Biomarker-Based Prediction Models for Response to Treatment in Systemic Sclerosis Related Interstitial Lung Disease
Abstract
Systemic sclerosis (SSc-Scleroderma) is associated with substantial morbidity and mortality. Interstitial lung disease (ILD) is the leading cause of disease-related mortality. Response to immunosuppression is highly variable in patients with SSc-ILD. The currently available clinical markers are inadequate for identifying the likely responders. Our goal is to develop prediction tools using a combination of molecular biomarkers with potential clinical predictors. Serum based candidate biomarkers have been identified in the Scleroderma Lung Study II and replicated in an observational cohort. The predictive significance of these serum biomarkers was independent of clinical predictors. Peripheral blood gene expression modules predictive of ILD course were also identified. Moreover, gene expression changes ensuing from immunosuppressive treatment were characterized. The results of this project build the basis for prediction tools that can transform our current one-size fits all approach, enabling the timely initiation of the most effective treatment in SSc-ILD.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2021
- Accession Number
- AD1152297
Entities
People
- Shervin Assassi
Organizations
- University of Texas Health Science Center at Houston