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Chen SF, Yeh FC, Chen CY, Chang HY. Tailored therapeutic decision of rheumatoid arthritis using proteomic strategies: how to start and when to stop? Clin Proteomics 2023; 20:22. [PMID: 37301840 DOI: 10.1186/s12014-023-09411-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
Unpredictable treatment responses have been an obstacle for the successful management of rheumatoid arthritis. Although numerous serum proteins have been proposed, there is a lack of integrative survey to compare their relevance in predicting treatment outcomes in rheumatoid arthritis. Also, little is known about their applications in various treatment stages, such as dose modification, drug switching or withdrawal. Here we present an in-depth exploration of the potential usefulness of serum proteins in clinical decision-making and unveil the spectrum of immunopathology underlying responders to different drugs. Patients with robust autoimmunity and inflammation are more responsive to biological treatments and prone to relapse during treatment de-escalation. Moreover, the concentration changes of serum proteins at the beginning of the treatments possibly assist early recognition of treatment responders. With a better understanding of the relationship between the serum proteome and treatment responses, personalized medicine in rheumatoid arthritis will be more achievable in the near future.
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Affiliation(s)
- Shuo-Fu Chen
- Department of Heavy Particles & Radiation Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Fu-Chiang Yeh
- Division of Rheumatology, Immunology and Allergy, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Ching-Yun Chen
- Department of Biomedical Sciences and Engineering, Institute of Biomedical Engineering and Nanomedicine, National Central University, Taoyuan, Taiwan
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Hui-Yin Chang
- Department of Biomedical Sciences and Engineering, Institute of Systems Biology and Bioinformatics, National Central University, No. 300, Zhongda Rd., Zhongli District, Taoyuan, 320317, Taiwan.
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De Cock D, Myasoedova E, Aletaha D, Studenic P. Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs). Ther Adv Musculoskelet Dis 2022; 14:1759720X221105978. [PMID: 35794905 PMCID: PMC9251966 DOI: 10.1177/1759720x221105978] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Accepted: 05/22/2022] [Indexed: 11/17/2022] Open
Abstract
Health care processes are under constant development and will need to embrace advances in technology and health science aiming to provide optimal care. Considering the perspective of increasing treatment options for people with rheumatic and musculoskeletal diseases, but in many cases not reaching all treatment targets that matter to patients, care systems bare potential to improve on a holistic level. This review provides an overview of systems and technologies under evaluation over the past years that show potential to impact diagnosis and treatment of rheumatic diseases in about 10 years from now. We summarize initiatives and studies from the field of electronic health records, biobanking, remote monitoring, and artificial intelligence. The combination and implementation of these opportunities in daily clinical care will be key for a new era in care of our patients. This aims to inform rheumatologists and healthcare providers concerned with chronic inflammatory musculoskeletal conditions about current important and promising developments in science that might substantially impact the management processes of rheumatic diseases in the 2030s.
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Affiliation(s)
- Diederik De Cock
- Clinical and Experimental Endocrinology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Elena Myasoedova
- Division of Rheumatology, Department of Internal Medicine and Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Daniel Aletaha
- Division of Rheumatology, Department of Internal Medicine 3, Medical University Vienna, Vienna, Austria
| | - Paul Studenic
- Division of Rheumatology, Department of Internal Medicine 3, Medical University Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
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Prasad B, McGeough C, Eakin A, Ahmed T, Small D, Gardiner P, Pendleton A, Wright G, Bjourson AJ, Gibson DS, Shukla P. ATRPred: A machine learning based tool for clinical decision making of anti-TNF treatment in rheumatoid arthritis patients. PLoS Comput Biol 2022; 18:e1010204. [PMID: 35788746 PMCID: PMC9321399 DOI: 10.1371/journal.pcbi.1010204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 07/26/2022] [Accepted: 05/14/2022] [Indexed: 01/10/2023] Open
Abstract
Rheumatoid arthritis (RA) is a chronic autoimmune condition, characterised by joint pain, damage and disability, which can be addressed in a high proportion of patients by timely use of targeted biologic treatments. However, the patients, non-responsive to the treatments often suffer from refractoriness of the disease, leading to poor quality of life. Additionally, the biologic treatments are expensive. We obtained plasma samples from N = 144 participants with RA, who were about to commence anti-tumour necrosis factor (anti-TNF) therapy. These samples were sent to Olink Proteomics, Uppsala, Sweden, where proximity extension assays of 4 panels, containing 92 proteins each, were performed. A total of n = 89 samples of patients passed the quality control of anti-TNF treatment response data. The preliminary analysis of plasma protein expression values suggested that the RA population could be divided into two distinct molecular sub-groups (endotypes). However, these broad groups did not predict response to anti-TNF treatment, but were significantly different in terms of gender and their disease activity. We then labelled these patients as responders (n = 60) and non-responders (n = 29) based on the change in disease activity score (DAS) after 6 months of anti-TNF treatment and applied machine learning (ML) with a rigorous 5-fold nested cross-validation scheme to filter 17 proteins that were significantly associated with the treatment response. We have developed a ML based classifier ATRPred (anti-TNF treatment response predictor), which can predict anti-TNF treatment response in RA patients with 81% accuracy, 75% sensitivity and 86% specificity. ATRPred may aid clinicians to direct anti-TNF therapy to patients most likely to receive benefit, thus save cost as well as prevent non-responsive patients from refractory consequences. ATRPred is implemented in R.
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Affiliation(s)
- Bodhayan Prasad
- Northern Ireland Centre for Stratified Medicine (NICSM), Biomedical Sciences Research Institute, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Londonderry, United Kingdom
| | - Cathy McGeough
- Northern Ireland Centre for Stratified Medicine (NICSM), Biomedical Sciences Research Institute, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Londonderry, United Kingdom
| | - Amanda Eakin
- Northern Ireland Centre for Stratified Medicine (NICSM), Biomedical Sciences Research Institute, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Londonderry, United Kingdom
| | - Tan Ahmed
- Northern Ireland Centre for Stratified Medicine (NICSM), Biomedical Sciences Research Institute, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Londonderry, United Kingdom
| | - Dawn Small
- Western Health and Social Care Trust (WHSCT), Altnagelvin Area Hospital, Londonderry, United Kingdom
| | - Philip Gardiner
- Western Health and Social Care Trust (WHSCT), Altnagelvin Area Hospital, Londonderry, United Kingdom
| | - Adrian Pendleton
- Belfast Health and Social Care Trust (BHSCT), Belfast City Hospital, Belfast, United Kingdom
| | - Gary Wright
- Belfast Health and Social Care Trust (BHSCT), Belfast City Hospital, Belfast, United Kingdom
| | - Anthony J. Bjourson
- Northern Ireland Centre for Stratified Medicine (NICSM), Biomedical Sciences Research Institute, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Londonderry, United Kingdom
| | - David S. Gibson
- Northern Ireland Centre for Stratified Medicine (NICSM), Biomedical Sciences Research Institute, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Londonderry, United Kingdom
| | - Priyank Shukla
- Northern Ireland Centre for Stratified Medicine (NICSM), Biomedical Sciences Research Institute, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Londonderry, United Kingdom
- * E-mail:
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Edner NM, Wang CJ, Petersone L, Walker LSK. Predicting clinical response to costimulation blockade in autoimmunity. IMMUNOTHERAPY ADVANCES 2020; 1:ltaa003. [PMID: 36017489 PMCID: PMC7613378 DOI: 10.1093/immadv/ltaa003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Curbing unwanted T cell responses by costimulation blockade has been a recognised immunosuppressive strategy for the last 15 years. However, our understanding of how best to deploy this intervention is still evolving. A key challenge has been the heterogeneity in the clinical response to costimulation blockade, and an inability to predict which individuals are likely to benefit most. Here, we discuss our recent findings based on the use of costimulation blockade in people with type 1 diabetes (T1D) and place them in the context of the current literature. We discuss how profiling follicular helper T cells (Tfh) in pre-treatment blood samples may have value in predicting which individuals are likely to benefit from costimulation blockade drugs such as abatacept.
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Affiliation(s)
- Natalie M Edner
- Division of Infection and Immunity, Institute of Immunity and Transplantation, University College London, London, UK
| | - Chun Jing Wang
- Division of Infection and Immunity, Institute of Immunity and Transplantation, University College London, London, UK
| | - Lina Petersone
- Division of Infection and Immunity, Institute of Immunity and Transplantation, University College London, London, UK
| | - Lucy S K Walker
- Division of Infection and Immunity, Institute of Immunity and Transplantation, University College London, London, UK
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