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(LUT)治疗费用昂贵，并显著影响患者的生活质量。这个 确定LUTS的亚型对于了解LUTS的病理生理和有效的临床治疗是至关重要的 病人的管理和治疗。新颖的工具，可以准确识别存在、类型和 LUTS的严重性是必要的，生物标记物就是这样一种工具。最近系统地回顾了 LUTS的生物标记物表明在已发表的研究中存在重要的知识缺口：差 重复性差，LUTS患者分类不明确，临床协变量缺乏调整。这个 建立了下尿路功能障碍症状研究网络(LURN)，目标是 识别和解释LUTS患者的临床相关亚型。超过75,000个生物样本 收集并存储在NIDDK存储库，包括1200多个患者和 对照组，以及纵向收集的800多名患者的血清样本。我们建议分析 Lurn采集的血浆和血清样本以鉴定LUTS亚型及其生物标志物特征 对治疗的反应。我们假设：(A)LUT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)生物标记物的特征不仅在LUT的不同亚型之间会有所不同，而且 也在改良剂和非改良剂的比较中；(C)生物标记物签名的纵向变化 改良剂和非改良剂对治疗的反应将不同。 我们的研究包括三个具体目标。目的1.确定包含在 LUTS男性和女性亚组的血浆水平。我们建议分析一组1306种蛋白质 高灵敏度和可重复性的SomaScan分析，我们成功地在Biomarker试点项目中使用了它 在Lurn I中，这一目标是为了通过区分识别来产生LUT的全面表型 不同亚型LUTS患者体内蛋白质含量丰富。我们将在Lurn I的结果基础上再接再厉 并使用生物标记物数据来提炼我们之前识别的基于症状的男性和女性亚型 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。 目的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中执行的测试结果以测试生物标记物签名之间的显著差异 “最好的改进者”和“最差的非改进者”。这一点具有实际意义，因为它将有助于及早选择 可能从LUTS的更密集或替代治疗中受益的患者。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 在患有LUTS的男性和女性中。结合现有的代谢和代谢的生物学知识 使用富集化分析的信号通路这将允许识别与改进的 并将有助于了解LUTS的病理生理学和开发更好的治疗方法。

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.