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 related ILD. The currently available clinical markers are inadequate for identifying the likely responders. The utilized treatments are also associated with potentially serious adverse events, and their use should be reserved for highly responsive patients, further underscoring the critical need for development of reliable prediction tools. Our goal is to develop prediction tools using a combination of serum biomarkers and whole blood/skin gene expression data with potential clinical predictors. Serum based candidate biomarkers have been identified in the Scleroderma Lung Study II and replicated in an observational cohort. These serum biomarkers are independent of clinical predictors. Peripheral blood gene expression modules predictive of ILD course have been also identified.
Document Details
- Document Type
- Technical Report
- Publication Date
- Oct 01, 2020
- Accession Number
- AD1123339
Entities
People
- Shervin Assassi
Organizations
- University of Texas Health Science Center at Houston