Proteomics of human vitreous to investigate mechanisms underlying the variability in anti-VEGF treatment response in neovascular AMD patients

项目总结 老年性黄斑变性(AMD)是导致老年人严重、不可逆性视力丧失的主要原因 世界各地的成年人。AMD的大部分视力丧失是由晚期新生血管疾病引起的 (NVAMD)。玻璃体内注射抗血管内皮生长因子(抗血管内皮生长因子)有效地保存了 对许多患者的视力有影响，但NVAMD患者的治疗反应存在显著差异。 导致抗血管内皮生长因子反应的这种差异性的分子因素是知识上的一个关键差距。 我们的建议通过利用一种独特而强大的资源直接解决了这一知识差距。我们 拥有从1100多名患者的办公室内收集的已确认玻璃体样本的独家存储库 治疗视网膜疾病，包括许多患有NVAMD的人。我们将把这一资源与高度敏感的 基于高通量多重免疫分析的蛋白质组学技术鉴定玻璃体相关蛋白 抗血管内皮细胞生长因子治疗NVAMD有效。我们假设玻璃体炎症水平的变化 而血管生成蛋白影响的基本机制是基础的可变性的反应 血管内皮生长因子治疗。为了验证这一假设，我们将测量参与炎症和 NVAMD患者治疗前和治疗过程中玻璃体标本中的血管生成 治疗。我们已经确定了83名接受标准3种治疗的单纯NVAMD患者的队列 每月玻璃体内注射贝伐单抗，然后根据视力需要每月注射 视力、眼底检查和OCT评估。使用视力和OCT测量，每个患者的 初级(三次负荷量后1个月；第3个月)和继发性(开始治疗后6个月； 6个月)抗血管内皮生长因子治疗反应分为良好、部分、差或无反应。 在目标1中，我们将使用Olink蛋白质组学的多重免疫分析来测量涉及的733个蛋白质的水平 NVAMD患者玻璃体标本中炎症和血管生成的变化 贝伐单抗注射剂。为了比较部分响应者和非响应者的优劣，我们将 根据以下基准水平对应答者状态(良好/部分或差/无反应)进行逻辑回归 每个蛋白质在第一和第二响应时间点都有和没有对协变量进行调整。 在目标2中，我们将确定贝伐单抗引起的玻璃体水平的纵向变化 炎症和血管生成蛋白与NVAMD患者的临床结局相关。对于其中的58个 来自AIM 1的患者，我们在1个月(治疗后1个月)收集了额外的玻璃体样本 开始)、3个月和6个月。我们将量化相同的733个炎症和血管生成蛋白的水平 在这些纵向的玻璃样本中。我们将比较蛋白质的折叠变化和蛋白质的趋势 在每个响应时间点，良好/部分响应者和较差/无响应者之间的时间变化。

PROJECT SUMMARY Age-related macular degeneration (AMD) is the leading cause of severe, irreversible vision loss in older adults worldwide. Most vision loss in AMD is caused by the advanced neovascular form of the disease (NVAMD). Intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections are effective in preserving vision for many patients, but there is marked variability among NVAMD patients in treatment response. Molecular factors contributing to this variability in anti-VEGF response represent a critical gap in knowledge. Our proposal addresses this knowledge gap directly by leveraging a unique and powerful resource. We have an exclusive repository of deidentified vitreous samples collected in-office from more than 1,100 patients treated for retinal diseases, including many with NVAMD. We will couple this resource with a highly sensitive, high-throughput multiplex immunoassay-based proteomics technology to identify vitreous proteins associated with anti-VEGF treatment response in NVAMD. We hypothesize that variation in vitreous levels of inflammatory and angiogenic proteins impacts the fundamental mechanisms that underlie the variability in response to anti- VEGF treatment. To test this hypothesis, we will measure levels of proteins involved in inflammation and angiogenesis in vitreous samples collected from NVAMD patients both prior to and throughout the course of treatment. We have identified a cohort of 83 treatment-naïve NVAMD patients who received the standard three monthly loading doses of intravitreal bevacizumab, followed by monthly injections as needed based on visual acuity, fundus examination, and OCT assessment. Using visual acuity and OCT measurements, each patient’s primary (one month after three loading doses; Month 3) and secondary (6 months after treatment initiation; Month 6) anti-VEGF treatment responses were classified as Good, Partial, Poor, or Non-Response. In Aim 1, we will use Olink Proteomics’ multiplex immunoassays to measure levels of 733 proteins involved in inflammation and angiogenesis in vitreous samples collected from these NVAMD patients prior to their initial bevacizumab injection. To compare between Good+Partial Responders and Poor+Non-Responders, we will perform logistic regression of responder status (Good/Partial or Poor/Non-Response) against baseline levels of each protein with and without adjustment for covariates at both primary and secondary response timepoints. In Aim 2, we will determine the longitudinal bevacizumab-induced changes in vitreous levels of inflammatory and angiogenic proteins that correlate with clinical outcomes in NVAMD patients. For 58 of the patients from Aim 1, we have additional vitreous samples collected at Month 1 (one month after treatment initiation), Month 3, and Month 6. We will quantify levels of the same 733 inflammatory and angiogenic proteins in these longitudinal vitreous samples. We will compare the protein fold-changes and the trend of protein changes over time between Good/Partial Responders and Poor/Non-Responders at each response timepoint.