Proteomic Profiling of Idiopathic Pulmonary Fibrosis Progression Trajectory

项目摘要 特发性肺纤维化（IPF）是一种破坏性的间质性肺疾病，其特征是进行性肺纤维化， 功能下降生存比大多数癌症更差。尽管死亡率高，但IPF进展的轨迹是 高度异质性。这种异质性阻碍了药物的开发，因为当需要大样本量时， 进行IPF临床试验，以确保有足够的患者经历用力肺活量（FVC）下降，以检测 治疗效果。然而，预测IPF进展轨迹的能力仍然难以捉摸。新兴 蛋白质组学平台为解决这一知识缺口提供了宝贵的机会。在目标1中，我们将验证 IPF进展的蛋白质组学生物标志物。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。 对一项发现中确定的一年分类IPF进展的258种初步生物标志物进行有针对性的分析 来自肺纤维化基金会的队列（n=813）。在目标2中，我们将推导并验证一个蛋白质组 FVC轨迹的特征。机器学习将应用于半定量蛋白质组学数据， 用于定量特征开发的蛋白质。将开发一个定制的定量平台， 量化选定的蛋白质，数据用于开发一年FVC轨迹的蛋白质组学特征。这 然后将在三个前瞻性招募的IPF队列中评估签名。在目标3中，我们将确定 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 在240例初治患者中生成，并在80例患者接受 吡非尼酮、尼达尼布或无治疗。生物标志物浓度和检测性能的纵向变化 将在抗纤维化治疗开始之前和之后比较治疗组之间的特征。成功 该提案的完成将确定IPF进展的新型分子介导物， 预测IPF进展轨迹的显著生物标志物特征。这种工具有很大的潜力， 通过临床试验丰富开发，使精准医疗在IPF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.