Proteomic Profiling of Idiopathic Pulmonary Fibrosis Progression Trajectory

项目摘要 特发性肺纤维化(IPF)是一种以进行性肺为特征的破坏性间质性肺疾病。 功能减退的存活率比大多数癌症更严重。尽管死亡率很高，但IPF的进展轨迹是 高度异质性。这种异质性阻碍了药物开发，因为在以下情况下需要大量样本 进行IPF临床试验，以确保有足够多的患者经历用力肺活量(FVC)下降以检测到 一种治疗效果。然而，预测IPF进展轨迹的能力仍然难以捉摸。新兴 蛋白质组平台为解决这一知识差距提供了宝贵的机会。在目标1中，我们将验证 IPF进展的蛋白质组生物标志物。使用高通量、半定量蛋白质组学平台，我们 将测定英国Profile队列(n=550)中约3000种分析物的血浆浓度，并执行。 一项发现的258个IPF进展一年初步生物标志物的靶向分析 来自肺纤维化基金会的队列(n=813)。在目标2中，我们将推导并验证蛋白质组 FVC轨迹的签名。将机器学习应用于半定量蛋白质组数据的选择 用于定量签名开发的蛋白质。将开发一个定制的量化平台，并用于 量化选定的蛋白质，用数据来开发一年FVC轨迹的蛋白质组学签名。这 然后将在三个预期招募的森林小组队列中对签名进行评估。在目标3中，我们将确定是否 抗纤维化治疗调节IPF进展的生物标志物。定量蛋白质组学数据将是 为240名未接受治疗的患者生成，并在每名患者接受治疗后12个月重复 吡非尼酮，9tedanib或不接受治疗。生物标志物浓度和测试性能的纵向变化 治疗组在开始抗纤维化治疗前后的特点将进行比较。成功 该提案的完成将确定IPF进展的新分子介体，并导致高度的 预测IPF进展轨迹的重要生物标志物签名。这种工具有很大的潜力来加快毒品的速度 通过临床试验丰富开发，使精准医学在特发性肺纤维化患者中成为现实。

Project Summary Idiopathic pulmonary fibrosis (IPF) is a devastating interstitial lung disease characterized by progressive lung function decline survival worse than most cancers. Despite high mortality, the trajectory of IPF progression is highly heterogeneous. This heterogeneity hampers drug development, as large sample sizes are required when conducting IPF clinical trials to ensure enough patients experience forced vital capacity (FVC) decline to detect a treatment effect. The ability to predict IPF progression trajectory, however, remains elusive. Emerging proteomic platforms provide a valuable opportunity to address this gap in knowledge. In Aim 1, we will validate proteomic biomarkers of IPF progression. Using a high-throughput, semi-quantitative proteomic platform, we will determine plasma concentration for ~3000 analytes in the UK-based PROFILE cohort (n=550) and perform. targeted analysis of 258 preliminary biomarkers of one-year categorical IPF progression identified in a discovery cohort from the Pulmonary Fibrosis Foundation (n=813). In aim 2, we will derive and validate a proteomic signature of FVC trajectory. Machine learning will be applied to semi-quantitative proteomic data to select proteins for quantitative signature development. A custom, quantitative platform will be developed and used to quantify selected proteins, with data used to develop a proteomic signature of one-year FVC trajectory. This signature will then be assessed in three prospectively recruited IPF cohorts. In aim 3, we will determine whether anti-fibrotic therapy modulates biomarkers of IPF progression. Quantitative proteomic data will be generated for 240 treatment-naïve patients and repeated at 12-months after 80 patients each received pirfenidone, nintedanib or no therapy. Longitudinal change in biomarker concentration and test performance characteristics will be compared between treatment groups before and after anti-fibrotic initiation. Successful completion of this proposal will identify novel molecular mediators of IPF progression and result in a highly significant biomarker signature to predict IPF progression trajectory. This tool has high potential to speed drug development through clinical trial enrichment, making precision medicine a reality in patients with IPF.