# Myositis-ILD Risk Prediction Models and Endotypes

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $539,218

## Abstract

Interstitial lung disease (ILD) is a highly morbid and potentially fatal complication of myositis.
Current myositis phenotypes do not capture significant heterogeneity in ILD clinical behavior.
Existing prediction models and biomarkers perform poorly in patients with myositis-ILD and
require longitudinal validation in diverse patient populations. Because clinicians lack tools to
prognosticate long-term myositis-ILD outcomes, many patients receive either inadequate or
overly aggressive treatment. We have shown that the GAP-ILD Index performs poorly in myositis-
ILD. Myositis-ILD specific prediction tools do not adequately model disease progression and do
not generalize to populations outside of Asia. Previously, we demonstrated that specific human
leukocyte antigen (HLA) alleles are myositis-ILD susceptibility factors. Our preliminary model of
progression-free survival containing clinical data and HLA genotypes discriminated progression,
suggesting that HLA haplotypes are also biomarkers of myositis-ILD disease activity and
treatment responsiveness. As a next step, we plan to refine and validate our preliminary model.
The goal of Specific Aim 1 is to utilize a large, diverse multicenter cohort of patients with myositis-
ILD in North America to develop a prediction model of progression free survival using known and
novel biomarkers. While increasingly recognized, there is no current strategy to confront the
clinical and biological heterogeneity within myositis-ILD subgroups. Our preliminary data
demonstrates two clusters of myositis-ILD. We hypothesize that these myositis-ILD clusters carry
distinct pathobiology, treatment responsiveness, and outcomes. The goal of specific Aim 2 is to
apply cluster analysis to clinical, genomic, and serological data from patients with myositis-ILD to
identify novel subphenotypes. The data generated from this proposal will result in a novel
classification of myositis-ILD subphenotypes and a superior tool for prognosticating important
clinical outcomes. Our approach is feasible because we are building on existing clinical and
biorepository data from five demographically diverse myositis-ILD referral centers in the United
States. This contribution is significant because it will establish a validated clinical signature for
precision therapy in patients with myositis-ILD and generate novel treatment approaches.

## Key facts

- **NIH application ID:** 10883025
- **Project number:** 1R01HL169392-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Cheilonda Johnson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $539,218
- **Award type:** 1
- **Project period:** 2024-09-17 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10883025

## Citation

> US National Institutes of Health, RePORTER application 10883025, Myositis-ILD Risk Prediction Models and Endotypes (1R01HL169392-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10883025. Licensed CC0.

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