Skip to main content

Advertisement

Log in

Biomarker zur Prognose des Ansprechens auf eine Anti-TNF-Therapie bei der rheumatoiden Arthritis

Wo stehen wir?

Biomarkers for prognosis of response to anti-TNF therapy of rheumatoid arthritis

Where do we stand?

  • Neues aus der Forschung
  • Published:
Zeitschrift für Rheumatologie Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Literatur

  1. Dennis G, Holweg CT Jr, Kummerfeld SK et al (2014) Synovial phenotypes in rheumatoid arthritis correlate with response to biologic therapeutics. Arthritis Res Ther 16:R90

    Article  PubMed  PubMed Central  Google Scholar 

  2. Curtis JR, Yang S, Chen L et al (2012) Predicting low disease activity and remission using early treatment response to antitumour necrosis factor therapy in patients with rheumatoid arthritis: exploratory analyses from the TEMPO trial. Ann Rheum Dis 71:206–212

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Alessandri C, Bombardieri M, Papa N et al (2004) Decrease of anti-cyclic citrullinated peptide antibodies and rheumatoid factor following anti-TNFalpha therapy (infliximab) in rheumatoid arthritis is associated with clinical improvement. Ann Rheum Dis 63:1218–1221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Bobbio-Pallavicini F, Caporali R, Alpini C et al (2007) High IgA rheumatoid factor levels are associated with poor clinical response to tumour necrosis factor alpha inhibitors in rheumatoid arthritis. Ann Rheum Dis 66:302–307

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Braun-Moscovici Y, Markovits D, Zinder O et al (2006) Anti-cyclic citrullinated protein antibodies as a predictor of response to anti-tumor necrosis factor-alpha therapy in patients with rheumatoid arthritis. J Rheumatol 33:497–500

    CAS  PubMed  Google Scholar 

  6. Hyrich KL, Watson KD, Silman AJ et al (2006) Predictors of response to anti-TNF-alpha therapy among patients with rheumatoid arthritis: results from the British Society for Rheumatology Biologics Register. Rheumatology (Oxford) 45:1558–1565

  7. Wells G, Becker JC, Teng J et al (2009) Validation of the 28-joint Disease Activity Score (DAS28) and European League Against Rheumatism response criteria based on C-reactive protein against disease progression in patients with rheumatoid arthritis, and comparison with the DAS28 based on erythrocyte sedimentation rate. Ann Rheum Dis 68:954–960

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bowes JD, Potter C, Gibbons LJ et al (2009) Investigation of genetic variants within candidate genes of the TNFRSF1B signalling pathway on the response to anti-TNF agents in a UK cohort of rheumatoid arthritis patients. Pharmacogenet Genomics 19:319–323

    Article  CAS  PubMed  Google Scholar 

  9. Ceccarelli F, D’Alfonso S, Perricone C et al (2012) The role of eight polymorphisms in three candidate genes in determining the susceptibility, phenotype, and response to anti-TNF therapy in patients with rheumatoid arthritis. Clin Exp Rheumatol 30:939–942

    PubMed  Google Scholar 

  10. Chatzikyriakidou A, Georgiou I, Voulgari PV et al (2007) Combined tumour necrosis factor-alpha and tumour necrosis factor receptor genotypes could predict rheumatoid arthritis patients‘ response to anti-TNF-alpha therapy and explain controversies of studies based on a single polymorphism. Rheumatology (Oxford) 46:1034–1035

  11. Criswell LA, Lum RF, Turner KN et al (2004) The influence of genetic variation in the HLA-DRB1 and LTA-TNF regions on the response to treatment of early rheumatoid arthritis with methotrexate or etanercept. Arthritis Rheum 50:2750–2756

    Article  CAS  PubMed  Google Scholar 

  12. Cui J, Stahl EA, Saevarsdottir S et al (2013) Genome-wide association study and gene expression analysis identifies CD84 as a predictor of response to etanercept therapy in rheumatoid arthritis. PLoS Genet 9:e1003394

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Iannaccone CK, Lee YC, Cui J et al (2011) Using genetic and clinical data to understand response to disease-modifying anti-rheumatic drug therapy: data from the Brigham and Women’s Hospital Rheumatoid Arthritis Sequential Study. Rheumatology (Oxford) 50:40–46

  14. Lee YH, Bae SC, Song GG (2014) Functional FCGR3A 158 V/F and IL-6 -174 C/G polymorphisms predict response to biologic therapy in patients with rheumatoid arthritis: a meta-analysis. Rheumatol Int 34:1409–1415

    Article  CAS  PubMed  Google Scholar 

  15. Martinez A, Salido M, Bonilla G et al (2004) Association of the major histocompatibility complex with response to infliximab therapy in rheumatoid arthritis patients. Arthritis Rheum 50:1077–1082

    Article  CAS  PubMed  Google Scholar 

  16. Miceli-Richard C, Comets E, Verstuyft C et al (2008) A single tumour necrosis factor haplotype influences the response to adalimumab in rheumatoid arthritis. Ann Rheum Dis 67:478–484

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Plant D, Bowes J, Potter C et al (2011) Genome-wide association study of genetic predictors of anti-tumor necrosis factor treatment efficacy in rheumatoid arthritis identifies associations with polymorphisms at seven loci. Arthritis Rheum 63:645–653

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Potter C, Cordell HJ, Barton A et al (2010) Association between anti-tumour necrosis factor treatment response and genetic variants within the TLR and NF{kappa}B signalling pathways. Ann Rheum Dis 69:1315–1320

    Article  CAS  PubMed  Google Scholar 

  19. Prieto-Perez R, Cabaleiro T, Dauden E et al (2013) Gene polymorphisms that can predict response to anti-TNF therapy in patients with psoriasis and related autoimmune diseases. Pharmacogenomics J 13:297–305

    Article  CAS  PubMed  Google Scholar 

  20. Sode J, Vogel U, Bank S et al (2014) Anti-TNF treatment response in rheumatoid arthritis patients is associated with genetic variation in the NLRP3-inflammasome. PLoS One 9:e100361

    Article  PubMed  PubMed Central  Google Scholar 

  21. Swierkot J, Bogunia-Kubik K, Nowak B et al (2014) Analysis of associations between polymorphisms within genes coding for tumour necrosis factor (TNF)-alpha and TNF receptors and responsiveness to TNF-alpha blockers in patients with rheumatoid arthritis. Joint Bone Spine. doi: 10.1016/j.jbspin.2014.08.006. Epub 2014 Oct 11

  22. Umicevic Mirkov M, Cui J, Vermeulen SH et al (2013) Genome-wide association analysis of anti-TNF drug response in patients with rheumatoid arthritis. Ann Rheum Dis 72:1375–1381

    Article  Google Scholar 

  23. Zervou MI, Myrthianou E, Flouri I et al (2013) Lack of association of variants previously associated with anti-TNF medication response in rheumatoid arthritis patients: results from a homogeneous Greek population. PLoS One 8:e74375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Montes A, Perez-Pampin E, Narvaez J et al (2014) Association of FCGR2A with the response to infliximab treatment of patients with rheumatoid arthritis. Pharmacogenet Genomics 24:238–245

    CAS  PubMed  Google Scholar 

  25. Julia A, Erra A, Palacio C et al (2009) An eight-gene blood expression profile predicts the response to infliximab in rheumatoid arthritis. PLoS One 4:e7556

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kim TH, Choi SJ, Lee YH et al (2014) Gene expression profile predicting the response to anti-TNF treatment in patients with rheumatoid arthritis; analysis of GEO datasets. Joint Bone Spine 81:325–330

    Article  CAS  PubMed  Google Scholar 

  27. Koczan D, Drynda S, Hecker M et al (2008) Molecular discrimination of responders and nonresponders to anti-TNF alpha therapy in rheumatoid arthritis by etanercept. Arthritis Res Ther 10:R50

    Article  PubMed  PubMed Central  Google Scholar 

  28. Lequerre T, Gauthier-Jauneau AC, Bansard C et al (2006) Gene profiling in white blood cells predicts infliximab responsiveness in rheumatoid arthritis. Arthritis Res Ther 8:R105

    Article  PubMed  PubMed Central  Google Scholar 

  29. MacIsaac KD, Baumgartner R, Kang J et al (2014) Pre-treatment whole blood gene expression is associated with 14-week response assessed by dynamic contrast enhanced magnetic resonance imaging in infliximab-treated rheumatoid arthritis patients. PLoS One 9:e113937

    Article  PubMed  PubMed Central  Google Scholar 

  30. Sekiguchi N, Kawauchi S, Furuya T et al (2008) Messenger ribonucleic acid expression profile in peripheral blood cells from RA patients following treatment with an anti-TNF-alpha monoclonal antibody, infliximab. Rheumatology (Oxford) 47:780–788

  31. Stuhlmüller B, Häupl T, Hernandez MM et al (2010) CD11c as a transcriptional biomarker to predict response to anti-TNF monotherapy with adalimumab in patients with rheumatoid arthritis. Clin Pharmacol Ther 87:311–321

    Article  PubMed  Google Scholar 

  32. Tanino M, Matoba R, Nakamura S et al (2009) Prediction of efficacy of anti-TNF biologic agent, infliximab, for rheumatoid arthritis patients using a comprehensive transcriptome analysis of white blood cells. Biochem Biophys Res Commun 387:261–265

    Article  CAS  PubMed  Google Scholar 

  33. Stuhlmüller B, Häupl T, Burmester G et al (PLoS One) Letter to the editor: prerequisites to define and to validate therapy-predicitive blood biomarkers. http://www.plosone.org/annotation/listThread.action?root=55071

  34. Stuhlmüller B (2013) Biomarker signatures to define the right therapy the first time. http://www.affymetrix.com/estore/community/researchers/bruno-stuhlmuller/index.affx

  35. Smolen JS, Aletaha D (2013) Forget personalised medicine and focus on abating disease activity. Ann Rheum Dis 72:3–6

    Article  CAS  PubMed  Google Scholar 

  36. Krenn V, Petersen I, Häupl T et al (2004) Array technology and proteomics in autoimmune diseases. Pathol Res Pract 200:95–103

    Article  CAS  PubMed  Google Scholar 

  37. Centola M, Cavet G, Shen Y et al (2013) Development of a multi-biomarker disease activity test for rheumatoid arthritis. PLoS One 8:e60635

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Choi IY, Gerlag DM, Herenius MJ et al (2015) MRP8/14 serum levels as a strong predictor of response to biological treatments in patients with rheumatoid arthritis. Ann Rheum Dis 74:499–505

    Article  PubMed  Google Scholar 

  39. Hirata S, Li W, Defranoux N et al (2014) A multi-biomarker disease activity score tracks clinical response consistently in patients with rheumatoid arthritis treated with different anti-tumor necrosis factor therapies: a retrospective observational study. Mod Rheumatol. doi: 10.3109/14397595.2014.958893. Epub 2014 Oct 8

  40. Hueber W, Tomooka BH, Batliwalla F et al (2009) Blood autoantibody and cytokine profiles predict response to anti-tumor necrosis factor therapy in rheumatoid arthritis. Arthritis Res Ther 11:R76

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ortea I, Roschitzki B, Ovalles JG et al (2012) Discovery of serum proteomic biomarkers for prediction of response to infliximab (a monoclonal anti-TNF antibody) treatment in rheumatoid arthritis: an exploratory analysis. J Proteomics 77:372–382

    Article  CAS  PubMed  Google Scholar 

  42. Senolt L, Cerezo LA, Sumova B et al (2015) High levels of metastasis-inducing S100A4 protein and treatment outcome in early rheumatoid arthritis: data from the PERAC cohort. Biomarkers 20:47–51

    Article  CAS  PubMed  Google Scholar 

  43. Serada S, Fujimoto M, Ogata A et al (2010) iTRAQ-based proteomic identification of leucine-rich alpha-2 glycoprotein as a novel inflammatory biomarker in autoimmune diseases. Ann Rheum Dis 69:770–774

    Article  CAS  PubMed  Google Scholar 

  44. Maruotti N, d’Onofrio F, Cantatore FP (in press) Metabolic syndrome and chronic arthritis: effects of anti-TNF-alpha therapy. Clin Exp Med. doi:10.1007/s10238-014-0323-4

  45. Cacciapaglia F, Anelli MG, Rinaldi A et al (2014) Lipid profile of rheumatoid arthritis patients treated with anti-tumor necrosis factor-alpha drugs changes according to disease activity and predicts clinical response. Drug Dev Res 75(Suppl 1):77–80

    Article  Google Scholar 

  46. Chimenti MS, Tucci P, Candi E et al (2013) Metabolic profiling of human CD4+ cells following treatment with methotrexate and anti-TNF-alpha infliximab. Cell Cycle 12:3025–3036

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kapoor SR, Filer A, Fitzpatrick MA et al (2013) Metabolic profiling predicts response to anti-tumor necrosis factor alpha therapy in patients with rheumatoid arthritis. Arthritis Rheum 65:1448–1456

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Cacciapaglia F, Buzzulini F, Arcarese L et al (2014) The use of an interferon-gamma release assay as a biomarker of response to anti-TNF-alpha treatment. Drug Dev Res 75(Suppl 1):50–53

    Article  Google Scholar 

  49. Ceeraz S, Hall C, Choy EH et al (2013) Defective CD8+ CD28+ regulatory T cell suppressor function in rheumatoid arthritis is restored by tumour necrosis factor inhibitor therapy. Clin Exp Immunol 174:18–26

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Chara L, Sanchez-Atrio A, Perez A et al (2012) Monocyte populations as markers of response to adalimumab plus MTX in rheumatoid arthritis. Arthritis Res Ther 14:R175

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Huang Z, Yang B, Shi Y et al (2012) Anti-TNF-alpha therapy improves Treg and suppresses Teff in patients with rheumatoid arthritis. Cell Immunol 279:25–29

    Article  CAS  PubMed  Google Scholar 

  52. Kayakabe K, Kuroiwa T, Sakurai N et al (2012) Interleukin-1beta measurement in stimulated whole blood cultures is useful to predict response to anti-TNF therapies in rheumatoid arthritis. Rheumatology (Oxford) 51:1639–1643

  53. Meusch U, Klingner M, Baerwald C et al (2013) Deficient spontaneous in vitro apoptosis and increased tmTNF reverse signaling-induced apoptosis of monocytes predict suboptimal therapeutic response of rheumatoid arthritis to TNF inhibition. Arthritis Res Ther 15:R219

    Article  PubMed  PubMed Central  Google Scholar 

  54. Park YJ, Kim JY, Park J et al (2014) Bone erosion is associated with reduction of circulating endothelial progenitor cells and endothelial dysfunction in rheumatoid arthritis. Arthritis Rheumatol 66:1450–1460

    Article  CAS  PubMed  Google Scholar 

  55. Spinelli FR, Metere A, Barbati C et al (2013) Effect of therapeutic inhibition of TNF on circulating endothelial progenitor cells in patients with rheumatoid arthritis. Mediators Inflamm 2013:537539

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Steinbrich-Zollner M, Grun JR, Kaiser T et al (2008) From transcriptome to cytome: integrating cytometric profiling, multivariate cluster, and prediction analyses for a phenotypical classification of inflammatory diseases. Cytometry A 73:333–340

    Article  PubMed  Google Scholar 

  57. Sorensen T, Baumgart S, Durek P et al (2015) ImmunoClust-An automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytometry A. doi:10.1002/cyto.a.22626

  58. Pappas DA, Kremer JM, Reed G et al (2014) Design characteristics of the CORRONA CERTAIN study: a comparative effectiveness study of biologic agents for rheumatoid arthritis patients. BMC Musculoskelet Disord 15:113

    Article  PubMed  PubMed Central  Google Scholar 

  59. Detert J, Bastian H, Listing J et al (2013) Induction therapy with adalimumab plus methotrexate for 24 weeks followed by methotrexate monotherapy up to week 48 versus methotrexate therapy alone for DMARD-naive patients with early rheumatoid arthritis: HIT HARD, an investigator-initiated study. Ann Rheum Dis 72:844–850

    Article  CAS  PubMed  Google Scholar 

  60. Häupl T, Appel H, Backhaus M et al (2012) Biomarkers in rheumatology. Biomarkers and imaging for the diagnosis and stratification of rheumatoid arthritis and spondylarthritis in the ArthroMark network funded by the Federal Ministry of Education and Research. Z Rheumatol 71:314–318

    Article  PubMed  Google Scholar 

Download references

Einhaltung ethischer Richtlinien

Interessenkonflikt. B. Stuhlmüller, K. Skriner und T. Häupl geben an, dass kein Interessenkonflikt besteht.

Dieser Beitrag beinhaltet keine Studien an Menschen oder Tieren.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. Stuhlmüller.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Stuhlmüller, B., Skriner, K. & Häupl, T. Biomarker zur Prognose des Ansprechens auf eine Anti-TNF-Therapie bei der rheumatoiden Arthritis. Z Rheumatol 74, 812–818 (2015). https://doi.org/10.1007/s00393-014-1543-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00393-014-1543-4

Navigation