# Optimizing the Approach to Identify Cancer-Associated Myositis

> **NIH NIH K23** · JOHNS HOPKINS UNIVERSITY · 2020 · $154,592

## Abstract

PROJECT SUMMARY
Although the pathogenesis of idiopathic inflammatory myopathies (IIM) is largely unknown, data has emerged
describing a relationship between cancer and IIM onset. In a subset of IIM patients, there exists an increased
risk of cancer around the time of myositis onset, referred to as cancer-associated myositis (CAM). Several
myositis-specific autoantibodies have proven useful in the clinical phenotyping of patients with IIM, including
associating with cancer. However, which patients are at highest risk for developing cancer, the magnitude
of the risk, the type of cancer, and the optimal cancer detection strategy are all unknown. Our preliminary
data demonstrate that these myositis-specific autoantibodies can serve a useful role in the ability to define
subgroups with regards to cancer risk as well as provide insight into the type of cancer a patient may be at
highest risk for. Furthermore, we demonstrate that despite the widespread use of a large variety of cancer-
screening tests employed by clinicians in the United States, not all tests have equal value in IIM patients. The
proposed studies will utilize one of the largest cohorts of IIM patients in the world to define and validate
autoantibodies associated with increased cancer risk and to assess their utility in quantifying the cancer risk at
disease onset. In Aim 1 we will determine the risk of cancer-associated myositis relative to the general population
in our cohort overall and in distinct autoantibody subgroups. We will demonstrate that the risk and type of cancer
associated with IIM relative to the general population will vary based on the autoantibody the patient produces.
Aim 2 will provide data on the usefulness of the current standard of cancer assessment that is performed in IIM
patients, and generate an argument for a more selective, less harmful detection strategy. Lastly, in Aim 3 we will
derive a predictive tool that will be used to inform clinical decision-making for optimal strategies to assess IIM
patients for malignancy. This work will allow the development of a clinically relevant and evidence-based
approach to cancer detection at IIM onset and establish the role for defining IIM patients by autoantibody
subsets.

## Key facts

- **NIH application ID:** 10025569
- **Project number:** 5K23AR075898-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Christopher Mecoli
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $154,592
- **Award type:** 5
- **Project period:** 2019-09-26 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10025569

## Citation

> US National Institutes of Health, RePORTER application 10025569, Optimizing the Approach to Identify Cancer-Associated Myositis (5K23AR075898-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10025569. Licensed CC0.

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