Identifying multimodal biomarkers for autologous serum tears in the treatment of chronic postoperative ocular pain

项目总结/摘要 白内障手术是55岁及以上患者最常见的眼科手术。大约10 全世界每年进行数百万例白内障手术。慢性术后眼痛（CPOP） 估计在这些患者中约有18%发生。随着社会老龄化，白内障手术的数量和 因此，CPOP的发病率预计在未来20年内将翻一番。CPOP严重影响 生活质量下降，经济负担沉重。CPOP的有效治疗方法仍然是 缺乏开发这种治疗方法的一个主要限制是无法客观地衡量它们的疗效。 方式自体血清泪液（AST）疗法已成为最有效的治疗方法之一， CPOP。然而，对AST的反应在个体患者中差异很大。因此，迫切需要 建立稳健的生物标志物，以预测治疗结果（应答者vs非应答者）并监测 对治疗的反应我们的目标是发现和验证治疗反应和反应监测 白内障手术后CPOP患者AST治疗的生物标志物。为了实现这些目标，我们 组建了一个跨学科的团队（眼科，疼痛管理，生物信息学，数据科学， 免疫学和分子生物学）来发现患者中对AST治疗的应答的多模式生物标志物 CPOP使用最先进的机器学习，将临床病史、问卷调查和 来自疼痛部位（眼睛）和治疗来源（血清）的分子生物标志物。我们会评估基因， 转录组学、代谢组学等等。机器学习将被用来确定一组简约的 用于表征AST反应的非侵入性生物标志物。我们将通过两个具体目标实现我们的目标： (1)开发白内障手术后CPOP中AST治疗反应的多模式生物标志物（UG 3 Discovery 阶段）;和（2）在第一个随机对照试验中验证目标1中确定的多模式生物标志物 评估白内障手术后CPOP中AST与安慰剂的比较（UH 3验证阶段）。这项提议将产生 客观和稳健的生物标志物特征，以促进CPOP患者的AST治疗分层。在 除了本研究将对创建精确方法以指导临床管理的影响外， CPOP患者，该提案的方法和结果将提供一个框架，以识别和 在许多其他外科专业中验证慢性术后疼痛的客观生物标志物。

PROJECT SUMMARY/ABSTRACT Cataract surgery is the most common eye procedure in patients aged 55 years and above. Approximately 10 million cataract surgeries are performed annually in the world. Chronic postoperative ocular pain (CPOP) is estimated to develop in about 18% of these patients. As society ages, the number of cataract surgeries and subsequently the incidence of CPOP are expected to double in the next 20 years. CPOP substantially affects the quality of life and is associated with enormous economic burden. Effective treatments for CPOP are still lacking. One major limitation in developing such treatments is the inability to gauge their efficacy in an objective manner. Autologous serum tear (AST) therapy has emerged as one of the most highly efficacious treatments for CPOP. However, responses to AST vary vastly among individual patients. Thus, there is an urgent need to establish robust biomarkers to predict the therapeutic outcomes (responder vs non-responder) and to monitor response to treatment. Our goals are to discover and validate treatment response and response monitoring biomarkers for AST treatment in patients with CPOP after cataract surgery. To meet these goals, we have assembled an interdisciplinary team (ophthalmology, pain management, bioinformatics, data science, immunology, and molecular biology) to discover multimodal biomarkers of response to AST therapy in patients with CPOP using state-of-the art machine-learning that integrates clinical histories, questionnaires, and molecular biomarkers from the site of pain (eye) and the source of treatment (serum). We will assess genetics, transcriptomics, metabolomics, among others. Machine learning will be used to determine a parsimonious set of non-invasive biomarkers for characterizing AST response. We will achieve our goal through two specific aims: (1) develop multimodal biomarkers of AST treatment response in CPOP after cataract surgery (UG3 Discovery Phase); and (2) validate the multimodal biomarkers identified in Aim 1 in the first randomized controlled trial evaluating AST versus placebo in CPOP after cataract surgery(UH3 Validation Phase). This proposal will yield objective and robust biomarker signatures to facilitate AST treatment stratification in patients with CPOP. In addition to the impact this study will have on creating precision approach to guide the clinical management of patients with CPOP, the approaches and findings from this proposal will provide a framework to identify and validate objective biomarkers for chronic postoperative pain in many other surgical specialties.