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Sharma SD, Leung SH, Viatte S. Genetics of rheumatoid arthritis. Best Pract Res Clin Rheumatol 2024:101968. [PMID: 38955657 DOI: 10.1016/j.berh.2024.101968] [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: 04/29/2024] [Revised: 06/17/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
In the past four decades, a plethora of genetic association studies have been carried out in cohorts of patients with rheumatoid arthritis. These studies have highlighted key aspects of disease pathogenesis and suggested causal mechanisms. In this review, we discuss major advances in our understanding of the genetic architecture of rheumatoid arthritis susceptibility, severity and treatment response and explain how genetics supports current models of disease pathogenesis and outcome. We outline future research directions, like Mendelian randomisation, and present a number of potential avenues for clinical translation, including risk and outcome prediction, patient stratification into treatment response groups and pharmacological applications.
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Affiliation(s)
- Seema D Sharma
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK; NIHR Manchester Musculoskeletal Biomedical Research Centre, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
| | - Shek H Leung
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK.
| | - Sebastien Viatte
- Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, The University of Manchester, Oxford Road, Manchester, M13 9PT, UK; NIHR Manchester Musculoskeletal Biomedical Research Centre, Central Manchester NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK; Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
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2
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Passero K, Noll JG, Verma SS, Selin C, Hall MA. Longitudinal method comparison: modeling polygenic risk for post-traumatic stress disorder over time in individuals of African and European ancestry. Front Genet 2024; 15:1203577. [PMID: 38818035 PMCID: PMC11137250 DOI: 10.3389/fgene.2024.1203577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 04/15/2024] [Indexed: 06/01/2024] Open
Abstract
Cross-sectional data allow the investigation of how genetics influence health at a single time point, but to understand how the genome impacts phenotype development, one must use repeated measures data. Ignoring the dependency inherent in repeated measures can exacerbate false positives and requires the utilization of methods other than general or generalized linear models. Many methods can accommodate longitudinal data, including the commonly used linear mixed model and generalized estimating equation, as well as the less popular fixed-effects model, cluster-robust standard error adjustment, and aggregate regression. We simulated longitudinal data and applied these five methods alongside naïve linear regression, which ignored the dependency and served as a baseline, to compare their power, false positive rate, estimation accuracy, and precision. The results showed that the naïve linear regression and fixed-effects models incurred high false positive rates when analyzing a predictor that is fixed over time, making them unviable for studying time-invariant genetic effects. The linear mixed models maintained low false positive rates and unbiased estimation. The generalized estimating equation was similar to the former in terms of power and estimation, but it had increased false positives when the sample size was low, as did cluster-robust standard error adjustment. Aggregate regression produced biased estimates when predictor effects varied over time. To show how the method choice affects downstream results, we performed longitudinal analyses in an adolescent cohort of African and European ancestry. We examined how developing post-traumatic stress symptoms were predicted by polygenic risk, traumatic events, exposure to sexual abuse, and income using four approaches-linear mixed models, generalized estimating equations, cluster-robust standard error adjustment, and aggregate regression. While the directions of effect were generally consistent, coefficient magnitudes and statistical significance differed across methods. Our in-depth comparison of longitudinal methods showed that linear mixed models and generalized estimating equations were applicable in most scenarios requiring longitudinal modeling, but no approach produced identical results even if fit to the same data. Since result discrepancies can result from methodological choices, it is crucial that researchers determine their model a priori, refrain from testing multiple approaches to obtain favorable results, and utilize as similar as possible methods when seeking to replicate results.
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Affiliation(s)
- Kristin Passero
- Virginia Institute of Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
| | - Jennie G. Noll
- Department of Psychology, Mount Hope Family Center, University of Rochester, Rochester, NY, United States
| | - Shefali Setia Verma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Claire Selin
- Center for Childhood Deafness, Language, and Learning, Boys Town National Research Hospital, Omaha, NE, United States
| | - Molly A. Hall
- Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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3
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Ren Y, Wang L, Dai H, Qiu G, Liu J, Yu D, Liu J, Lyu CZ, Liu L, Zheng M. Genome-wide association analysis of anti-TNF-α treatment response in Chinese patients with psoriasis. Front Pharmacol 2022; 13:968935. [PMID: 36059983 PMCID: PMC9437453 DOI: 10.3389/fphar.2022.968935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 07/28/2022] [Indexed: 11/22/2022] Open
Abstract
Background: TNF-α inhibitors are effective biological agents for treating psoriasis, but the treatment responses differ across patients. This study aimed to identify genetic biomarkers of anti-TNF-α response in Chinese psoriasis patients using a genome-wide association approach. Methods: We recruited two independent cohorts of Chinese psoriasis patients administered etanercept biosimilar (with or without methotrexate). We identified 61 and 87 good responders (PASI improvement ≥75%), 19 and 10 poor responders (PASI improvement <50%) after 24 weeks treatment in the two cohorts, respectively. Then we performed genome-wide association studies (GWAS) on anti-TNF-α response in each cohort independently, followed by a fixed-effects inverse-variance meta-analysis in the 148 good and 29 poor responders. Results: We tested genetic associations with >3 million genetic variants in either cohort. Meta-analysis identified significant associations within seven loci at p < 10−5, which also showed consistent association evidence in the two cohorts. These seven loci include rs2431355 (OR = 6.65, p = 4.46 × 10−7, IQGAP2-F2RL2 on 5q13.3), rs11801616 (OR = 0.11, p = 1.75 × 10−6, SDC3 on 1p35.2), rs3754679 (OR = 0.17, p = 7.71 × 10−6, CNOT11 on 2q11.2), rs13166823 (OR = 0.09, p = 3.71 × 10−6, IRF1-AS1 on 5q31.1), rs10220768 (OR = 5.49, p = 1.48 × 10−6, NPAP1 on 15q11.2), rs4796752 (OR = 5.56, p = 1.49 × 10−6, KRT31 on 17q21.2), and rs13045590 (OR = 0.08, p = 9.67 × 10−7, CTSZ on 20q13.3). Of the seven SNPs, six SNPs showed significant eQTL effect (p < 1 × 10−6) for several genes in multiple tissues. Conclusion: These results suggest novel biological mechanisms and potential biomarkers for the response to anti-TNF therapies. These findings warrant further validation.
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Affiliation(s)
- Yunqing Ren
- Department of Dermatology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China
- Department of Dermatology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ling Wang
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Huatuo Dai
- Department of Dermatology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China
- Department of Dermatology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guiying Qiu
- Department of Dermatology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China
- Department of Dermatology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jipeng Liu
- Department of Dermatology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China
| | - Dianhe Yu
- Department of Dermatology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China
- Department of Dermatology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Cheng-Zhi Lyu
- Department of Dermatology, Dalian Dermatosis Hospital, Dalian, China
| | - Lunfei Liu
- Department of Dermatology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Dermatology, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
- *Correspondence: Lunfei Liu, ; Min Zheng,
| | - Min Zheng
- Department of Dermatology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- *Correspondence: Lunfei Liu, ; Min Zheng,
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4
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-Omic Approaches and Treatment Response in Rheumatoid Arthritis. Pharmaceutics 2022; 14:pharmaceutics14081648. [PMID: 36015273 PMCID: PMC9412998 DOI: 10.3390/pharmaceutics14081648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/22/2022] [Accepted: 08/03/2022] [Indexed: 11/17/2022] Open
Abstract
Rheumatoid arthritis (RA) is an inflammatory disorder characterized by an aberrant activation of innate and adaptive immune cells. There are different drugs used for the management of RA, including disease-modifying antirheumatic drugs (DMARDs). However, a significant percentage of RA patients do not initially respond to DMARDs. This interindividual variation in drug response is caused by a combination of environmental, genetic and epigenetic factors. In this sense, recent -omic studies have evidenced different molecular signatures involved in this lack of response. The aim of this review is to provide an updated overview of the potential role of -omic approaches, specifically genomics, epigenomics, transcriptomics, and proteomics, to identify molecular biomarkers to predict the clinical efficacy of therapies currently used in this disorder. Despite the great effort carried out in recent years, to date, there are still no validated biomarkers of response to the drugs currently used in RA. -Omic studies have evidenced significant differences in the molecular profiles associated with treatment response for the different drugs used in RA as well as for different cell types. Therefore, global and cell type-specific -omic studies analyzing response to the complete therapeutical arsenal used in RA, including less studied therapies, such as sarilumab and JAK inhibitors, are greatly needed.
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Gene Ontology Analysis Highlights Biological Processes Influencing Non-Response to Anti-TNF Therapy in Rheumatoid Arthritis. Biomedicines 2022; 10:biomedicines10081808. [PMID: 36009355 PMCID: PMC9404936 DOI: 10.3390/biomedicines10081808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/22/2022] [Accepted: 07/26/2022] [Indexed: 11/20/2022] Open
Abstract
Anti-TNF therapy has significantly improved disease control in rheumatoid arthritis, but a fraction of rheumatoid arthritis patients do not respond to anti-TNF therapy or lose response over time. Moreover, the mechanisms underlying non-response to anti-TNF therapy remain largely unknown. To date, many single biomarkers of response to anti-TNF therapy have been published but they have not yet been analyzed as a system of interacting nodes. The aim of our study is to systematically elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis using the gene ontologies of previously published predictive biomarkers. Gene networks were constructed based on published biomarkers and then enriched gene ontology terms were elucidated in subgroups using gene ontology software tools. Our results highlight the novel role of proteasome-mediated protein catabolic processes (p = 2.91 × 10−15) and plasma lipoproteins (p = 4.55 × 10−11) in anti-TNF therapy response. The results of our gene ontology analysis help elucidate the biological processes underlying non-response to anti-TNF therapy in rheumatoid arthritis and encourage further study of the highlighted processes.
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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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7
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White IR, Kleinstein SE, Praet C, Chamberlain C, McHale D, Maia JM, Xie P, Goldstein DB, Urban TJ, Shea PR. A genome-wide screen for variants influencing certolizumab pegol response in a moderate to severe rheumatoid arthritis population. PLoS One 2022; 17:e0261165. [PMID: 35413058 PMCID: PMC9004786 DOI: 10.1371/journal.pone.0261165] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 11/24/2021] [Indexed: 12/14/2022] Open
Abstract
Certolizumab pegol (CZP) is a PEGylated Fc-free tumor necrosis factor (TNF) inhibitor antibody approved for use in the treatment of rheumatoid arthritis (RA), Crohn’s disease, psoriatic arthritis, axial spondyloarthritis and psoriasis. In a clinical trial of patients with severe RA, CZP improved disease symptoms in approximately half of patients. However, variability in CZP efficacy remains a problem for clinicians, thus, the aim of this study was to identify genetic variants predictive of CZP response. We performed a genome-wide association study (GWAS) of 302 RA patients treated with CZP in the REALISTIC trial to identify common single nucleotide polymorphisms (SNPs) associated with treatment response. Whole-exome sequencing was also performed for 74 CZP extreme responders and non-responders within the same population, as well as 1546 population controls. No common SNPs or rare functional variants were significantly associated with CZP response, though a non-significant enrichment in the RA-implicated KCNK5 gene was observed. Two SNPs near spondin-1 and semaphorin-4G approached genome-wide significance. The results of the current study did not provide an unambiguous predictor of CZP response.
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Affiliation(s)
- Ian R. White
- Experimental Medicine and Diagnostics, UCB Celltech, Slough, United Kingdom
| | - Sarah E. Kleinstein
- Institute for Genomic Medicine, Columbia University, New York, New York, United States of America
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, North Carolina, United States of America
| | | | - Chris Chamberlain
- Experimental Medicine and Diagnostics, UCB Celltech, Slough, United Kingdom
| | - Duncan McHale
- Experimental Medicine and Diagnostics, UCB Celltech, Slough, United Kingdom
| | - Jessica M. Maia
- Institute for Genomic Medicine, Columbia University, New York, New York, United States of America
| | - Pingxing Xie
- Institute for Genomic Medicine, Columbia University, New York, New York, United States of America
- Faculty of Medicine, McGill University, Montréal, Québec, Canada
| | - David B. Goldstein
- Institute for Genomic Medicine, Columbia University, New York, New York, United States of America
| | - Thomas J. Urban
- Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Patrick R. Shea
- Institute for Genomic Medicine, Columbia University, New York, New York, United States of America
- * E-mail:
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8
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Zhao J, Wei K, Chang C, Xu L, Jiang P, Guo S, Schrodi SJ, He D. DNA Methylation of T Lymphocytes as a Therapeutic Target: Implications for Rheumatoid Arthritis Etiology. Front Immunol 2022; 13:863703. [PMID: 35309322 PMCID: PMC8927780 DOI: 10.3389/fimmu.2022.863703] [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: 01/27/2022] [Accepted: 02/14/2022] [Indexed: 11/28/2022] Open
Abstract
Rheumatoid arthritis (RA) is an autoimmune disease that can cause joint damage and disability. Epigenetic variation, especially DNA methylation, has been shown to be involved in almost all the stages of the pathology of RA, from autoantibody production to various self-effector T cells and the defects of protective T cells that can lead to chronic inflammation and erosion of bones and joints. Given the critical role of T cells in the pathology of RA, the regulatory functions of DNA methylation in T cell biology remain unclear. In this review, we elaborate on the relationship between RA pathogenesis and DNA methylation in the context of different T cell populations. We summarize the relevant methylation events in T cell development, differentiation, and T cell-related genes in disease prediction and drug efficacy. Understanding the epigenetic regulation of T cells has the potential to profoundly translate preclinical results into clinical practice and provide a framework for the development of novel, individualized RA therapeutics.
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Affiliation(s)
- Jianan Zhao
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Kai Wei
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Cen Chang
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Lingxia Xu
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Ping Jiang
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China
| | - Shicheng Guo
- Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI, United States.,Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Steven J Schrodi
- Computation and Informatics in Biology and Medicine, University of Wisconsin-Madison, Madison, WI, United States.,Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Dongyi He
- Guanghua Clinical Medical College, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Department of Rheumatology, Shanghai Guanghua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Institute of Arthritis Research in Integrative Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, China.,Arthritis Institute of Integrated Traditional and Western medicine, Shanghai Chinese Medicine Research Institute, Shanghai, China
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9
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Roodenrijs NMT, Welsing PMJ, van Roon J, Schoneveld JLM, van der Goes MC, Nagy G, Townsend MJ, van Laar JM. Mechanisms underlying DMARD inefficacy in difficult-to-treat rheumatoid arthritis: a narrative review with systematic literature search. Rheumatology (Oxford) 2022; 61:3552-3566. [PMID: 35238332 PMCID: PMC9434144 DOI: 10.1093/rheumatology/keac114] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/07/2022] [Accepted: 02/14/2022] [Indexed: 12/03/2022] Open
Abstract
Management of RA patients has significantly improved over the past decades. However, a substantial proportion of patients is difficult-to-treat (D2T), remaining symptomatic after failing biological and/or targeted synthetic DMARDs. Multiple factors can contribute to D2T RA, including treatment non-adherence, comorbidities and co-existing mimicking diseases (e.g. fibromyalgia). Additionally, currently available biological and/or targeted synthetic DMARDs may be truly ineffective (‘true’ refractory RA) and/or lead to unacceptable side effects. In this narrative review based on a systematic literature search, an overview of underlying (immune) mechanisms is presented. Potential scenarios are discussed including the influence of different levels of gene expression and clinical characteristics. Although the exact underlying mechanisms remain largely unknown, the heterogeneity between individual patients supports the assumption that D2T RA is a syndrome involving different pathogenic mechanisms.
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Affiliation(s)
- Nadia M T Roodenrijs
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Paco M J Welsing
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Joel van Roon
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Jan L M Schoneveld
- Department of Rheumatology, Bravis Hospital, Roosendaal, the Netherlands
| | - Marlies C van der Goes
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, the Netherlands.,Department of Rheumatology, Meander Medical Center, Amersfoort, the Netherlands
| | - György Nagy
- Department of Rheumatology & Clinical Immunology, Semmelweis University, Budapest, Hungary.,Department of Genetics, Cell and Immunobiology, Semmelweis University, Budapest, Hungary
| | - Michael J Townsend
- Biomarker Discovery OMNI, Genentech Research & Early Development, South San Francisco, USA
| | - Jacob M van Laar
- Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Utrecht University, the Netherlands
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10
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Aluko A, Ranganathan P. Pharmacogenetics of Drug Therapies in Rheumatoid Arthritis. Methods Mol Biol 2022; 2547:527-567. [PMID: 36068476 DOI: 10.1007/978-1-0716-2573-6_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Rheumatoid arthritis (RA) is a chronic systemic inflammatory disorder that can lead to severe joint damage and is often associated with a high morbidity and disability. Disease-modifying anti-rheumatic drugs (DMARDs) are the mainstay of treatment in RA. DMARDs not only relieve the clinical signs and symptoms of RA but also inhibit the radiographic progression of disease and reduce the effects of chronic systemic inflammation. Since the introduction of biologic DMARDs in the late 1990s, the therapeutic range of options for the management of RA has significantly expanded. However, patients' response to these agents is not uniform with considerable variability in both efficacy and toxicity. There are no reliable means of predicting an individual patient's response to a given DMARD prior to initiation of therapy. In this chapter, the current published literature on the pharmacogenetics of traditional DMARDS and the newer biologic DMARDs in RA is highlighted. Pharmacogenetics may help individualize drug therapy in patients with RA by providing reliable biomarkers to predict medication toxicity and efficacy.
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Affiliation(s)
- Atinuke Aluko
- Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Prabha Ranganathan
- Division of Rheumatology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
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11
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Sánchez-Maldonado JM, Cáliz R, López-Nevot MÁ, Cabrera-Serrano AJ, Moñiz-Díez A, Canhão H, Ter Horst R, Quartuccio L, Sorensen SB, Glintborg B, Hetland ML, Filipescu I, Pérez-Pampin E, Conesa-Zamora P, Swierkot J, den Broeder AA, De Vita S, Petersen ERB, Li Y, Ferrer MA, Escudero A, Netea MG, Coenen MJH, Andersen V, Fonseca JE, Jurado M, Bogunia-Kubik K, Collantes E, Sainz J. Validation of GWAS-Identified Variants for Anti-TNF Drug Response in Rheumatoid Arthritis: A Meta-Analysis of Two Large Cohorts. Front Immunol 2021; 12:672255. [PMID: 34777329 PMCID: PMC8579100 DOI: 10.3389/fimmu.2021.672255] [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: 02/25/2021] [Accepted: 10/11/2021] [Indexed: 12/29/2022] Open
Abstract
We aimed to validate the association of 28 GWAS-identified genetic variants for response to TNF inhibitors (TNFi) in a discovery cohort of 1361 rheumatoid arthritis (RA) patients monitored in routine care and ascertained through the REPAIR consortium and DANBIO registry. We genotyped selected markers and evaluated their association with response to TNFi after 6 months of treatment according to the change in disease activity score 28 (ΔDAS28). Next, we confirmed the most interesting results through meta-analysis of our data with those from the DREAM cohort that included 706 RA patients treated with TNFi. The meta-analysis of the discovery cohort and DREAM registry including 2067 RA patients revealed an overall association of the LINC02549rs7767069 SNP with a lower improvement in DAS28 that remained significant after correction for multiple testing (per-allele ORMeta=0.83, PMeta=0.000077; PHet=0.61). In addition, we found that each copy of the LRRC55rs717117G allele was significantly associated with lower improvement in DAS28 in rheumatoid factor (RF)-positive patients (per-allele ORMeta=0.67, P=0.00058; PHet=0.06) whereas an opposite but not significant effect was detected in RF-negative subjects (per-allele ORMeta=1.38, P=0.10; PHet=0.45; PInteraction=0.00028). Interestingly, although the identified associations did not survive multiple testing correction, the meta-analysis also showed overall and RF-specific associations for the MAFBrs6071980 and CNTN5rs1813443 SNPs with decreased changes in DAS28 (per-allele ORMeta_rs6071980 = 0.85, P=0.0059; PHet=0.63 and ORMeta_rs1813443_RF+=0.81, P=0.0059; PHet=0.69 and ORMeta_rs1813443_RF-=1.00, P=0.99; PHet=0.12; PInteraction=0.032). Mechanistically, we found that subjects carrying the LINC02549rs7767069T allele had significantly increased numbers of CD45RO+CD45RA+ T cells (P=0.000025) whereas carriers of the LINC02549rs7767069T/T genotype showed significantly increased levels of soluble scavengers CD5 and CD6 in serum (P=0.00037 and P=0.00041). In addition, carriers of the LRRC55rs717117G allele showed decreased production of IL6 after stimulation of PBMCs with B burgdorferi and E coli bacteria (P=0.00046 and P=0.00044), which suggested a reduced IL6-mediated anti-inflammatory effect of this marker to worsen the response to TNFi. In conclusion, this study confirmed the influence of the LINC02549 and LRRC55 loci to determine the response to TNFi in RA patients and suggested a weak effect of the MAFB and CNTN5 loci that need to be further investigated.
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Affiliation(s)
- Jose Manuel Sánchez-Maldonado
- Genomic Oncology Area, Centre for Genomics and Oncological Research (GENYO), Parque tecnológico de la Salud (PTS) Granada, Granada, Spain.,Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain.,Instituto de Investigación Biosanitaria (IBs) Granada, Granada, Spain
| | - Rafael Cáliz
- Genomic Oncology Area, Centre for Genomics and Oncological Research (GENYO), Parque tecnológico de la Salud (PTS) Granada, Granada, Spain.,Instituto de Investigación Biosanitaria (IBs) Granada, Granada, Spain.,Department of Rheumatology, Virgen de las Nieves University Hospital, Granada, Spain
| | - Miguel Ángel López-Nevot
- Instituto de Investigación Biosanitaria (IBs) Granada, Granada, Spain.,Immunology Department, Virgen de las Nieves University Hospital, Granada, Spain
| | - Antonio José Cabrera-Serrano
- Genomic Oncology Area, Centre for Genomics and Oncological Research (GENYO), Parque tecnológico de la Salud (PTS) Granada, Granada, Spain.,Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain.,Instituto de Investigación Biosanitaria (IBs) Granada, Granada, Spain
| | - Ana Moñiz-Díez
- Genomic Oncology Area, Centre for Genomics and Oncological Research (GENYO), Parque tecnológico de la Salud (PTS) Granada, Granada, Spain.,Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain.,Instituto de Investigación Biosanitaria (IBs) Granada, Granada, Spain
| | - Helena Canhão
- EpiDoC Unit, CEDOC, NOVA Medical School and National School of Public Health, Universidade Nova de Lisboa, Lisbon, Portugal.,Comprehensive Health Research Center (CHRC), NOVA Medical School, Lisbon, Portugal
| | - Rob Ter Horst
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Nijmegen Medical Center, Nijmegen, Netherlands
| | - Luca Quartuccio
- Department of Medical Area, Clinic of Rheumatology, University of Udine, Udine, Italy
| | - Signe B Sorensen
- Molecular Diagnostic and Clinical Research Unit, IRS-Center Sonderjylland, University Hospital of Southern Jutland, Aabenraa, Denmark.,Institute of Molecular Medicine, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Bente Glintborg
- The Danish Rheumatologic Biobank and Copenhagen Center for Arthritis Research (DANBIO) Registry, The Danish Rheumatologic Biobank and Copenhagen Center for Arthritis Research (COPECARE), Center for Rheumatology and Spine Diseases, Centre of Head and Orthopaedics, Rigshospitalet, Glostrup, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Merete L Hetland
- The Danish Rheumatologic Biobank and Copenhagen Center for Arthritis Research (DANBIO) Registry, The Danish Rheumatologic Biobank and Copenhagen Center for Arthritis Research (COPECARE), Center for Rheumatology and Spine Diseases, Centre of Head and Orthopaedics, Rigshospitalet, Glostrup, Denmark.,Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ileana Filipescu
- Rheumatology Department, University of Medicine and Pharmacy "Iuliu Hatieganu", Cluj-Napoca, Romania
| | - Eva Pérez-Pampin
- Rheumatology Unit, University Hospital of Santiago de Compostela, Santiago de Compostela, Spain
| | - Pablo Conesa-Zamora
- Clinical Analysis Department, Santa Lucía University Hospital, Cartagena, Spain
| | - Jerzy Swierkot
- Department of Rheumatology and Internal Medicine, Wroclaw Medical University, Wroclaw, Poland
| | - Alfons A den Broeder
- Radboud Institute for Health Sciences, Department of Rheumatology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Salvatore De Vita
- Department of Medical Area, Clinic of Rheumatology, University of Udine, Udine, Italy
| | - Eva Rabing Brix Petersen
- Department of Biochemistry and Immunology, University Hospital of Southern Jutland, Aabenraa, Denmark
| | - Yang Li
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Nijmegen Medical Center, Nijmegen, Netherlands.,Centre for Individualised Infection Medicine (CiiM) & Centre for Experimental and Clinical Infection Research (TWINCORE), Helmholtz-Centre for Infection Research (HZI) and The Hannover Medical School (MHH), Hannover, Germany
| | - Miguel A Ferrer
- Instituto de Investigación Biosanitaria (IBs) Granada, Granada, Spain
| | - Alejandro Escudero
- Rheumatology Department, Reina Sofía Hospital/Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC)/University of Córdoba, Córdoba, Spain
| | - Mihai G Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Nijmegen Medical Center, Nijmegen, Netherlands.,Department for Immunology & Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany
| | - Marieke J H Coenen
- Radboud Institute for Health Sciences, Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
| | - Vibeke Andersen
- Department of Medical Area, Clinic of Rheumatology, University of Udine, Udine, Italy.,Molecular Diagnostic and Clinical Research Unit, IRS-Center Sonderjylland, University Hospital of Southern Jutland, Aabenraa, Denmark.,Institute of Regional Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - João E Fonseca
- Rheumatology and Metabolic Bone Diseases Department, Hospital de Santa Maria, Centro Hospitalar Universitário Lisboa Norte (CHLN), Lisbon, Portugal.,Rheumatology Research Unit, Instituto de Medicina Molecular, Faculty of Medicine, University of Lisbon, Lisbon Academic Medical Center, Lisbon, Portugal
| | - Manuel Jurado
- Genomic Oncology Area, Centre for Genomics and Oncological Research (GENYO), Parque tecnológico de la Salud (PTS) Granada, Granada, Spain.,Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain.,Instituto de Investigación Biosanitaria (IBs) Granada, Granada, Spain
| | - Katarzyna Bogunia-Kubik
- Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, Wrocław, Poland
| | - Eduardo Collantes
- Rheumatology Department, Reina Sofía Hospital/Instituto Maimónides de Investigación Biomédica de Córdoba (IMIBIC)/University of Córdoba, Córdoba, Spain
| | - Juan Sainz
- Genomic Oncology Area, Centre for Genomics and Oncological Research (GENYO), Parque tecnológico de la Salud (PTS) Granada, Granada, Spain.,Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain.,Instituto de Investigación Biosanitaria (IBs) Granada, Granada, Spain.,Department of Biochemistry and Molecular Biology I, University of Granada, Granada, Spain
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12
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Yang M, Yi P, Jiang J, Zhao M, Wu H, Lu Q. Dysregulated translational factors and epigenetic regulations orchestrate in B cells contributing to autoimmune diseases. Int Rev Immunol 2021; 42:1-25. [PMID: 34445929 DOI: 10.1080/08830185.2021.1964498] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
B cells play a crucial role in antigen presentation, antibody production and pro-/anti-inflammatory cytokine secretion in adaptive immunity. Several translational factors including transcription factors and cytokines participate in the regulation of B cell development, with the cooperation of epigenetic regulations. Autoimmune diseases are generally characterized with autoreactive B cells and high-level pathogenic autoantibodies. The success of B cell depletion therapy in mouse model and clinical trials has proven the role of B cells in pathogenesis of autoimmune diseases. The failure of B cell tolerance in immune checkpoints results in accumulated autoreactive naïve B (BN) cells with aberrant B cell receptor signaling and dysregulated B cell response, contributing to self-antibody-mediated autoimmune reaction. Dysregulation of translational factors and epigenetic alterations in B cells has been demonstrated to correlate with aberrant B cell compartment in autoimmune diseases, such as systemic lupus erythematosus, rheumatoid arthritis, primary Sjögren's syndrome, multiple sclerosis, diabetes mellitus and pemphigus. This review is intended to summarize the interaction of translational factors and epigenetic regulations that are involved with development and differentiation of B cells, and the mechanism of dysregulation in the pathogenesis of autoimmune diseases.
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Affiliation(s)
- Ming Yang
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha, Hunan, China
| | - Ping Yi
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha, Hunan, China
| | - Jiao Jiang
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha, Hunan, China
| | - Ming Zhao
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha, Hunan, China
| | - Haijing Wu
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha, Hunan, China
| | - Qianjin Lu
- Department of Dermatology, Second Xiangya Hospital, Central South University, Hunan Key Laboratory of Medical Epigenomics, Changsha, Hunan, China.,Department of Dermatology, Institute of Dermatology, Chinese Academy of Medical Sciences and Peking Union Medical College, Nanjing, Jiangsu, China
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13
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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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14
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Abstract
Patients with inflammatory bowel disease (IBD) show large variability in disease course, and also treatment response. The variability in treatment response has led to many initiatives in search of genetic markers to optimize treatment and avoid severe side effects. This has been very successful for thiopurines, one of the drugs used to induce and maintain remission in IBD. However, for the newer treatment options for IBD, like biologicals, the search for genetic predictors has not yielded any candidate biomarkers with clinical utility. In this review, a summary of recent advances in pharmacogenetics focusing on thiopurines and anti-TNF agents is given.
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Affiliation(s)
- Bianca Jc van den Bosch
- Deparment of Clinical Genetics, Maastricht University Medical Center, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands
| | - Marieke Jh Coenen
- Department of Human Genetics, Radboud Institute for Health Sciences, Radboud University Medical Center, P.O. Box 9101, 6500HB, Nijmegen, The Netherlands
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15
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Guan Y, Zhang H, Quang D, Wang Z, Parker SCJ, Pappas DA, Kremer JM, Zhu F. Machine Learning to Predict Anti-Tumor Necrosis Factor Drug Responses of Rheumatoid Arthritis Patients by Integrating Clinical and Genetic Markers. Arthritis Rheumatol 2019; 71:1987-1996. [PMID: 31342661 DOI: 10.1002/art.41056] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 07/18/2019] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Accurate prediction of treatment responses in rheumatoid arthritis (RA) patients can provide valuable information on effective drug selection. Anti-tumor necrosis factor (anti-TNF) drugs are an important second-line treatment after methotrexate, the classic first-line treatment for RA. However, patient heterogeneity hinders identification of predictive biomarkers and accurate modeling of anti-TNF drug responses. This study was undertaken to investigate the usefulness of machine learning to assist in developing predictive models for treatment response. METHODS Using data on patient demographics, baseline disease assessment, treatment, and single-nucleotide polymorphism (SNP) array from the Dialogue on Reverse Engineering Assessment and Methods (DREAM): Rheumatoid Arthritis Responder Challenge, we created a Gaussian process regression model to predict changes in the Disease Activity Score in 28 joints (DAS28) for the patients and to classify them into either the responder or the nonresponder group. This model was developed and cross-validated using data from 1,892 RA patients. It was evaluated using an independent data set from 680 patients. We examined the effectiveness of the similarity modeling and the contribution of individual features. RESULTS In the cross-validation tests, our method predicted changes in DAS28 (ΔDAS28), with a correlation coefficient of 0.405. It correctly classified responses from 78% of patients. In the independent test, this method achieved a Pearson's correlation coefficient of 0.393 in predicting ΔDAS28. Gaussian process regression effectively remapped the feature space and identified subpopulations that do not respond well to anti-TNF treatments. Genetic SNP biomarkers showed small contributions in the prediction when added to the clinical models. This was the best-performing model in the DREAM Challenge. CONCLUSION The model described here shows promise in guiding treatment decisions in clinical practice, based primarily on clinical profiles with additional genetic information.
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Affiliation(s)
| | | | | | | | | | - Dimitrios A Pappas
- Columbia University College of Physicians and Surgeons, New York, New York, and Corrona LLC, Waltham, Massachusetts
| | - Joel M Kremer
- Corrona LLC, Waltham, Massachusetts, and Albany Medical College and The Center for Rheumatology, Albany, New York
| | - Fan Zhu
- Chinese Academy of Sciences, Chongqing, China
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16
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Aterido A, Cañete JD, Tornero J, Blanco F, Fernández-Gutierrez B, Pérez C, Alperi-López M, Olivè A, Corominas H, Martínez-Taboada V, González I, Fernández-Nebro A, Erra A, López-Lasanta M, López Corbeto M, Palau N, Marsal S, Julià A. A Combined Transcriptomic and Genomic Analysis Identifies a Gene Signature Associated With the Response to Anti-TNF Therapy in Rheumatoid Arthritis. Front Immunol 2019; 10:1459. [PMID: 31312201 PMCID: PMC6614444 DOI: 10.3389/fimmu.2019.01459] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 06/10/2019] [Indexed: 12/14/2022] Open
Abstract
Background: Rheumatoid arthritis (RA) is the most frequent autoimmune disease involving the joints. Although anti-TNF therapies have proven effective in the management of RA, approximately one third of patients do not show a significant clinical response. The objective of this study was to identify new genetic variation associated with the clinical response to anti-TNF therapy in RA. Methods: We performed a sequential multi-omic analysis integrating different sources of molecular information. First, we extracted the RNA from synovial biopsies of 11 RA patients starting anti-TNF therapy to identify gene coexpression modules (GCMs) in the RA synovium. Second, we analyzed the transcriptomic association between each GCM and the clinical response to anti-TNF therapy. The clinical response was determined at week 14 using the EULAR criteria. Third, we analyzed the association between the GCMs and anti-TNF response at the genetic level. For this objective, we used genome-wide data from a cohort of 348 anti-TNF treated patients from Spain. The GCMs that were significantly associated with the anti-TNF response were then tested for validation in an independent cohort of 2,706 anti-TNF treated patients. Finally, the functional implication of the validated GCMs was evaluated via pathway and cell type epigenetic enrichment analyses. Results: A total of 149 GCMs were identified in the RA synovium. From these, 13 GCMs were found to be significantly associated with anti-TNF response (P < 0.05). At the genetic level, we detected two of the 13 GCMs to be significantly associated with the response to adalimumab (P = 0.0015) and infliximab (P = 0.021) in the Spain cohort. Using the independent cohort of RA patients, we replicated the association of the GCM associated with the response to adalimumab (P = 0.0019). The validated module was found to be significantly enriched for genes involved in the nucleotide metabolism (P = 2.41e-5) and epigenetic marks from immune cells, including CD4+ regulatory T cells (P = 0.041). Conclusions: These findings show the existence of a drug-specific genetic basis for anti-TNF response, thereby supporting treatment stratification in the search for response biomarkers in RA.
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Affiliation(s)
- Adrià Aterido
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain.,Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Juan D Cañete
- Rheumatology Department, Hospital Clínic de Barcelona and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Jesús Tornero
- Rheumatology Department, Hospital Universitario De Guadalajara, Guadalajara, Spain
| | - Francisco Blanco
- Rheumatology Department, INIBIC-Hospital Universitario A Coruña, A Coruña, Spain
| | | | - Carolina Pérez
- Rheumatology Department, Parc de Salut Mar, Barcelona, Spain
| | | | - Alex Olivè
- Rheumatology Department, Hospital Universitari Germans Trias i Pujol, Barcelona, Spain
| | - Héctor Corominas
- Rheumatology Department, Hospital Moisès Broggi, Barcelona, Spain
| | | | - Isidoro González
- Rheumatology Department, Hospital Universitario La Princesa, IIS La Princesa, Madrid, Spain
| | - Antonio Fernández-Nebro
- UGC Reumatología, Instituto Investigación Biomédica Málaga, Hospital Regional Universitario, Universidad de Málaga, Málaga, Spain
| | - Alba Erra
- Rheumatology Department, Hospital Sant Rafael, Barcelona, Spain
| | - María López-Lasanta
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain
| | | | - Núria Palau
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain
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17
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Wells PM, Williams FMK, Matey-Hernandez ML, Menni C, Steves CJ. 'RA and the microbiome: do host genetic factors provide the link? J Autoimmun 2019; 99:104-115. [PMID: 30850234 PMCID: PMC6470121 DOI: 10.1016/j.jaut.2019.02.004] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 02/20/2019] [Accepted: 02/20/2019] [Indexed: 12/29/2022]
Abstract
Rheumatoid arthritis (RA) is a chronic autoimmune disease, characterised by painful synovium inflammation, bony erosions, immune activation and the circulation of autoantibodies. Despite recent advances in therapeutics enabling disease suppression, there is a considerable demand for alternative therapeutic strategies as well as optimising those available at present. The relatively low concordance rate between monozygotic twins, 20–30% contrasts with heritability estimates of ∼65%, indicating a substantive role of other risk factors in RA pathogenesis. There is established evidence that RA has an infective component to its aetiology. More recently, differences in the commensal microbiota in RA compared to controls have been identified. Studies have shown that the gut, oral and lung microbiota is different in new onset treatment naïve, and established RA patients, compared to controls. Key taxonomic associations are an increase in abundance of Porphyromonas gingivalis and Prevotella copri in RA patients, compared to healthy controls. Host genetics may provide the link between disease and the microbiome. Genetic influence may be mediated by the host immune system; a differential response to RA associated taxa is suggested. The gut microbiome contains elements which are as much as 30% heritable. A better understanding of the influence of host genetics will shed light onto the role of the microbiome in RA. Here we review the role of the microbiome in RA through the lens of host genetics, and consider future research areas addressing microbiome study design and bioinformatics approaches. Rheumatoid arthritis (RA) affects 1% of the population and is highly debilitating. RA is ~65% heritable, yet the concordance rate between monozygotic twins is just 20–30%, indicating a substantive role of other risk factors. Studies have shown that the gut, oral and lung microbiome is different in treatment naïve and established RA patients, compared to controls. Current findings suggest an important influence of host genetics on the microbiome, which may contribute to RA via the host immune system. Associations of the microbiome with RA described thus far are confounded by host genetics, and future studies need to take account of this.
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Affiliation(s)
- Philippa M Wells
- The Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, Lambeth Palace Road, London, SE1 7EH, UK.
| | - Frances M K Williams
- The Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, Lambeth Palace Road, London, SE1 7EH, UK
| | - M L Matey-Hernandez
- The Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, Lambeth Palace Road, London, SE1 7EH, UK
| | - Cristina Menni
- The Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, Lambeth Palace Road, London, SE1 7EH, UK
| | - Claire J Steves
- The Department of Twin Research and Genetic Epidemiology, King's College London, St Thomas' Hospital, Lambeth Palace Road, London, SE1 7EH, UK; Clinical Age Research Unit, Kings College Hospital Foundation Trust, London, UK
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18
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Evaluation of 12 GWAS-drawn SNPs as biomarkers of rheumatoid arthritis response to TNF inhibitors. A potential SNP association with response to etanercept. PLoS One 2019; 14:e0213073. [PMID: 30818333 PMCID: PMC6395028 DOI: 10.1371/journal.pone.0213073] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 02/14/2019] [Indexed: 12/14/2022] Open
Abstract
Research in rheumatoid arthritis (RA) is increasingly focused on the discovery of biomarkers that could enable personalized treatments. The genetic biomarkers associated with the response to TNF inhibitors (TNFi) are among the most studied. They include 12 SNPs exhibiting promising results in the three largest genome-wide association studies (GWAS). However, they still require further validation. With this aim, we assessed their association with response to TNFi in a replication study, and a meta-analysis summarizing all non-redundant data. The replication involved 755 patients with RA that were treated for the first time with a biologic drug, which was either infliximab (n = 397), etanercept (n = 155) or adalimumab (n = 203). Their DNA samples were successfully genotyped with a single-base extension multiplex method. Lamentably, none of the 12 SNPs was associated with response to the TNFi in the replication study (p > 0.05). However, a drug-stratified exploratory analysis revealed a significant association of the NUBPL rs2378945 SNP with a poor response to etanercept (B = -0.50, 95% CI = -0.82, -0.17, p = 0.003). In addition, the meta-analysis reinforced the previous association of three SNPs: rs2378945, rs12142623, and rs4651370. In contrast, five of the remaining SNPs were less associated than before, and the other four SNPs were no longer associated with the response to treatment. In summary, our results highlight the complexity of the pharmacogenetics of TNFi in RA showing that it could involve a drug-specific component and clarifying the status of the 12 GWAS-drawn SNPs.
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19
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Zamanpoor M. The genetic pathogenesis, diagnosis and therapeutic insight of rheumatoid arthritis. Clin Genet 2019; 95:547-557. [PMID: 30578544 DOI: 10.1111/cge.13498] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 12/18/2018] [Accepted: 12/19/2018] [Indexed: 12/16/2022]
Abstract
Rheumatoid arthritis (RA) is a systemic autoimmune disease that causes chronic inflammation of the joints. RA is a heterogeneous disorder caused by an abnormal autoimmune response triggered by the complex interactions of genetic and environmental factors that contribute to RA etiology. However, its underlying pathogenic mechanisms are yet to be fully understood. In this review, I provide an overview of the pathogenesis, diagnosis and therapeutic insight in the clinical management of RA in light of the recent updates to classification criteria and recent discoveries of genetic loci associated with susceptibility for RA.
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Affiliation(s)
- Mansour Zamanpoor
- Department of Biochemistry, University of Otago, Dunedin, New Zealand.,Wellington Regional Genetics Laboratory, Genetic Health Service New Zealand, Wellington Regional Hospital, Wellington, New Zealand
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20
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Okada Y, Eyre S, Suzuki A, Kochi Y, Yamamoto K. Genetics of rheumatoid arthritis: 2018 status. Ann Rheum Dis 2018; 78:446-453. [PMID: 30530827 DOI: 10.1136/annrheumdis-2018-213678] [Citation(s) in RCA: 117] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 11/06/2018] [Accepted: 11/07/2018] [Indexed: 01/08/2023]
Abstract
Study of the genetics of rheumatoid arthritis (RA) began about four decades ago with the discovery of HLA-DRB1 Since the beginning of this century, a number of non-HLA risk loci have been identified through genome-wide association studies (GWAS). We now know that over 100 loci are associated with RA risk. Because genetic information implies a clear causal relationship to the disease, research into the pathogenesis of RA should be promoted. However, only 20% of GWAS loci contain coding variants, with the remaining variants occurring in non-coding regions, and therefore, the majority of causal genes and causal variants remain to be identified. The use of epigenetic studies, high-resolution mapping of open chromatin, chromosomal conformation technologies and other approaches could identify many of the missing links between genetic risk variants and causal genetic components, thus expanding our understanding of RA genetics.
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Affiliation(s)
- Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan.,Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Stephen Eyre
- Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, The University of Manchester, Manchester, UK
| | - Akari Suzuki
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yuta Kochi
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kazuhiko Yamamoto
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
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21
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Lopez-Rodriguez R, Perez-Pampin E, Marquez A, Blanco FJ, Joven B, Carreira P, Ferrer MA, Caliz R, Valor L, Narvaez J, Cañete JD, Ordoñez MDC, Manrique-Arija S, Vasilopoulos Y, Balsa A, Pascual-Salcedo D, Moreno-Ramos MJ, Alegre-Sancho JJ, Navarro-Sarabia F, Moreira V, Garcia-Portales R, Raya E, Magro-Checa C, Martin J, Gomez-Reino JJ, Gonzalez A. Validation study of genetic biomarkers of response to TNF inhibitors in rheumatoid arthritis. PLoS One 2018; 13:e0196793. [PMID: 29734345 PMCID: PMC5937760 DOI: 10.1371/journal.pone.0196793] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 04/19/2018] [Indexed: 11/19/2022] Open
Abstract
Genetic biomarkers are sought to personalize treatment of patients with rheumatoid arthritis (RA), given their variable response to TNF inhibitors (TNFi). However, no genetic biomaker is yet sufficiently validated. Here, we report a validation study of 18 previously reported genetic biomarkers, including 11 from GWAS of response to TNFi. The validation was attempted in 581 patients with RA that had not been treated with biologic antirheumatic drugs previously. Their response to TNFi was evaluated at 3, 6 and 12 months in two ways: change in the DAS28 measure of disease activity, and according to the EULAR criteria for response to antirheumatic drugs. Association of these parameters with the genotypes, obtained by PCR amplification followed by single-base extension, was tested with regression analysis. These analyses were adjusted for baseline DAS28, sex, and the specific TNFi. However, none of the proposed biomarkers was validated, as none showed association with response to TNFi in our study, even at the time of assessment and with the outcome that showed the most significant result in previous studies. These negative results are notable because this was the first independent validation study for 12 of the biomarkers, and because they indicate that prudence is needed in the interpretation of the proposed biomarkers of response to TNFi even when they are supported by very low p values. The results also emphasize the requirement of independent replication for validation, and the need to search protocols that could increase reproducibility of the biomarkers of response to TNFi.
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Affiliation(s)
- Rosario Lopez-Rodriguez
- Experimental and Observational Rheumatology and Rheumatology Unit, Instituto de Investigación Sanitaria, Hospital Clínico Universitario de Santiago, Santiago de Compostela, Spain
| | - Eva Perez-Pampin
- Experimental and Observational Rheumatology and Rheumatology Unit, Instituto de Investigación Sanitaria, Hospital Clínico Universitario de Santiago, Santiago de Compostela, Spain
| | - Ana Marquez
- Instituto de Parasitología y Biomedicina López-Neyra, CSIC, Granada, Spain
| | - Francisco J. Blanco
- Rheumatology Department, Instituto de Investigacion Biomedica–Complejo Hospitalario Universitario A Coruna, Coruna, Spain
| | | | | | - Miguel Angel Ferrer
- Rheumatology Unit, Hospital Universitario Virgen de las Nieves, Granada, Spain
| | - Rafael Caliz
- Rheumatology Unit, Hospital Universitario Virgen de las Nieves, Granada, Spain
| | - Lara Valor
- Rheumatology Unit, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Javier Narvaez
- Department of Rheumatology, Hospital Universitario de Bellvitge, Barcelona, Spain
| | - Juan D. Cañete
- Arthritis Unit, Rheumatology Dpt, Hospital Clinic and IDIBAPS, Barcelona, Spain
| | - Maria del Carmen Ordoñez
- Servicio de Reumatología, HRU Carlos Haya, Universidad de Málaga, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga Spain
| | - Sara Manrique-Arija
- Servicio de Reumatología, HRU Carlos Haya, Universidad de Málaga, Instituto de Investigación Biomédica de Málaga (IBIMA), Málaga Spain
| | - Yiannis Vasilopoulos
- Department of Biochemistry and Biotechnology, University of Thessaly, Larissa, Greece
| | - Alejandro Balsa
- Rheumatology Unit, Instituto de Investigación Sanitaria del Hospital Universitario La Paz (IdiPAZ), Hospital Universitario La Paz, Madrid, Spain
| | - Dora Pascual-Salcedo
- Department of Immunology, Instituto de Investigación Hospital Universitario La Paz (IdiPAZ), Madrid, Spain
| | | | | | | | - Virginia Moreira
- Rheumatology Unit, Hospital Universitario Virgen Macarena, Sevilla, Spain
| | | | - Enrique Raya
- Department of Rheumatology, Hospital Clínico San Cecilio, Granada, Spain
| | - Cesar Magro-Checa
- Department of Rheumatology, Hospital Clínico San Cecilio, Granada, Spain
- Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
| | - Javier Martin
- Instituto de Parasitología y Biomedicina López-Neyra, CSIC, Granada, Spain
| | - Juan J. Gomez-Reino
- Experimental and Observational Rheumatology and Rheumatology Unit, Instituto de Investigación Sanitaria, Hospital Clínico Universitario de Santiago, Santiago de Compostela, Spain
| | - Antonio Gonzalez
- Experimental and Observational Rheumatology and Rheumatology Unit, Instituto de Investigación Sanitaria, Hospital Clínico Universitario de Santiago, Santiago de Compostela, Spain
- * E-mail:
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22
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Pharmacogenomics of etanercept, infliximab, adalimumab and methotrexate in rheumatoid arthritis. A structured review. ACTA ACUST UNITED AC 2018. [DOI: 10.1016/j.rcreue.2018.08.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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23
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Arshad M, Bhatti A, John P, Jalil F, Williams RO. Association of rs182429 variant in TAGAP with rheumatoid arthritis in Pakistani population. Meta Gene 2017. [DOI: 10.1016/j.mgene.2017.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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24
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Nair N, Wilson AG, Barton A. DNA methylation as a marker of response in rheumatoid arthritis. Pharmacogenomics 2017; 18:1323-1332. [PMID: 28836487 DOI: 10.2217/pgs-2016-0195] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Rheumatoid arthritis (RA) is a complex disease affecting approximately 0.5-1% of the population. While there are effective biologic therapies, in up to 40% of patients, disease activity remains inadequately controlled. Therefore, identifying factors that predict, prior to the initiation of therapy, which patients are likely to respond best to which treatment is a research priority and DNA methylation is increasingly being explored as a potential theranostic biomarker. DNA methylation is thought to play a role in RA disease pathogenesis and in mediating the relationship between genetic variants and patient outcomes. The role of DNA methylation has been most extensively explored in cancer medicine, where it has been shown to be predictive of treatment response. Studies in RA, however, are in their infancy and, while showing promise, further investigation in well-powered studies is warranted.
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Affiliation(s)
- Nisha Nair
- Arthritis Research UK Centre for Genetics & Genomics, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK
| | - Anthony G Wilson
- University College Dublin School of Medicine & Medical Science & Conway Institute, Dublin, Ireland
| | - Anne Barton
- Arthritis Research UK Centre for Genetics & Genomics, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK.,NIHR Manchester Musculoskeletal BRU, Central Manchester Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, UK
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25
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Bek S, Bojesen AB, Nielsen JV, Sode J, Bank S, Vogel U, Andersen V. Systematic review and meta-analysis: pharmacogenetics of anti-TNF treatment response in rheumatoid arthritis. THE PHARMACOGENOMICS JOURNAL 2017; 17:403-411. [PMID: 28607508 PMCID: PMC5637244 DOI: 10.1038/tpj.2017.26] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 02/08/2017] [Accepted: 03/02/2017] [Indexed: 02/06/2023]
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory disease that affects ~1% of the Caucasian population. Over the last decades, the availability of biological drugs targeting the proinflammatory cytokine tumour necrosis factor α, anti-TNF drugs, has improved the treatment of patients with RA. However, one-third of the patients do not respond to the treatment. We wanted to evaluate the status of pharmacogenomics of anti-TNF treatment. We performed a PubMed literature search and all studies reporting original data on associations between genetic variants and anti-TNF treatment response in RA patients were included and results evaluated by meta-analysis. In total, 25 single nucleotide polymorphisms were found to be associated with anti-TNF treatment response in RA (19 from genome-wide association studies and 6 from the meta-analyses), and these map to genes involved in T cell function, NFκB and TNF signalling pathways (including CTCN5, TEC, PTPRC, FCGR2A, NFKBIB, FCGR2A, IRAK3). Explorative prediction analyses found that biomarkers for clinical treatment selection are not yet available.
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Affiliation(s)
- S Bek
- Focused Research Unit for Molecular Diagnostic and Clinical Research, IRS-Center Sonderjylland, Laboratory Center, Hospital of Southern Jutland, Aabenraa, Denmark
| | - A B Bojesen
- Focused Research Unit for Molecular Diagnostic and Clinical Research, IRS-Center Sonderjylland, Laboratory Center, Hospital of Southern Jutland, Aabenraa, Denmark.,Research Unit for E-mental Health, Mental Health Services in the Region of Southern Odense, Odense, Denmark
| | - J V Nielsen
- Focused Research Unit for Molecular Diagnostic and Clinical Research, IRS-Center Sonderjylland, Laboratory Center, Hospital of Southern Jutland, Aabenraa, Denmark
| | - J Sode
- Focused Research Unit for Molecular Diagnostic and Clinical Research, IRS-Center Sonderjylland, Laboratory Center, Hospital of Southern Jutland, Aabenraa, Denmark
| | - S Bank
- Focused Research Unit for Molecular Diagnostic and Clinical Research, IRS-Center Sonderjylland, Laboratory Center, Hospital of Southern Jutland, Aabenraa, Denmark
| | - U Vogel
- Research Unit for E-mental Health, Mental Health Services in the Region of Southern Odense, Odense, Denmark.,National Research Centre for the Working Environment, Copenhagen, Denmark
| | - V Andersen
- Focused Research Unit for Molecular Diagnostic and Clinical Research, IRS-Center Sonderjylland, Laboratory Center, Hospital of Southern Jutland, Aabenraa, Denmark.,Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark.,Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark.,Odense Patient Data Explorative Network, Odense University Hospital, Odense, Denmark
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26
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How to manage rheumatoid arthritis according to classic biomarkers and polymorphisms? ACTA ACUST UNITED AC 2017. [DOI: 10.1007/s11515-017-1452-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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27
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A genetic risk score composed of rheumatoid arthritis risk alleles, HLA-DRB1 haplotypes, and response to TNFi therapy - results from a Swedish cohort study. Arthritis Res Ther 2016; 18:288. [PMID: 27912794 PMCID: PMC5135751 DOI: 10.1186/s13075-016-1174-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Accepted: 11/08/2016] [Indexed: 11/10/2022] Open
Abstract
Background To prevent debilitating and irreversible joint damage, rheumatoid arthritis (RA) is often treated with tumor necrosis factor inhibitor (TNFi), but many patients do not respond to this costly therapy. Few predictors for response are known, and it has been proposed that genetic factors which influence the development of RA may also influence disease severity and response to therapy. Several previous studies have attempted to confirm this but results remain inconclusive. We expand on previous studies by including more RA risk alleles, and maximize power by combining them into a genetic risk score. Method We linked genotyped RA patients from the Epidemiological Investigation of Rheumatoid Arthritis study to the Swedish Rheumatology Quality Register, identifying patients who started a TNFi as their first biological disease-modifying anti-rheumatic drug, with a return visit within 2–8 months after treatment start (N = 867). We calculated risk scores from 76 established RA risk SNPs, and four HLA-DRB1 amino acid positions, and tested whether risk scores or individual genetic risk factors could predict the European League Against Rheumatism (EULAR) response. Results We found no association between any of the risk scores or HLA-DRB1 haplotypes and EULAR response, neither overall nor stratified by anti-citrullinated protein/peptide antibody (ACPA) status. When evaluating each of the 76 SNPs, we found that the number of SNPs presenting significant associations was not higher than expected by chance (5/76 SNPs had p < 0.05 in ACPA-positive RA, 4/76 in ACPA-negative RA). Conclusion Overall, known RA risk SNPs do not predict response to TNFi, either individually or when combined into a risk score. This does not support the hypothesis that genes influencing RA onset would also influence its prognosis and treatment response. Electronic supplementary material The online version of this article (doi:10.1186/s13075-016-1174-z) contains supplementary material, which is available to authorized users.
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