Purchase this article with an account.
Roos A. W. Wennink, Viera Kalinina Ayuso, Weiyang Tao, Eveline M. Delemarre, Joke H. de Boer, Jonas J. W. Kuiper; A Blood Protein Signature Stratifies Clinical Response to csDMARD Therapy in Pediatric Uveitis. Trans. Vis. Sci. Tech. 2022;11(2):4. doi: https://doi.org/10.1167/tvst.11.2.4.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
To identify a serum biomarker signature that can help predict response to conventional synthetic disease-modifying antirheumatic drug (csDMARD) therapy in pediatric noninfectious uveitis.
In this case-control cohort study, we performed a 368-plex proteomic analysis of serum samples of 72 treatment-free patients with active uveitis (new onset or relapse) and 15 healthy controls. Among these, 37 patients were sampled at diagnosis before commencing csDMARD therapy. After 6 months, csDMARD response was evaluated and cases were categorized as “responder” or “nonresponder.” Patients were considered “nonresponders” if remission was not achieved under csDMARD therapy. Serum protein profiles were used to train random forest models to predict csDMARD failure and compared to a model based on eight clinical parameters at diagnosis (e.g., maximum cell grade).
In total, 19 of 37 (51%) cases were categorized as csDMARD nonresponders. We identified a 10-protein signature that could predict csDMARD failure with an overall accuracy of 84%, which was higher compared to a model based on eight clinical parameters (73% accuracy). Adjusting for age, sex, anatomic location of uveitis, and cell grade, cases stratified by the 10-protein signature at diagnosis showed a large difference in risk for csDMARD failure (hazard ratio, 12.8; 95% confidence interval, 2.5–64.6; P = 0.002).
Machine learning models based on the serum proteome can stratify pediatric patients with uveitis at high risk for csDMARD failure.
The identified protein signature has implications for the development of clinical decision tools that integrate clinical parameters with biological data to better predict the best treatment option.
This PDF is available to Subscribers Only