Accession Number:

AD1049254

Title:

Biomarker-Based Prediction Models for Response to Treatment in Systemic Sclerosis-Related Interstitial Lung Disease

Descriptive Note:

Technical Report,15 Sep 2016,14 Sep 2017

Corporate Author:

The University of Texas Health Science Center Houston United States

Personal Author(s):

• Assassi, Shervin

Report Date:

2017-10-01

Pagination or Media Count:

9.0

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 patients who are more likely to respond to treatment. 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 bloodskin gene expression data with potential clinical predictors. As the first step, key inflammatory serum proteins have been determined in the baseline samples of the Scleroderma Lung Study II SLS II. We are currently analyzing whether these serum proteins have predictive significance for response to treatment in SSc related ILD.

Descriptors:

• biological markers
• sclerosis
• lung diseases
• predictive modeling
• gene expression
• proteins
• blood serum
• ribonucleic acids
• skin
• immunochemistry
• cytokines
• immunosuppression

Subject Categories:

• Medicine and Medical Research
• Biochemistry
• Genetic Engineering and Molecular Biology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE