# Identifying predictors of treatment response in systemic sclerosis-related interstitial lung disease

> **NIH NIH K23** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2022 · $182,066

## Abstract

Project Summary/Abstract
Interstitial lung disease (ILD) occurs in the majority of patients with systemic sclerosis (SSc) and is the leading
cause of death in SSc. While immunosuppressive therapy has become a standard of care for patients with
SSc-ILD, data from randomized controlled trials demonstrate that there are distinct clinical ILD phenotypes,
including those who experience improvement in lung function with immunosuppression and those who
experience progressive deterioration despite immunosuppression. Understanding the clinical and biological
characteristics of these distinct SSc-ILD phenotypes is central to identifying SSc-ILD patients with who are
most likely to benefit from immunosuppression therapy (i.e. cyclophosphamide, mycophenolate) and those in
whom alternative treatments (anti-fibrotics, stem cell transplant) should be considered. This proposal aims to
investigate baseline clinical and biological features of SSc-ILD patients as predictors of treatment response to
immunosuppressive therapy, and in doing so, take the first steps towards developing a precision medicine
model for managing patients with this disabling and often fatal disease. This study will first explore whether a
focused group of proteins measured in bronchalveolar lavage and plasma/serum samples are correlated with
with surrogate measures of SSc-ILD severity (Aim 1). Those proteins found to correlate with SSc-ILD severity
measures will be subsequently tested for their ability to predict treatment response to immunosuppression
based on the course of the forced vital capacity (FVC) measured at multiple time points over two years using
data from the Scleroderma Lung Study (SLS) II (Aim 2a). SLS II was a 14-center, randomized controlled trial
comparing cyclophosphamide with mycophenolate for SSc-ILD; all patients in this study had well-characterized
SSc-ILD, uniform follow up measurements, equal access to health care and a standard treatment approach.
The proposed prediction model will also include clinical features, as well as innovative measures of ILD
severity, such as the quantitative extent of lung fibrosis/ILD on high-resolution computed tomography (HRCT).
This study will also explore whether changes in the levels of select peripherally measured proteins predict
response to immunosuppression (Aim 2b). An external, prospective cohort of SSc-ILD patients treated and
followed in a similar manner as those in SLS II will be used to validate the prediction model (Aim 3).
Completion of the proposed project and accompanying training plan will facilitate the candidate's short-term
goal of developing new skills in cytokine measurement, quantitative imaging analysis, longitudinal data
analysis, and precision medicine. Acquiring formal training in these areas will help the candidate fulfill her long-
term goal of becoming an independent NIH-funded investigator focused on the application of precision
medicine to discover and define specific SSc-ILD phenotypes and endotypes. T...

## Key facts

- **NIH application ID:** 10378622
- **Project number:** 5K23HL150237-03
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Elizabeth R Volkmann
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $182,066
- **Award type:** 5
- **Project period:** 2020-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10378622

## Citation

> US National Institutes of Health, RePORTER application 10378622, Identifying predictors of treatment response in systemic sclerosis-related interstitial lung disease (5K23HL150237-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10378622. Licensed CC0.

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