Derivation and validation of a clinical-molecular signature to predict fibrotic progression and treatment response in patients with autoimmune interstitial lung disease

项目摘要 进行性纤维化间质性肺病(PF-ILD)是一种破坏性的疾病，其特征是 实质破坏，肺功能下降，最终死亡。自身免疫性ILD(AILD)是主要原因 但AILD患者PF-ILD的自然病程尚不清楚。免疫抑制剂 (IS)疗法通常用于治疗aILD，因为实质炎症通常先于肺纤维化。的 那些接受IS治疗的AILD患者，有些会有良好的反应，而另一些则会发展成PF-ILD。因为 肺纤维化是一个不可逆转的过程，迫切需要更好地了解肺间质纤维化，明确 那些最有可能发展为这种表型并为这一群体建立最佳治疗方法的患者。 我们最近确定了一些PF-ILD的循环血浆生物标志物，现在已经产生了 令人兴奋的初步数据显示了临床分子特征(CMS)，包括聚合的临床和 血浆生物标记物数据预测不同的PF-ILD风险，并对aILD患者作出反应。优化和 这些发现的验证将促进我们对PF-ILD的理解，并对其进行深入的治疗 对该领域的影响。此应用程序的目标是优化和验证我们的Pf-CMS初步CMS- ILD和IS在一个大型、多中心、国际AILD队列中做出反应。我和我的合作调查员都有专业知识 在翻译ILD研究中，包括血浆生物标记物的研究。所有需要的血浆样本 已经收集了建议书，强调了我们方法的可行性。 我们将首先在一个表型良好的多中心aILD队列中描述PF-ILD的自然历史 (n=2000)。我们将确定短期强迫肺活量下降和长期死亡率的患病率。 将确定这些终点的临床预测因素，并在独立的aILD队列中验证我们的发现。下一首, 我们将推导并验证PF-ILD的CMS。将使用一个定量的多路平台来确定血浆 浓度为炎症和纤维化相关生物标志物。使用一年免预付款 以生存为主要终点，将进行Logistic回归以确定独立的临床和 生物标志物是预测结果的指标。使用回归点估计推导出PF-ILD的CMS，然后 在三个独立的aILD队列中进行了验证。最后，我们将推导并验证IS响应的CMS。vbl.使用 将进行交互作用建模以确定IS效应的独立临床和分子预测因子 修改。将使用回归点估计得出IS响应的CMS，然后在三个阶段进行验证 独立的aILD队列，包括两个预期收集的临床试验队列。 成功完成本提案将导致预期的CMS验证，目标是开发 首个支持aILD临床决策的分子诊断工具。这项工作将允许快速 识别疾病短期进展的高风险患者，并指导治疗选择。

Project Summary Progressive fibrosing interstitial lung disease (PF-ILD) is a devastating condition characterized by parenchymal destruction, lung function decline and ultimately death. Autoimmune ILD (aILD) is a leading cause of PF-ILD, but the natural history of PF-ILD in those with aILD has yet to be characterized. Immunosuppressant (IS) therapy is generally used to treat aILD, as parenchymal inflammation often precedes pulmonary fibrosis. Of those aILD patients treated with IS, some will respond favorably, while others will develop PF-ILD. Because pulmonary fibrosis is an irreversible process, there exists a critical need to better understand PF-ILD, identify those most likely to develop this phenotype and establish an optimal treatment approach for this group. We recently identified a number of circulating plasma biomarkers of PF-ILD and have now generated exciting preliminary data that show a clinical-molecular signature (CMS) comprised of aggregated clinical and plasma biomarker data predicts differential PF-ILD risk and IS response in those with aILD. Optimization and validation of these findings would advance our understanding of PF-ILD and have profound treatment implications for the field. The objective of this application to optimize and validate our preliminary CMS of PF- ILD and IS response in a large, multi-center, international aILD cohort. My co-investigators and I have expertise in translational ILD research, including plasma biomarker investigation. All plasma samples needed for this proposal have been collected, underscoring the feasibility of our approach. We will first characterize the natural history of PF-ILD in a well-phenotyped, multi-center aILD cohort (n=2000). We will determine the prevalence of short-term forced vital capacity decline and long-term mortality. Will identify clinical predictors of these endpoints and validate our findings in an independent aILD cohort. Next, we will derive and validate a CMS of PF-ILD. A quantitative, multiplex platform will be used to determine plasma concentration for 64 relevant biomarkers of inflammation and fibrogenesis. Using one-year progression-free survival as the primary endpoint, logistic regression will be performed to identify independent clinical and biomarker predictors of outcome. A CMS of PF-ILD will be derived using regression point estimates and then validated in three independent aILD cohorts. Finally, we will derive and validate a CMS of IS response. Using Interaction modeling will be performed to identify independent clinical and molecular predictors of IS effect modification. A CMS of IS response will be derived using regression point estimates and then validated in three independent aILD cohorts, including two prospectively collected IS clinical trial cohorts. Successful completion of this proposal will lead to prospective CMS validation with the goal of developing the first molecular diagnostic tool to support clinical decision making in aILD. This work will allow for rapid identification of patients at high-risk of short-term disease progression and guide therapeutic selection.