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

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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.