Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis
O'Neil LJ, Hu P, Liu Q, Islam MM, Spicer V, Rech J, Hueber A, Anaparti V, Smolik I, El-Gabalawy HS, Schett G, Wilkins JA (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 12
Article Number: 729681
DOI: 10.3389/fimmu.2021.729681
Abstract
Objectives: Patients with Rheumatoid Arthritis (RA) are increasingly achieving stable disease remission, yet the mechanisms that govern ongoing clinical disease and subsequent risk of future flare are not well understood. We sought to identify serum proteomic alterations that dictate clinically important features of stable RA, and couple broad-based proteomics with machine learning to predict future flare. Methods: We studied baseline serum samples from a cohort of stable RA patients (RETRO, n = 130) in clinical remission (DAS28<2.6) and quantified 1307 serum proteins using the SOMAscan platform. Unsupervised hierarchical clustering and supervised classification were applied to identify proteomic-driven clusters and model biomarkers that were associated with future disease flare after 12 months of follow-up and RA medication withdrawal. Network analysis was used to define pathways that were enriched in proteomic datasets. Results: We defined 4 proteomic clusters, with one cluster (Cluster 4) displaying a lower mean DAS28 score (p = 0.03), with DAS28 associating with humoral immune responses and complement activation. Clustering did not clearly predict future risk of flare, however an XGboost machine learning algorithm classified patients who relapsed with an AUC (area under the receiver operating characteristic curve) of 0.80 using only baseline serum proteomics. Conclusions: The serum proteome provides a rich dataset to understand stable RA and its clinical heterogeneity. Combining proteomics and machine learning may enable prediction of future RA disease flare in patients with RA who aim to withdrawal therapy.
Authors with CRIS profile
Hans Jürgen Rech
Department of Medicine 3 – Rheumatology and Immunology
Axel Hueber
Department of Medicine 3 – Rheumatology and Immunology
Georg Schett
Lehrstuhl für Innere Medizin III
Involved external institutions
Canada (CA)How to cite
APA:
O'Neil, L.J., Hu, P., Liu, Q., Islam, M.M., Spicer, V., Rech, J.,... Wilkins, J.A. (2021). Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis. Frontiers in Immunology, 12. https://dx.doi.org/10.3389/fimmu.2021.729681
MLA:
O'Neil, Liam J., et al. "Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis." Frontiers in Immunology 12 (2021).
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