identify protein biomarker signatures contained within plasma of subgroups of men and women with LUTS. We propose to analyze a panel of 1306 proteins using the highly sensitive and reproducible SomaScan assay, which we successfully used in the Biomarker Pilot Project in LURN I. This aim serves to produce comprehensive phenotyping of LUTS by identifying differentially abundant proteins among patients with different subtypes of LUTS. We will build upon the results of LURN I and use biomarker data to refine our previously identified symptom-based subtypes of men and women with LUTS. This is important for better classification, diagnosis, and personalized treatment of patients with LUTS. Aim 2. To identify biomarkers in baseline plasma samples associated with improvement in LUTS. We will use the results of assay performed in Aim 1 to test for significant differences between the biomarker signatures of “best improvers” and “worst non-improvers”. This is of practical importance since it will facilitate early selection of patients who may benefit from more intensive or alternative treatments to LUTS. Aim 3. To quantify longitudinal changes in biomarker signatures within serum of “best improvers” and “worst non-improvers” among men and women with LUTS. Combined with the existing biological knowledge of metabolic and signaling pathway using enrichment analysis this will allow identifying pathways correlated with the improved symptoms and will help in understanding of LUTS pathophysiology and development of better treatments.