Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis

ObjectivesPatients 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 import...

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Autores principales:	Liam J. O’Neil, Pingzhao Hu, Qian Liu, Md. Mohaiminul Islam, Victor Spicer, Juergen Rech, Axel Hueber, Vidyanand Anaparti, Irene Smolik, Hani S. El-Gabalawy, Georg Schett, John A. Wilkins
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	rheumatoid arthritis disease activity outcomes research treatment proteomics Immunologic diseases. Allergy RC581-607
Acceso en línea:	https://doaj.org/article/cf6534dc36e54628942187bed207ce42
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spelling	oai:doaj.org-article:cf6534dc36e54628942187bed207ce422021-11-18T05:27:35ZProteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis1664-322410.3389/fimmu.2021.729681https://doaj.org/article/cf6534dc36e54628942187bed207ce422021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fimmu.2021.729681/fullhttps://doaj.org/toc/1664-3224ObjectivesPatients 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.MethodsWe 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.ResultsWe 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.ConclusionsThe 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.Liam J. O’NeilLiam J. O’NeilPingzhao HuPingzhao HuQian LiuQian LiuMd. Mohaiminul IslamMd. Mohaiminul IslamVictor SpicerJuergen RechAxel HueberVidyanand AnapartiIrene SmolikHani S. El-GabalawyHani S. El-GabalawyGeorg SchettJohn A. WilkinsJohn A. WilkinsFrontiers Media S.A.articlerheumatoid arthritisdisease activityoutcomes researchtreatmentproteomicsImmunologic diseases. AllergyRC581-607ENFrontiers in Immunology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rheumatoid arthritis disease activity outcomes research treatment proteomics Immunologic diseases. Allergy RC581-607
spellingShingle	rheumatoid arthritis disease activity outcomes research treatment proteomics Immunologic diseases. Allergy RC581-607 Liam J. O’Neil Liam J. O’Neil Pingzhao Hu Pingzhao Hu Qian Liu Qian Liu Md. Mohaiminul Islam Md. Mohaiminul Islam Victor Spicer Juergen Rech Axel Hueber Vidyanand Anaparti Irene Smolik Hani S. El-Gabalawy Hani S. El-Gabalawy Georg Schett John A. Wilkins John A. Wilkins Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis
description	ObjectivesPatients 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.MethodsWe 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.ResultsWe 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.ConclusionsThe 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.
format	article
author	Liam J. O’Neil Liam J. O’Neil Pingzhao Hu Pingzhao Hu Qian Liu Qian Liu Md. Mohaiminul Islam Md. Mohaiminul Islam Victor Spicer Juergen Rech Axel Hueber Vidyanand Anaparti Irene Smolik Hani S. El-Gabalawy Hani S. El-Gabalawy Georg Schett John A. Wilkins John A. Wilkins
author_facet	Liam J. O’Neil Liam J. O’Neil Pingzhao Hu Pingzhao Hu Qian Liu Qian Liu Md. Mohaiminul Islam Md. Mohaiminul Islam Victor Spicer Juergen Rech Axel Hueber Vidyanand Anaparti Irene Smolik Hani S. El-Gabalawy Hani S. El-Gabalawy Georg Schett John A. Wilkins John A. Wilkins
author_sort	Liam J. O’Neil
title	Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis
title_short	Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis
title_full	Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis
title_fullStr	Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis
title_full_unstemmed	Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis
title_sort	proteomic approaches to defining remission and the risk of relapse in rheumatoid arthritis
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/cf6534dc36e54628942187bed207ce42
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Proteomic Approaches to Defining Remission and the Risk of Relapse in Rheumatoid Arthritis

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