Predicting Response to Treatment in Systemic Sclerosis-Related Interstitial Lung Disease in a Multicenter Observational Cohort

Systemic sclerosis (SSc) is a multi-organ, autoimmune disease that is associated with high disease burden. One of the main disease manifestations is interstitial lung disease (ILD; also called pulmonary fibrosis), which leads to thickening of air sac walls in the lung tissue. ILD results in decreased lung volumes and hinders efficient oxygen exchange. Initially, affected patients have only shortness of breath with exertion. As the lung disease progresses, they even experience shortness of breath at rest. Thus, advanced ILD can result in substantial limitations during activities of daily living and can severely impair quality of life in persons with SSc. Furthermore, ILD can lead to decreased survival and is the primary cause of disease-related death in SSc. Based on the results of a clinical trial called Scleroderma Lung Study II, mycophenolate mofetil has become the most commonly used medication for treatment of SSc-related ILD. This medication is immunosuppressive and therefore dampens the immune response. However, individual patients show a variable response to this medication. Some show significant improvement in their lung function while others will continue experiencing worsening disease, despite treatment. Furthermore, this medication can be associated with significant side effects, such as serious infections, and should be reserved for patients who will be likely responders. The currently available clinical information is not sufficient for predicting response to mycophenolate mofetil. Moreover, the U.S. Food and Drug Administration has recently approved the anti-fibrotic agent, nintedanib, and the anti-IL-6 agent, tocilizumab, for treatment of SSc-related ILD, providing accepted alternative medications for those patients who do not respond to mycophenolate mofetil. This further underscores the significant need for prediction tools that can inform the timely initiation of the most effective treatment in order to prevent irreversible lung damage. Utilizing the valuable samples collected in the Scleroderma Lung Study II and employing advanced molecular techniques, we have identified serum protein and blood cell RNA markers that predict the course of ILD in patients treated with mycophenolate mofetil. However, patients in clinical trials are enrolled based on strict inclusion and exclusion criteria. Therefore, we have to validate our findings in observational studies, which include a wide range of patients, before these potentially important discoveries can be used during routine clinical care. In this project, we plan to capitalize on the valuable clinical data and biospecimens collected in the observational, multicenter CONQUER study, to confirm the above-mentioned discoveries. The involvement of 13 U.S. specialized scleroderma centers in CONQUER makes it an unprecedented resource, as it provides access to a sufficient number of patients with SSc-related ILD from a wide geographic area. Moreover, the expertise and prior experience of the investigators will ensure that advanced molecular technology and analytic methods such as RNA sequencing, multi-level molecular and clinical data analysis, and machine learning are employed in a robust and scientifically sound manner. In order to facilitate the implementation of the developed prediction model in clinical practice, we will use blood samples for the validation of this prediction tool since blood is routinely obtained as part of clinical care in patients with SSc-ILD. The proposed research directly addresses the Scleroderma Research Program Translational Research Partnership Award Focus Areas by defining biomarkers that help informed therapeutic choices and by utilizing disease registries linked to biological samples and high-quality clinical data to understand the heterogeneity and course of disease. Given that ILD is associated with substantial disease burden and is the primary cause of death in SSc, prediction tools that replace the current one size