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Taylor PC, Downie B, Han L, Hawtin R, Hertz A, Moots RJ, Takeuchi T. Patients with High Baseline Neutrophil-to-Lymphocyte Ratio Exhibit Better Response to Filgotinib as Treatment for Rheumatoid Arthritis. Rheumatol Ther 2024:10.1007/s40744-024-00695-w. [PMID: 38985247 DOI: 10.1007/s40744-024-00695-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 06/18/2024] [Indexed: 07/11/2024] Open
Abstract
INTRODUCTION High baseline neutrophil-to-lymphocyte ratio (NLR) in rheumatoid arthritis (RA) has been associated with positive responses to biologic tumor necrosis factor inhibition and negative responses to conventional synthetic disease-modifying antirheumatic drug (csDMARD) triple therapy. Datasets from three randomized clinical trials in patients with RA were used to test the hypothesis that baseline NLR is associated with improved clinical response to filgotinib in methotrexate (MTX)-naïve or MTX-experienced RA populations. METHODS Patients from FINCH 1 (inadequate response to MTX, MTX-IR; NCT02889796), FINCH 2 (inadequate response to biologic DMARDs; NCT02873936), and FINCH 3 (MTX-naïve; NCT02886728) were classified as baseline NLR-High or baseline NLR-Low based on a previously published cut point of 2.7. In total, 3365 patients were included across the three studies. Differences in clinical outcomes and patient-reported outcomes (PROs) were determined using linear-regression models. RESULTS Control-arm patients (placebo + MTX/placebo + csDMARD) classified as NLR-High exhibited worse continuous clinical and PRO responses at week 12 across clinical trials compared to NLR-Low patients. In contrast, NLR-High patients who received FIL 200 mg + MTX/csDMARD exhibited consistently better responses after 12 weeks compared to NLR-Low patients across clinical trials, clinical endpoints, and PROs. These trends were most prominent among the MTX-IR population. CONCLUSION The 2.7 baseline NLR cut point could be used to enrich for patients most likely to benefit from the addition of filgotinib to background MTX/csDMARD. Use of baseline NLR as part of therapeutic decision-making would not require additional diagnostics and could contribute to improved outcomes for patients with RA. TRIAL REGISTRATION Clinicaltrials.gov: NCT02889796; NCT02873936; NCT02886728.
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Affiliation(s)
- Peter C Taylor
- Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
| | - Bryan Downie
- Gilead Sciences, Inc., Foster City, CA, 94404, USA
| | - Ling Han
- Gilead Sciences, Inc., Foster City, CA, 94404, USA
| | | | - Angie Hertz
- Gilead Sciences, Inc., Foster City, CA, 94404, USA
| | - Robert J Moots
- Department of Rheumatology, Aintree University Hospital, Liverpool, L9 7AL, UK
- Faculty of Health, Social Care and Medicine, Edge Hill University, Ormskirk, L39 4QP, UK
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Chang MJ, Feng QF, Hao JW, Zhang YJ, Zhao R, Li N, Zhao YH, Han ZY, He PF, Wang CH. Deciphering the molecular landscape of rheumatoid arthritis offers new insights into the stratified treatment for the condition. Front Immunol 2024; 15:1391848. [PMID: 38983856 PMCID: PMC11232074 DOI: 10.3389/fimmu.2024.1391848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 05/31/2024] [Indexed: 07/11/2024] Open
Abstract
Background For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments. Methods We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs. Results Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs. Conclusions The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.
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Affiliation(s)
- Min-Jing Chang
- Department of Rheumatology, Second Hospital of Shanxi Medical University, Taiyuan, China
- Shanxi Key Laboratory of Immunomicroecology, Taiyuan, China
- Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China
| | - Qi-Fan Feng
- Department of Rheumatology, Second Hospital of Shanxi Medical University, Taiyuan, China
- Shanxi Key Laboratory of Immunomicroecology, Taiyuan, China
| | - Jia-Wei Hao
- Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China
| | - Ya-Jing Zhang
- Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China
| | - Rong Zhao
- Department of Rheumatology, Second Hospital of Shanxi Medical University, Taiyuan, China
- Shanxi Key Laboratory of Immunomicroecology, Taiyuan, China
| | - Nan Li
- Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China
| | - Yu-Hui Zhao
- Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China
| | - Zi-Yi Han
- Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China
| | - Pei-Feng He
- Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, China
| | - Cai-Hong Wang
- Department of Rheumatology, Second Hospital of Shanxi Medical University, Taiyuan, China
- Shanxi Key Laboratory of Immunomicroecology, Taiyuan, China
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Yu CY, Lee HS, Joo YB, Cho SK, Choi CB, Sung YK, Kim TH, Jun JB, Yoo DH, Bae SC, Kim K, Bang SY. Transcriptomic network analysis reveals key drivers of response to anti-TNF biologics in patients with rheumatoid arthritis. Rheumatology (Oxford) 2024; 63:1422-1431. [PMID: 37572297 DOI: 10.1093/rheumatology/kead403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 07/08/2023] [Accepted: 07/28/2023] [Indexed: 08/14/2023] Open
Abstract
OBJECTIVE Anti-TNF biologics have been widely used to ameliorate disease activity in patients with RA. However, a large fraction of patients show a poor response to these agents. Moreover, no clinically applicable predictive biomarkers have been established. This study aimed to identify response-associated biomarkers using longitudinal transcriptomic data in two independent RA cohorts. METHODS RNA sequencing data from peripheral blood cell samples of Korean and Caucasian RA cohorts before and after initial treatment with anti-TNF biologics were analysed to assess treatment-induced expression changes that differed between highly reliable excellent responders and null responders. Weighted correlation network, immune cell composition, and key driver analyses were performed to understand response-associated transcriptomic networks and cell types and their correlation with disease activity indices. RESULTS In total, 305 response-associated genes showed significantly different treatment-induced expression changes between excellent and null responders. Co-expression network construction and subsequent key driver analysis revealed that 41 response-associated genes played a crucial role as key drivers of transcriptomic alteration in four response-associated networks involved in various immune pathways: type I IFN signalling, myeloid leucocyte activation, B cell activation, and NK cell/lymphocyte-mediated cytotoxicity. Transcriptomic response scores that we developed to estimate the individual-level degree of expression changes in the response-associated key driver genes were significantly correlated with the changes in clinical indices in independent patients with moderate or ambiguous response outcomes. CONCLUSION This study provides response-specific treatment-induced transcriptomic signatures by comparing the transcriptomic landscape between patients with excellent and null responses to anti-TNF drugs at both gene and network levels.
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Affiliation(s)
- Chae-Yeon Yu
- Department of Biology, Kyung Hee University, Seoul, Republic of Korea
- Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Seoul, Republic of Korea
| | - Hye-Soon Lee
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases and Hanyang University Institute for Rheumatology, Seoul, Republic of Korea
| | - Young Bin Joo
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases and Hanyang University Institute for Rheumatology, Seoul, Republic of Korea
| | - Soo-Kyung Cho
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases and Hanyang University Institute for Rheumatology, Seoul, Republic of Korea
| | - Chan-Bum Choi
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases and Hanyang University Institute for Rheumatology, Seoul, Republic of Korea
| | - Yoon-Kyoung Sung
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases and Hanyang University Institute for Rheumatology, Seoul, Republic of Korea
| | - Tae-Hwan Kim
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases and Hanyang University Institute for Rheumatology, Seoul, Republic of Korea
| | - Jae-Bum Jun
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases and Hanyang University Institute for Rheumatology, Seoul, Republic of Korea
| | - Dae Hyun Yoo
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases and Hanyang University Institute for Rheumatology, Seoul, Republic of Korea
| | - Sang-Cheol Bae
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases and Hanyang University Institute for Rheumatology, Seoul, Republic of Korea
| | - Kwangwoo Kim
- Department of Biology, Kyung Hee University, Seoul, Republic of Korea
- Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Seoul, Republic of Korea
| | - So-Young Bang
- Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases and Hanyang University Institute for Rheumatology, Seoul, Republic of Korea
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Ling SF, Yap CF, Nair N, Bluett J, Morgan AW, Isaacs JD, Wilson AG, Hyrich KL, Barton A, Plant D. A proteomics study of rheumatoid arthritis patients on etanercept identifies putative biomarkers associated with clinical outcome measures. Rheumatology (Oxford) 2024; 63:1015-1021. [PMID: 37389432 PMCID: PMC10986807 DOI: 10.1093/rheumatology/kead321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 05/26/2023] [Accepted: 06/15/2023] [Indexed: 07/01/2023] Open
Abstract
OBJECTIVES Biologic DMARDs (bDMARDs) are widely used in patients with RA, but response to bDMARDs is heterogeneous. The objective of this work was to identify pretreatment proteomic biomarkers associated with RA clinical outcome measures in patients starting bDMARDs. METHODS Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was used to generate spectral maps of sera from patients with RA before and after 3 months of treatment with the bDMARD etanercept. Protein levels were regressed against RA clinical outcome measures, i.e. 28-joint DAS (DAS28) and its subcomponents and DAS28 <2.6 (i.e. remission). The proteins with the strongest evidence for association were analysed in an independent, replication dataset. Finally, subnetwork analysis was carried out using the Disease Module Detection algorithm and biological plausibility of identified proteins was assessed by enrichment analysis. RESULTS A total of 180 patients with RA were included in the discovery dataset and 58 in the validation dataset from a UK-based prospective multicentre study. Ten individual proteins were found to be significantly associated with RA clinical outcome measures. The association of T-complex protein 1 subunit η with DAS28 remission was replicated in an independent cohort. Subnetwork analysis of the 10 proteins from the regression analysis identified the ontological theme, with the strongest associations being with acute phase and acute inflammatory responses. CONCLUSION This longitudinal study of 180 patients with RA commencing etanercept has identified several putative protein biomarkers of treatment response to this drug, one of which was replicated in an independent cohort.
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Affiliation(s)
- Stephanie F Ling
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Chuan Fu Yap
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Nisha Nair
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - James Bluett
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Ann W Morgan
- School of Medicine, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- NIHR In Vitro Diagnostic Co-operative, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - John D Isaacs
- Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK
- Musculoskeletal Unit, Newcastle-upon-Tyne Hospitals NHS Foundation Trust, Newcastle-upon-Tyne, UK
| | - Anthony G Wilson
- School of Medicine and Medical Science, Conway Institute, University College Dublin, Dublin, Ireland
| | - Kimme L Hyrich
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Anne Barton
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Darren Plant
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
- NIHR Biomedical Research Centre Manchester, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
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Liu X, Li J, Sun L, Wang T, Liang W. The association between neutrophil-to-lymphocyte ratio and disease activity in rheumatoid arthritis. Inflammopharmacology 2023; 31:2237-2244. [PMID: 37418101 DOI: 10.1007/s10787-023-01273-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/15/2023] [Indexed: 07/08/2023]
Abstract
The inflammatory response is responsible for the promotion of pannus development over the joint, which is the primary factor in joint injury in rheumatoid arthritis (RA). More in-depth investigations have been conducted in recent years leading to a greater understanding of RA. Yet, it's difficult to gauge inflammation levels in RA patients. Some people who have RA do not exhibit normal symptoms, which makes it more challenging to make a diagnosis. Typical RA evaluations are subject to a few restrictions. Earlier research demonstrated that some patients continued to experience the progression of bone and joint degeneration even while in clinical remission. This progression was attributed to ongoing synovial inflammation. As a result, performing a precise evaluation of the level of inflammation is of the utmost importance. The neutrophil-to-lymphocyte ratio (NLR) has consistently been one of the most interesting novel non-specific inflammatory indicators. It is a reflection of the equilibrium between lymphocytes and neutrophils, which are inflammatory regulators and inflammatory activators, respectively. A higher NLR is linked to more severe levels of imbalance and inflammation. The aim of this study was to depict the role of NLR in RA progression and to show if NLR could predict the response to disease-modifying antirheumatic drugs (DMARDs) therapy in RA.
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Affiliation(s)
- Xiangsu Liu
- General Practice Medicine, Yanqing District Hospital, Yanqing Hospital, Peking University Third Hospital, Beijing, 102100, China
| | - Jiaqi Li
- General Practice Medicine, Yanqing District Hospital, Yanqing Hospital, Peking University Third Hospital, Beijing, 102100, China
| | - Leilei Sun
- Department of Endocrinology, Yanqing District Hospital, Yanqing Hospital, Peking University Third Hospital, Beijing, 102100, China
| | - Tong Wang
- Department of Laboratory Medicine, The First Hospital of Hebei Medical University, Shijiazhuang, 050030, China
| | - Wenxia Liang
- General Practice Medicine, Yanqing District Hospital, Yanqing Hospital, Peking University Third Hospital, Beijing, 102100, China.
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Hedman ÅK, Winter E, Yoosuf N, Benita Y, Berg L, Brynedal B, Folkersen L, Klareskog L, Maciejewski M, Sirota-Madi A, Spector Y, Ziemek D, Padyukov L, Shen-Orr SS, Jelinsky SA. Peripheral blood cellular dynamics of rheumatoid arthritis treatment informs about efficacy of response to disease modifying drugs. Sci Rep 2023; 13:10058. [PMID: 37344505 DOI: 10.1038/s41598-023-36999-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 06/14/2023] [Indexed: 06/23/2023] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease characterized by systemic inflammation and is mediated by multiple immune cell types. In this work, we aimed to determine the relevance of changes in cell proportions in peripheral blood mononuclear cells (PBMCs) during the development of disease and following treatment. Samples from healthy blood donors, newly diagnosed RA patients, and established RA patients that had an inadequate response to MTX and were about to start tumor necrosis factor inhibitors (TNFi) treatment were collected before and after 3 months of treatment. We used in parallel a computational deconvolution approach based on RNA expression and flow cytometry to determine the relative cell-type frequencies. Cell-type frequencies from deconvolution of gene expression indicate that monocytes (both classical and non-classical) and CD4+ cells (Th1 and Th2) were increased in RA patients compared to controls, while NK cells and B cells (naïve and mature) were significantly decreased in RA patients. Treatment with MTX caused a decrease in B cells (memory and plasma cell), and a decrease in CD4 Th cells (Th1 and Th17), while treatment with TNFi resulted in a significant increase in the population of B cells. Characterization of the RNA expression patterns found that most of the differentially expressed genes in RA subjects after treatment can be explained by changes in cell frequencies (98% and 74% respectively for MTX and TNFi).
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Affiliation(s)
- Åsa K Hedman
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Department of Inflammation and Immunology, Pfizer, 1 Portland Street, Cambridge, MA, 02139, USA
| | | | - Niyaz Yoosuf
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden
| | | | - Louise Berg
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Boel Brynedal
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Lasse Folkersen
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Lars Klareskog
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Mateusz Maciejewski
- Department of Inflammation and Immunology, Pfizer, 1 Portland Street, Cambridge, MA, 02139, USA
| | | | | | - Daniel Ziemek
- Department of Inflammation and Immunology, Pfizer, Berlin, Germany
| | - Leonid Padyukov
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Shai S Shen-Orr
- CytoReason, Tel-Aviv, Israel
- Technion-Israel Institute of Technology, Haifa, Israel
| | - Scott A Jelinsky
- Department of Inflammation and Immunology, Pfizer, 1 Portland Street, Cambridge, MA, 02139, USA.
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7
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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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Wang J, Conlon D, Rivellese F, Nerviani A, Lewis MJ, Housley W, Levesque MC, Cao X, Cuff C, Long A, Pitzalis C, Ruzek MC. Synovial Inflammatory Pathways Characterize Anti-TNF-Responsive Rheumatoid Arthritis Patients. Arthritis Rheumatol 2022; 74:1916-1927. [PMID: 35854416 DOI: 10.1002/art.42295] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 05/16/2022] [Accepted: 06/30/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVE This study was undertaken to understand the mechanistic basis of response to anti-tumor necrosis factor (anti-TNF) therapies and to determine whether transcriptomic changes in the synovium are reflected in peripheral protein markers. METHODS Synovial tissue from 46 rheumatoid arthritis (RA) patients was profiled with RNA sequencing before and 12 weeks after treatment with anti-TNF therapies. Pathway and gene signature analyses were performed on RNA expression profiles of synovial biopsies to identify mechanisms that could discriminate among patients with a good response, a moderate response, or no response, according to the American College of Rheumatology (ACR)/EULAR response criteria. Serum proteins encoded by synovial genes that were differentially expressed between ACR/EULAR response groups were measured in the same patients. RESULTS Gene signatures predicted which patients would have good responses, and pathway analysis identified elevated immune pathways, including chemokine signaling, Th1/Th2 cell differentiation, and Toll-like receptor signaling, uniquely in good responders. These inflammatory pathways were correspondingly down-modulated by anti-TNF therapy only in good responders. Based on cell signature analysis, lymphocyte, myeloid, and fibroblast cell populations were elevated in good responders relative to nonresponders, consistent with the increased inflammatory pathways. Cell signatures that decreased following anti-TNF treatment were predominately associated with lymphocytes, and fewer were associated with myeloid and fibroblast populations. Following anti-TNF treatment, and only in good responders, several peripheral inflammatory proteins decreased in a manner that was consistent with corresponding synovial gene changes. CONCLUSION Collectively, these data suggest that RA patients with robust responses to anti-TNF therapies are characterized at baseline by immune pathway activation, which decreases following anti-TNF treatment. Understanding mechanisms that define patient responsiveness to anti-TNF treatment may assist in development of predictive markers of patient response and earlier treatment options.
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Affiliation(s)
- Jing Wang
- Immunology Systems Computational Biology, Genomic Research Center, AbbVie, Cambridge, Massachusetts
| | - Donna Conlon
- Immunology Discovery, AbbVie Research Center, Worcester, Massachusetts
| | - Felice Rivellese
- Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute and Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Alessandra Nerviani
- Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute and Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Myles J Lewis
- Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute and Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - William Housley
- Immunology Discovery, AbbVie Research Center, Worcester, Massachusetts
| | - Marc C Levesque
- Immunology Discovery, Cambridge Research Center, Cambridge, Massachusetts
| | - Xiaohong Cao
- Immunology Systems Computational Biology, Genomic Research Center, AbbVie, Cambridge, Massachusetts
| | - Carolyn Cuff
- Immunology Discovery, AbbVie Research Center, Worcester, Massachusetts
| | - Andrew Long
- Immunology Discovery, AbbVie Research Center, Worcester, Massachusetts
| | - Costantino Pitzalis
- Centre for Experimental Medicine & Rheumatology, William Harvey Research Institute and Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Melanie C Ruzek
- Immunology Discovery, AbbVie Research Center, Worcester, Massachusetts
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9
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Iwasaki T, Watanabe R, Ito H, Fujii T, Okuma K, Oku T, Hirayama Y, Ohmura K, Murata K, Murakami K, Yoshitomi H, Tanaka M, Matsuda S, Matsuda F, Morinobu A, Hashimoto M. Dynamics of Type I and Type II Interferon Signature Determines Responsiveness to Anti-TNF Therapy in Rheumatoid Arthritis. Front Immunol 2022; 13:901437. [PMID: 35734167 PMCID: PMC9208293 DOI: 10.3389/fimmu.2022.901437] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/28/2022] [Indexed: 12/13/2022] Open
Abstract
The factors influencing long-term responses to a tumor necrosis factor inhibitor (TNFi) in rheumatoid arthritis (RA) patients currently remain unknown. Therefore, we herein conducted a multi-omics analysis of TNFi responses in a Japanese RA cohort. Blood samples were collected from 27 biological disease-modifying antirheumatic drug (DMARD)-naive RA patients at the initiation of and after three months of treatment with TNFi. Treatment responses were evaluated at one year. Differences in gene expression levels in peripheral blood mononuclear cells (PBMCs), plasma protein levels, drug concentrations, and the presence/absence of anti-drug antibodies were investigated, and a cell phenotypic analysis of PBMCs was performed using flow cytometry. After one year of treatment, thirteen patients achieved clinical remission (responders), while the others did not or switched to other biologics (non-responders). Differentially expressed genes related to treatment responses were enriched for the interferon (IFN) pathway. The expression of type I IFN signaling-related genes was higher in non-responders than in responders before and after treatment (P = 0.03, 0.005, respectively). The expression of type II IFN signaling-related genes did not significantly differ before treatment; however, it increased in non-responders and decreased in responders, with a significant difference being observed after three months of treatment (P = 1.2×10-3). The total number of lymphocytes and C-X-C Motif Chemokine Ligand 10 (CXCL10) protein levels were associated with the type I IFN signature (P = 6.7×10-7, 6.4×10-3, respectively). Hepatocyte growth factor (HGF) protein levels before treatment predicted fold increases in type II IFN (P = 0.03). These IFN signature-related indices (the number of lymphocytes, CXCL10, and HGF) significantly differed between responders and non-responders (P = 0.01, 0.01, and 0.04, respectively). A single-cell analysis revealed that the type I IFN signature was more highly enriched in monocytes than in other cell types. A deconvolution analysis of bulk-RNA sequence data identified CD4+ and CD8+ T cells as the main sources of the type II IFN signature in non-responders. Collectively, the present results demonstrated that the dynamics of the type I and II IFN pathways affected long-term responses to TNFi, providing information on its biological background and potential for clinical applications.
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Affiliation(s)
- Takeshi Iwasaki
- Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ryu Watanabe
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Clinical Immunology, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
- *Correspondence: Ryu Watanabe, ; Motomu Hashimoto,
| | - Hiromu Ito
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Orthopaedic Surgery, Kurashiki Central Hospital, Okayama, Japan
| | - Takayuki Fujii
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kenji Okuma
- Center for Innovation in Immunoregulative Technology and Therapeutics, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Candidate Discovery Science Labs, Astellas Pharma Inc., Ibaraki, Japan
| | - Takuma Oku
- Center for Innovation in Immunoregulative Technology and Therapeutics, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Candidate Discovery Science Labs, Astellas Pharma Inc., Ibaraki, Japan
| | - Yoshitaka Hirayama
- Center for Innovation in Immunoregulative Technology and Therapeutics, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Candidate Discovery Science Labs, Astellas Pharma Inc., Ibaraki, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Koichi Murata
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Orthopaedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Kosaku Murakami
- Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Hiroyuki Yoshitomi
- Department of Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Masao Tanaka
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shuichi Matsuda
- Department of Orthopaedic Surgery, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akio Morinobu
- Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Motomu Hashimoto
- Department of Advanced Medicine for Rheumatic Diseases, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Clinical Immunology, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
- *Correspondence: Ryu Watanabe, ; Motomu Hashimoto,
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10
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Boegel S, Castle JC, Schwarting A. Current status of use of high throughput nucleotide sequencing in rheumatology. RMD Open 2021; 7:rmdopen-2020-001324. [PMID: 33408124 PMCID: PMC7789458 DOI: 10.1136/rmdopen-2020-001324] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 09/15/2020] [Accepted: 11/24/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE Here, we assess the usage of high throughput sequencing (HTS) in rheumatic research and the availability of public HTS data of rheumatic samples. METHODS We performed a semiautomated literature review on PubMed, consisting of an R-script and manual curation as well as a manual search on the Sequence Read Archive for public available HTS data. RESULTS Of the 699 identified articles, rheumatoid arthritis (n=182 publications, 26%), systemic lupus erythematous (n=161, 23%) and osteoarthritis (n=152, 22%) are among the rheumatic diseases with the most reported use of HTS assays. The most represented assay is RNA-Seq (n=457, 65%) for the identification of biomarkers in blood or synovial tissue. We also find, that the quality of accompanying clinical characterisation of the sequenced patients differs dramatically and we propose a minimal set of clinical data necessary to accompany rheumatological-relevant HTS data. CONCLUSION HTS allows the analysis of a broad spectrum of molecular features in many samples at the same time. It offers enormous potential in novel personalised diagnosis and treatment strategies for patients with rheumatic diseases. Being established in cancer research and in the field of Mendelian diseases, rheumatic diseases are about to become the third disease domain for HTS, especially the RNA-Seq assay. However, we need to start a discussion about reporting of clinical characterisation accompany rheumatological-relevant HTS data to make clinical meaningful use of this data.
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Affiliation(s)
- Sebastian Boegel
- Department of Internal Medicine, University Center of Autoimmunity, University Medical Center Mainz, Mainz, Germany
| | | | - Andreas Schwarting
- Department of Internal Medicine, University Center of Autoimmunity, University Medical Center Mainz, Mainz, Germany.,Division of Rheumatology and Clinical Immunology, University Hospital Mainz, Mainz, Germany.,Acura Rheumatology Center Rhineland Palatinate, Bad Kreuznach, Germany
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11
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Miagoux Q, Singh V, de Mézquita D, Chaudru V, Elati M, Petit-Teixeira E, Niarakis A. Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map. J Pers Med 2021; 11:785. [PMID: 34442429 PMCID: PMC8400381 DOI: 10.3390/jpm11080785] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/02/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients' data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.
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Affiliation(s)
- Quentin Miagoux
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
| | - Vidisha Singh
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
| | - Dereck de Mézquita
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
| | - Valerie Chaudru
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
| | - Mohamed Elati
- CANTHER, University of Lille, CNRS UMR 1277, Inserm U9020, 59045 Lille, France;
| | - Elisabeth Petit-Teixeira
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
| | - Anna Niarakis
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
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12
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Wajda A, Sivitskaya L, Paradowska-Gorycka A. Application of NGS Technology in Understanding the Pathology of Autoimmune Diseases. J Clin Med 2021; 10:3334. [PMID: 34362117 PMCID: PMC8348854 DOI: 10.3390/jcm10153334] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 07/26/2021] [Indexed: 11/16/2022] Open
Abstract
NGS technologies have transformed clinical diagnostics and broadly used from neonatal emergencies to adult conditions where the diagnosis cannot be made based on clinical symptoms. Autoimmune diseases reveal complicate molecular background and traditional methods could not fully capture them. Certainly, NGS technologies meet the needs of modern exploratory research, diagnostic and pharmacotherapy. Therefore, the main purpose of this review was to briefly present the application of NGS technology used in recent years in the understanding of autoimmune diseases paying particular attention to autoimmune connective tissue diseases. The main issues are presented in four parts: (a) panels, whole-genome and -exome sequencing (WGS and WES) in diagnostic, (b) Human leukocyte antigens (HLA) as a diagnostic tool, (c) RNAseq, (d) microRNA and (f) microbiome. Although all these areas of research are extensive, it seems that epigenetic impact on the development of systemic autoimmune diseases will set trends for future studies on this area.
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Affiliation(s)
- Anna Wajda
- Department of Molecular Biology, National Institute of Geriatrics, Rheumatology and Rehabilitation, 02-637 Warsaw, Poland
| | - Larysa Sivitskaya
- Institute of Genetics and Cytology, National Academy of Sciences of Belarus, 220072 Minsk, Belarus
| | - Agnieszka Paradowska-Gorycka
- Department of Molecular Biology, National Institute of Geriatrics, Rheumatology and Rehabilitation, 02-637 Warsaw, Poland
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13
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Yoosuf N, Maciejewski M, Ziemek D, Jelinsky SA, Folkersen L, Müller M, Sahlström P, Vivar N, Catrina A, Berg L, Klareskog L, Padyukov L, Brynedal B. Early Prediction of Clinical Response to Anti-TNF Treatment using Multi-omics and Machine Learning in Rheumatoid Arthritis. Rheumatology (Oxford) 2021; 61:1680-1689. [PMID: 34175943 PMCID: PMC8996791 DOI: 10.1093/rheumatology/keab521] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/18/2021] [Accepted: 06/21/2021] [Indexed: 11/18/2022] Open
Abstract
Objectives Advances in immunotherapy by blocking TNF have remarkably improved treatment outcomes for Rheumatoid arthritis (RA) patients. Although treatment specifically targets TNF, the downstream mechanisms of immune suppression are not completely understood. The aim of this study was to detect biomarkers and expression signatures of treatment response to TNF inhibition. Methods Peripheral blood mononuclear cells (PBMCs) from 39 female patients were collected before anti-TNF treatment initiation (day 0) and after 3 months. The study cohort included patients previously treated with MTX who failed to respond adequately. Response to treatment was defined based on the EULAR criteria and classified 23 patients as responders and 16 as non-responders. We investigated differences in gene expression in PBMCs, the proportion of cell types and cell phenotypes in peripheral blood using flow cytometry and the level of proteins in plasma. Finally, we used machine learning models to predict non-response to anti-TNF treatment. Results The gene expression analysis in baseline samples revealed notably higher expression of the gene EPPK1 in future responders. We detected the suppression of genes and proteins following treatment, including suppressed expression of the T cell inhibitor gene CHI3L1 and its protein YKL-40. The gene expression results were replicated in an independent cohort. Finally, machine learning models mainly based on transcriptomic data showed high predictive utility in classifying non-response to anti-TNF treatment in RA. Conclusions Our integrative multi-omics analyses identified new biomarkers for the prediction of response, found pathways influenced by treatment and suggested new predictive models of anti-TNF treatment in RA patients.
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Affiliation(s)
- Niyaz Yoosuf
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.,Translational Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | | | | | | | | | - Malin Müller
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Peter Sahlström
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Nancy Vivar
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Anca Catrina
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Louise Berg
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Lars Klareskog
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Leonid Padyukov
- Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Boel Brynedal
- Translational Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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14
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A Molecular Signature Response Classifier to Predict Inadequate Response to Tumor Necrosis Factor-α Inhibitors: The NETWORK-004 Prospective Observational Study. Rheumatol Ther 2021; 8:1159-1176. [PMID: 34148193 PMCID: PMC8214458 DOI: 10.1007/s40744-021-00330-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Accepted: 06/03/2021] [Indexed: 12/12/2022] Open
Abstract
Introduction Timely matching of patients to beneficial targeted therapy is an unmet need in rheumatoid arthritis (RA). A molecular signature response classifier (MSRC) that predicts which patients with RA are unlikely to respond to tumor necrosis factor-α inhibitor (TNFi) therapy would have wide clinical utility. Methods The protein–protein interaction map specific to the rheumatoid arthritis pathophysiology and gene expression data in blood patient samples was used to discover a molecular signature of non-response to TNFi therapy. Inadequate response predictions were validated in blood samples from the CERTAIN cohort and a multicenter blinded prospective observational clinical study (NETWORK-004) among 391 targeted therapy-naïve and 113 TNFi-exposed patient samples. The primary endpoint evaluated the ability of the MSRC to identify patients who inadequately responded to TNFi therapy at 6 months according to ACR50. Additional endpoints evaluated the prediction of inadequate response at 3 and 6 months by ACR70, DAS28-CRP, and CDAI. Results The 23-feature molecular signature considers pathways upstream and downstream of TNFα involvement in RA pathophysiology. Predictive performance was consistent between the CERTAIN cohort and NETWORK-004 study. The NETWORK-004 study met primary and secondary endpoints. A molecular signature of non-response was detected in 45% of targeted therapy-naïve patients. The MSRC had an area under the curve (AUC) of 0.64 and patients were unlikely to adequately respond to TNFi therapy according to ACR50 at 6 months with an odds ratio of 4.1 (95% confidence interval 2.0–8.3, p value 0.0001). Odds ratios (3.4–8.8) were significant (p value < 0.01) for additional endpoints at 3 and 6 months, with AUC values up to 0.74. Among TNFi-exposed patients, the MSRC had an AUC of up to 0.83 and was associated with significant odds ratios of 3.3–26.6 by ACR, DAS28-CRP, and CDAI metrics. Conclusion The MSRC stratifies patients according to likelihood of inadequate response to TNFi therapy and provides patient-specific data to guide therapy choice in RA for targeted therapy-naïve and TNFi-exposed patients. Supplementary Information The online version contains supplementary material available at 10.1007/s40744-021-00330-y. A blood-based molecular signature response classifier (MSRC) integrating next-generation RNA sequencing data with clinical features predicts the likelihood that a patient with rheumatoid arthritis will have an inadequate response to TNFi therapy. Treatment selection guided by test results, with likely inadequate responders appropriately redirected to a different therapy, could improve response rates to TNFi therapies, generate healthcare cost savings, and increase rheumatologists’ confidence in prescribing decisions and altered treatment choices. The MSRC described in this study predicts the likelihood of inadequate response to TNFi therapies among targeted therapy-naïve and TNFi-exposed patients in a multicenter, 24-week blinded prospective clinical study: NETWORK-004. Patients with a molecular signature of non-response are less likely to have an adequate response to TNFi therapies than those patients lacking the signature according to ACR50, ACR70, CDAI, and DAS28-CRP with significant odds ratios of 3.4–8.8 for targeted therapy-naïve patients and 3.3–26.6 for TNFi-exposed patients. This MSRC provides a solution to the long-standing need for precision medicine tools to predict drug response in rheumatoid arthritis—a heterogeneous and progressive disease with an abundance of therapeutic options. These data validate the performance of the MSRC in a blinded prospective clinical study of targeted therapy-naïve and TNFi therapy-exposed patients.
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15
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Mezghiche I, Yahia-Cherbal H, Rogge L, Bianchi E. Novel approaches to develop biomarkers predicting treatment responses to TNF-blockers. Expert Rev Clin Immunol 2021; 17:331-354. [PMID: 33622154 DOI: 10.1080/1744666x.2021.1894926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Introduction: Chronic inflammatory diseases (CIDs) cause significant morbidity and are a considerable burden for the patients in terms of pain, impaired function, and diminished quality of life. Important progress in CID treatment has been obtained with biological therapies, such as tumor-necrosis-factor blockers. However, more than a third of the patients fail to respond to these inhibitors and are exposed to the side effects of treatment, without the benefits. Therefore, there is a strong interest in developing tools to predict response of patients to biologics. Areas covered: The authors searched PubMed for recent studies on biomarkers for disease assessment and prediction of therapeutic responses, focusing on the effect of TNF blockers on immune responses in spondyloarthritis (SpA), and other CID, in particular rheumatoid arthritis and inflammatory bowel disease. Conclusions will be drawn about the possible development of predictive biomarkers for response to treatment. Expert opinion: No validated biomarker is currently available to predict treatment response in CID. New insight could be generated through the development of new bioinformatic modeling approaches to combine multidimensional biomarkers that explain the different genetic, immunological and environmental determinants of therapeutic responses.
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Affiliation(s)
- Ikram Mezghiche
- Department of Immunology, Immunoregulation Unit, Institut Pasteur, Paris, France.,Université De Paris, Sorbonne Paris Cité, Paris, France
| | - Hanane Yahia-Cherbal
- Department of Immunology, Immunoregulation Unit, Institut Pasteur, Paris, France.,Fondation AP-HP, Paris, France
| | - Lars Rogge
- Department of Immunology, Immunoregulation Unit, Institut Pasteur, Paris, France.,Unité Mixte AP-HP/Institut Pasteur, Institut Pasteur, Paris, France
| | - Elisabetta Bianchi
- Department of Immunology, Immunoregulation Unit, Institut Pasteur, Paris, France.,Unité Mixte AP-HP/Institut Pasteur, Institut Pasteur, Paris, France
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