# Derivation and validation of a clinical-molecular signature to predict fibrotic progression and treatment response in patients with autoimmune interstitial lung disease

> **NIH NIH R56** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2021 · $399,681

## Abstract

Project Summary
 Progressive fibrosing interstitial lung disease (PF-ILD) is a devastating condition characterized by
parenchymal destruction, lung function decline and ultimately death. Autoimmune ILD (aILD) is a leading cause
of PF-ILD, but the natural history of PF-ILD in those with aILD has yet to be characterized. Immunosuppressant
(IS) therapy is generally used to treat aILD, as parenchymal inflammation often precedes pulmonary fibrosis. Of
those aILD patients treated with IS, some will respond favorably, while others will develop PF-ILD. Because
pulmonary fibrosis is an irreversible process, there exists a critical need to better understand PF-ILD, identify
those most likely to develop this phenotype and establish an optimal treatment approach for this group.
 We recently identified a number of circulating plasma biomarkers of PF-ILD and have now generated
exciting preliminary data that show a clinical-molecular signature (CMS) comprised of aggregated clinical and
plasma biomarker data predicts differential PF-ILD risk and IS response in those with aILD. Optimization and
validation of these findings would advance our understanding of PF-ILD and have profound treatment
implications for the field. The objective of this application to optimize and validate our preliminary CMS of PF-
ILD and IS response in a large, multi-center, international aILD cohort. My co-investigators and I have expertise
in translational ILD research, including plasma biomarker investigation. All plasma samples needed for this
proposal have been collected, underscoring the feasibility of our approach.
 We will first characterize the natural history of PF-ILD in a well-phenotyped, multi-center aILD cohort
(n=2000). We will determine the prevalence of short-term forced vital capacity decline and long-term mortality.
Will identify clinical predictors of these endpoints and validate our findings in an independent aILD cohort. Next,
we will derive and validate a CMS of PF-ILD. A quantitative, multiplex platform will be used to determine plasma
concentration for 64 relevant biomarkers of inflammation and fibrogenesis. Using one-year progression-free
survival as the primary endpoint, logistic regression will be performed to identify independent clinical and
biomarker predictors of outcome. A CMS of PF-ILD will be derived using regression point estimates and then
validated in three independent aILD cohorts. Finally, we will derive and validate a CMS of IS response. Using
Interaction modeling will be performed to identify independent clinical and molecular predictors of IS effect
modification. A CMS of IS response will be derived using regression point estimates and then validated in three
independent aILD cohorts, including two prospectively collected IS clinical trial cohorts.
 Successful completion of this proposal will lead to prospective CMS validation with the goal of developing
the first molecular diagnostic tool to support clinical decision making in aILD. This work wil...

## Key facts

- **NIH application ID:** 10275747
- **Project number:** 1R56HL158935-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Justin M Oldham
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $399,681
- **Award type:** 1
- **Project period:** 2021-09-20 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10275747

## Citation

> US National Institutes of Health, RePORTER application 10275747, Derivation and validation of a clinical-molecular signature to predict fibrotic progression and treatment response in patients with autoimmune interstitial lung disease (1R56HL158935-01). Retrieved via AI Analytics 2026-06-14 from https://api.ai-analytics.org/grant/nih/10275747. Licensed CC0.

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