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Rheumatoid arthritis
Association of response to TNF inhibitors in rheumatoid arthritis with quantitative trait loci for CD40 and CD39
  1. Athina Spiliopoulou1,2,
  2. Marco Colombo1,
  3. Darren Plant3,4,
  4. Nisha Nair3,
  5. Jing Cui5,
  6. Marieke JH Coenen6,
  7. Katsunori Ikari7,8,
  8. Hisashi Yamanaka9,
  9. Saedis Saevarsdottir10,11,
  10. Leonid Padyukov10,
  11. S Louis Bridges Jr.12,
  12. Robert P Kimberly12,
  13. Yukinori Okada13,14,
  14. Piet L CM van Riel6,
  15. Gertjan Wolbink15,
  16. Irene E van der Horst-Bruinsma16,
  17. Niek de Vries17,
  18. Paul P Tak17,
  19. Koichiro Ohmura18,
  20. Helena Canhão19,
  21. Henk-Jan Guchelaar20,
  22. Tom WJ Huizinga21,
  23. Lindsey A Criswell22,
  24. Soumya Raychaudhuri5,23,
  25. Michael E Weinblatt5,
  26. Anthony G Wilson24,
  27. Xavier Mariette25,
  28. John D Isaacs26,27,
  29. Ann W Morgan28,29,
  30. Costantino Pitzalis30,
  31. Anne Barton3,4,
  32. Paul McKeigue1
  1. 1 Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
  2. 2 MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
  3. 3 Arthritis Research UK Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
  4. 4 NIHR Manchester Biomedical Research Centre, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
  5. 5 Division of Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
  6. 6 Department of Human Genetics, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
  7. 7 Department of Orthopedic Surgery, Tokyo Women's Medical University, Tokyo, Japan
  8. 8 The Centers of Research Excellence in Science and Technology, Japan Science and Technology Agency, Tokyo, Japan
  9. 9 Department of Rheumatology, School of Medicine, Tokyo Women's Medical University, Tokyo, Japan
  10. 10 Division of Rheumatology, Department of Medicine Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
  11. 11 Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
  12. 12 Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama, USA
  13. 13 Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
  14. 14 Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
  15. 15 Amsterdam Rheumatology and Immunology Centre, Reade, Amsterdam, The Netherlands
  16. 16 Department of Rheumatology, VU University Medical Centre, Amsterdam University Medical Centres, Amsterdam, The Netherlands
  17. 17 Department of Clinical Immunology and Rheumatology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
  18. 18 Department of Rheumatology and Clinical Immunology, Kyoto University Graduate School of Medicine, Kyoto, Japan
  19. 19 CEDOC, EpiDoC Unit, NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal
  20. 20 Department of Clinical Pharmacy and Toxicology, Leiden University Medical Center, Leiden, The Netherlands
  21. 21 Department of Rheumatology, Leiden University Medical Center, Leiden, The Netherlands
  22. 22 Rosalind Russell / Ephraim P Engleman Rheumatology Research Center, University of California San Francisco, San Francisco, California, USA
  23. 23 Center for Data Sciences, Harvard Medical School, Boston, Massachusetts, USA
  24. 24 EULAR Centre of Excellence/UCD Centre for Arthritis Research, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
  25. 25 y Université Paris-Sud, INSERM UMR1184, Hôpitaux Universitaire Paris-Sud, AP-HP, Le Kremlin Bicêtre, Paris, France
  26. 26 Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, UK
  27. 27 Musculoskeletal Unit, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
  28. 28 School of Medicine, University of Leeds, Leeds, UK
  29. 29 NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
  30. 30 Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Queen Mary University of London, London, UK
  1. Correspondence to Dr Athina Spiliopoulou, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, UK; A.Spiliopoulou{at}ed.ac.uk; Prof Paul McKeigue, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh EH8 9AG, UK; Paul.McKeigue{at}ed.ac.uk

Abstract

Objectives We sought to investigate whether genetic effects on response to TNF inhibitors (TNFi) in rheumatoid arthritis (RA) could be localised by considering known genetic susceptibility loci for relevant traits and to evaluate the usefulness of these genetic loci for stratifying drug response.

Methods We studied the relation of TNFi response, quantified by change in swollen joint counts ( Δ SJC) and erythrocyte sedimentation rate ( Δ ESR) with locus-specific scores constructed from genome-wide assocation study summary statistics in 2938 genotyped individuals: 37 scores for RA; scores for 19 immune cell traits; scores for expression or methylation of 93 genes with previously reported associations between transcript level and drug response. Multivariate associations were evaluated in penalised regression models by cross-validation.

Results We detected a statistically significant association between Δ SJC and the RA score at the CD40 locus (p=0.0004) and an inverse association between Δ SJC and the score for expression of CD39 on CD4 T cells (p=0.00005). A previously reported association between CD39 expression on regulatory T cells and response to methotrexate was in the opposite direction. In stratified analysis by concomitant methotrexate treatment, the inverse association was stronger in the combination therapy group and dissipated in the TNFi monotherapy group. Overall, ability to predict TNFi response from genotypic scores was limited, with models explaining less than 1% of phenotypic variance.

Conclusions The association with the CD39 trait is difficult to interpret because patients with RA are often prescribed TNFi after failing to respond to methotrexate. The CD39 and CD40 pathways could be relevant for targeting drug therapy.

  • rheumatoid arthritis
  • pharmacogenetics
  • anti-tnf

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

https://doi.org/10.1136/annrheumdis-2018-214877

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    Footnotes

    • Handling editor Josef S Smolen

    • Correction notice This article has been corrected since it first published. Table 1 data aligmment has been corrected.

    • Collaborators MATURA Consortium.

    • Contributors Substantial contributions to conception or design of the study: AS, PM, AB, CP. Substantial contributions to drafting the manuscript: AS, PM. Substantial contributions to data acquisition: JC, MJHC, KI, HY, SS, LP, SLB, RPK, YO, PLCMvR, GW, IEvdH-B, NdV, PPT, KO, HC, H-JG, TWJH, LAC, SR, MW, AGW, XM, JDI, AWM, AB. Substantial contributions to data analysis or interpretation: AS, PM, MC, DP, NN, CP. All authors contributed to revising the manuscript critically for important intellectual content and approved the final manuscript. The funding agencies had no part in writing or reviewing the manuscript.

    • Funding This study was supported by Medical Research Council (MRC) MR/K015346/1 MATURA study. ARUK 20670 MATURA study.

    • Competing interests None declared.

    • Patient and public involvement statement This study was conducted as part of Work Stream 2 of the MATURA Consortium (http://www.matura.whri.qmul.ac.uk/what_is_matura.php). A patient advisory group was established in 2014 when the MATURA project commenced. The group meets regularly to:

      Ensure MATURA strategy is maintaining relevance, accountability and direction by embedding patients and members of the public within the decision making processes.

      Determine what level of confidence in tests, and what type of tests, would be acceptable to patients for treatment decisions.

      Maximise patient recruitment to research studies by increasing awareness through patient groups.

      Readily obtain patients’ perspective on grant applications related to stratified medicines for RA.

      Facilitate the dissemination of the results from MATURA research, for instance by producing lay summaries of papers in conjunction with the researchers (http://www.matura.whri.qmul.ac.uk/news.php).

    • Patient consent for publication Not required.

    • Ethics approval This study used anonymised data for human subjects from an international collaboration of 13 studies originally published in PLOS Genetics 2013;9:e1003394. All participants provided informed consent and institutional review board and ethics approvals were in place for each of the studies and described in the original publication.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement Data are available upon reasonable request.