Genetic and epigenetic predictors of responsiveness to treatment in RA

Journal name:
Nature Reviews Rheumatology
Volume:
10,
Pages:
329–337
Year published:
DOI:
doi:10.1038/nrrheum.2014.16
Published online

Abstract

Methotrexate and TNF-blocking agents are the DMARDs most commonly prescribed for the treatment of rheumatoid arthritis (RA). However, not all patients treated with these nonbiologic and biologic DMARDs respond satisfactorily and few predictors of treatment efficacy have been identified, despite the fact that these therapies have now been available for many years. Many studies have investigated genetic factors that might predict patient responsiveness to therapies used to treat RA, and epigenetic studies regarding response to treatment are expected to accumulate in the literature in the near future. Herein, we review the advances in identifying genetic and epigenetic predictors of therapeutic responses to methotrexate and/or TNF inhibitors in RA that have been made to date, and highlight important considerations for future studies, such as the need for an improved, preferably biological, outcome measure reflecting response to treatment.

At a glance

Figures

  1. Potential approach to stratified medicine in RA.
    Figure 1: Potential approach to stratified medicine in RA.

    Patients presenting to the clinic with inflammatory arthritis for the first time would be tested for predictive biomarkers of response to treatment and this data would be used to inform selection of the therapy that each patient is most likely to respond to. In reality, responsiveness to various treatments is probably multifactorial, and thus a predictive algorithm incorporating assessments of a panel of biomarkers, which could include genetic, epigenetic (DNA methylation profiles, for example) and transcriptomic factors (such as the levels of certain mRNAs, microRNAs or proteins), is likely to be necessary. Abbreviation: RA, rheumatoid arthritis.

  2. Pretreatment DNA methylation status as a predictive biomarker of response to treatment.
    Figure 2: Pretreatment DNA methylation status as a predictive biomarker of response to treatment.

    The schematic shows a simplified illustration of how a patient's baseline DNA methylation profile could act as a predictive biomarker of responsiveness to treatment. The locus shown in each panel corresponds to the same gene in two patients; for example, a gene encoding an inflammatory cytokine, such as IL-6. a | In a patient in whom DNA methylation in the promoter region of the gene is low, which is associated with active transcription, the encoded protein might be produced at high levels. In this scenario, a therapy targeting the pathway this protein is involved in—anti-IL-6 therapy, for instance—could lead, indirectly, to increased DNA methylation, restricting transcription of the gene and production of a proinflammatory mediator. This response would thus reduce or halt inflammation; therefore, this patient would be a 'responder' to the drug. b | In a patient with high levels of DNA methylation within the same promoter region, potentially indicating that inflammation in this patient is mediated by a different pathway (B-cell activation rather than IL-6 production, for example), the same treatment is unlikely to have an effect on expression of the gene, because the gene is already 'switched off'. In this case, the patient will probably be nonresponsive to therapies targeting the pathway this gene is involved in.

References

  1. Finckh, A., Liang, M. H., van Herckenrode, C. M. & de Pablo, P. Long-term impact of early treatment on radiographic progression in rheumatoid arthritis: a meta-analysis. Arthritis Rheum. 55, 864872 (2006).
  2. Scirè, C. A. et al. Reduction of long-term disability in inflammatory polyarthritis by early and persistent suppression of joint inflammation: results from the Norfolk Arthritis Register. Arthritis Care Res. (Hoboken) 63, 945952 (2011).
  3. Scirè, C. A., Lunt, M., Marshall, T., Symmons, D. P. & Verstappen, S. M. Early remission is associated with improved survival in patients with inflammatory polyarthritis: results from the Norfolk Arthritis Register. Ann. Rheum. Dis. http://dx.doi.org/10.1136/annrheumdis-2013-203339.
  4. Nice Pathways. Rheumatoid arthritis overview [online], (2013).
  5. Barrera, P. et al. Drug survival, efficacy and toxicity of monotherapy with a fully human anti-tumour necrosis factor-α antibody compared with methotrexate in long-standing rheumatoid arthritis. Rheumatology (Oxford) 41, 430439 (2002).
  6. Hyrich, K. L., Watson, K. D., Silman, A. J. & Symmons, D. P. Predictors of response to anti-TNF- α therapy among patients with rheumatoid arthritis: results from the British Society for Rheumatology Biologics Register. Rheumatology (Oxford) 45, 15581565 (2006).
  7. Farragher, T. M., Lunt, M., Fu, B., Bunn, D. & Symmons, D. P. Early treatment with, and time receiving, first disease-modifying antirheumatic drug predicts long-term function in patients with inflammatory polyarthritis. Ann. Rheum. Dis. 69, 689695 (2010).
  8. Potter, C. et al. Association of rheumatoid factor and anti-cyclic citrullinated peptide positivity, but not carriage of shared epitope or PTPN22 susceptibility variants, with anti-tumour necrosis factor response in rheumatoid arthritis. Ann. Rheum. Dis. 68, 6974 (2009).
  9. Hider, S. L. et al. Can clinical factors at presentation be used to predict outcome of treatment with methotrexate in patients with early inflammatory polyarthritis? Ann. Rheum. Dis. 68, 5762 (2009).
  10. Dalton, W. S. & Friend, S. H. Cancer biomarkers—an invitation to the table. Science 312, 11651168 (2006).
  11. Bodin, L. et al. Cytochrome P450 2C9 (CYP2C9) and vitamin K epoxide reductase (VKORC1) genotypes as determinants of acenocoumarol sensitivity. Blood 106, 135140 (2005).
  12. Cooper, G. M. et al. A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose. Blood 112, 10221027 (2008).
  13. Dervieux, T., Greenstein, N. & Kremer, J. Pharmacogenomic and metabolic biomarkers in the folate pathway and their association with methotrexate effects during dosage escalation in rheumatoid arthritis. Arthritis Rheum. 54, 30953103 (2006).
  14. Urano, W. et al. Polymorphisms in the methylenetetrahydrofolate reductase gene were associated with both the efficacy and the toxicity of methotrexate used for the treatment of rheumatoid arthritis, as evidenced by single locus and haplotype analyses. Pharmacogenetics 12, 183190 (2002).
  15. Owen, S. A. et al. MTHFR gene polymorphisms and outcome of methotrexate treatment in patients with rheumatoid arthritis: analysis of key polymorphisms and meta-analysis of C677T and A1298C polymorphisms. Pharmacogenomics J. 13, 137147 (2013).
  16. Raychaudhuri, S. et al. Genetic variants at CD28, PRDM1 and CD2/CD58 are associated with rheumatoid arthritis risk. Nat. Genet. 41, 13131318 (2009).
  17. Cui, J. et al. Rheumatoid arthritis risk allele PTPRC is also associated with response to anti-tumor necrosis factor α therapy. Arthritis Rheum. 62, 18491861 (2010).
  18. Plant, D. et al. Replication of association of the PTPRC gene with response to anti-tumor necrosis factor therapy in a large UK cohort. Arthritis Rheum. 64, 665670 (2012).
  19. Pappas, D. A., Oh, C., Plenge, R. M., Kremer, J. M. & Greenberg, J. D. Association of rheumatoid arthritis risk alleles with response to anti-TNF biologics: results from the CORRONA registry and meta-analysis. Inflammation 36, 279284 (2013).
  20. Cui, J. et al. 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 (2013).
  21. Acosta-Colman, I. et al. GWAS replication study confirms the association of PDE3ASLCO1C1 with anti-TNF therapy response in rheumatoid arthritis. Pharmacogenomics 14, 727734 (2013).
  22. Smith, S. L., Plant, D., Eyre, S. & Barton, A. The potential use of expression profiling: implications for predicting treatment response in rheumatoid arthritis. Ann. Rheum. Dis. 72, 11181124 (2013).
  23. Breitling, L. P., Yang, R., Korn, B., Burwinkel, B. & Brenner, H. Tobacco-smoking-related differential DNA methylation: 27K discovery and replication. Am. J. Hum. Genet. 88, 450457 (2011).
  24. Fraga, M. F. et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc. Natl Acad. Sci. USA 102, 1060410609 (2005).
  25. Ziller, M. J. et al. Charting a dynamic DNA methylation landscape of the human genome. Nature 500, 477481 (2013).
  26. Kouzarides, T. Chromatin modifications and their function. Cell 128, 693705 (2007).
  27. Nile, C. J., Read, R. C., Akil, M., Duff, G. W. & Wilson, A. G. Methylation status of a single CpG site in the IL6 promoter is related to IL6 messenger RNA levels and rheumatoid arthritis. Arthritis Rheum. 58, 26862693 (2008).
  28. MacGregor, A. J. et al. Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from twins. Arthritis Rheum. 43, 3037 (2000).
  29. Breitling, L. P., Salzmann, K., Rothenbacher, D., Burwinkel, B. & Brenner, H. Smoking, F2RL3 methylation, and prognosis in stable coronary heart disease. Eur. Heart J. 33, 28412848 (2012).
  30. Christensen, B. C. et al. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG island context. PLoS Genet. 5, e1000602 (2009).
  31. Langevin, S. M. et al. The influence of aging, environmental exposures and local sequence features on the variation of DNA methylation in blood. Epigenetics. 6, 908919 (2011).
  32. Tapp, H. S. et al. Nutritional factors and gender influence age-related DNA methylation in the human rectal mucosa. Aging Cell 12, 148155 (2013).
  33. Karouzakis, E., Gay, R. E., Michel, B. A., Gay, S. & Neidhart, M. DNA hypomethylation in rheumatoid arthritis synovial fibroblasts. Arthritis Rheum. 60, 36133622 (2009).
  34. Richardson, B. et al. Evidence for impaired T cell DNA methylation in systemic lupus erythematosus and rheumatoid arthritis. Arthritis Rheum. 33, 16651673 (1990).
  35. de la Rica, L. et al. Identification of novel markers in rheumatoid arthritis through integrated analysis of DNA methylation and microRNA expression. J. Autoimmun. 41, 616 (2013).
  36. Nakano, K., Whitaker, J. W., Boyle, D. L., Wang, W. & Firestein, G. S. DNA methylome signature in rheumatoid arthritis. Ann. Rheum. Dis. 72, 110117 (2013).
  37. Ishida, K. et al. Interleukin-6 gene promoter methylation in rheumatoid arthritis and chronic periodontitis. J. Periodontol. 83, 917925 (2012).
  38. Liu, Y. et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat. Biotechnol. 31, 142147 (2013).
  39. Illumina. Products / Infinium HumanMethylation450 BeadChip Kit [online], (2013).
  40. Bottini, N. & Firestein, G. S. Duality of fibroblast-like synoviocytes in RA: passive responders and imprinted aggressors. Nat. Rev. Rheumatol. 9, 2433 (2013).
  41. Grabiec, A. M. & Reedquist, K. A. The ascent of acetylation in the epigenetics of rheumatoid arthritis. Nat. Rev. Rheumatol. 9, 311318 (2013).
  42. Laird, P. W. Principles and challenges of genomewide DNA methylation analysis. Nat. Rev. Genet. 11, 191203 (2010).
  43. Esteller, M. et al. Inactivation of the DNA-repair gene MGMT and the clinical response of gliomas to alkylating agents. N. Engl. J. Med. 343, 13501354 (2000).
  44. Esteller, M. et al. Hypermethylation of the DNA repair gene O-6-methylguanine DNA methyltransferase and survival of patients with diffuse large B-cell lymphoma. J. Natl Cancer Inst. 94, 2632 (2002).
  45. Kawakami, K. et al. Long interspersed nuclear element-1 hypomethylation is a potential biomarker for the prediction of response to oral fluoropyrimidines in microsatellite stable and CpG island methylator phenotype-negative colorectal cancer. Cancer Sci. 102, 166174 (2011).
  46. Fodinger, M., Horl, W. H. & Sunder-Plassmann, G. Molecular biology of 5,10-methylenetetrahydrofolate reductase. J. Nephrol. 13, 2033 (2000).
  47. Chiang, P. K. et al. S-adenosylmethionine and methylation. FASEB J. 10, 471480 (1996).
  48. Kim, Y. I., Logan, J. W., Mason, J. B. & Roubenoff, R. DNA hypomethylation in inflammatory arthritis: reversal with methotrexate. J. Lab. Clin. Med. 128, 165172 (1996).
  49. Ellis, J. A. et al. Genome-scale case–control analysis of CD4+ T-cell DNA methylation in juvenile idiopathic arthritis reveals potential targets involved in disease. Clin. Epigenetics 4, 20 (2012).
  50. Danese, E. et al. Epigenetic alteration: new insights moving from tissue to plasma—the example of PCDH10 promoter methylation in colorectal cancer. Br. J. Cancer 109, 807813 (2013).
  51. Lin, Y. L., Li, Z. G., He, Z. K., Guan, T. Y. & Ma, J. G. Clinical and prognostic significance of protocadherin-10 (PCDH10) promoter methylation in bladder cancer. J. Int. Med. Res. 40, 21172123 (2012).
  52. Ma, J. G., He, Z. K., Ma, J. H., Li, W. P. & Sun, G. Downregulation of protocadherin-10 expression correlates with malignant behaviour and poor prognosis in human bladder cancer. J. Int. Med. Res. 41, 3847 (2013).
  53. Tang, X. et al. Protocadherin 10 is frequently downregulated by promoter methylation and functions as a tumor suppressor gene in non-small cell lung cancer. Cancer Biomark. 12, 1119 (2012).
  54. Narayan, G. et al. Promoter methylation-mediated inactivation of PCDH10 in acute lymphoblastic leukemia contributes to chemotherapy resistance. Genes Chromosomes Cancer 50, 10431053 (2011).
  55. Yang, R. et al. Quantitative correlation between promoter methylation and messenger RNA levels of the reduced folate carrier. BMC Cancer 8, 124 (2008).
  56. Worm, J., Kirkin, A. F., Dzhandzhugazyan, K. N. & Guldberg, P. Methylation-dependent silencing of the reduced folate carrier gene in inherently methotrexate-resistant human breast cancer cells. J. Biol. Chem. 276, 3999040000 (2001).
  57. Bohanec, G. P., Logar, D., Lestan, B. & Dolzan, V. Genetic determinants of methotrexate toxicity in rheumatoid arthritis patients: a study of polymorphisms affecting methotrexate transport and folate metabolism. Eur. J. Clin. Pharmacol. 64, 10571068 (2008).
  58. Chatzikyriakidou, A. et al. Transcription regulatory polymorphism −43T>C in the 5′-flanking region of SLC19A1 gene could affect rheumatoid arthritis patient response to methotrexate therapy. Rheumatol. Int. 27, 10571061 (2007).
  59. Dervieux, T. et al. Patterns of interaction between genetic and nongenetic attributes and methotrexate efficacy in rheumatoid arthritis. Pharmacogenet. Genomics 22, 19 (2012).
  60. Drozdzik, M. et al. Reduced folate carrier-1 80G>A polymorphism affects methotrexate treatment outcome in rheumatoid arthritis. Pharmacogenomics J. 7, 404407 (2007).
  61. Stamp, L. K. et al. Polymorphisms within the folate pathway predict folate concentrations but are not associated with disease activity in rheumatoid arthritis patients on methotrexate. Pharmacogenet. Genomics 20, 367376 (2010).
  62. Wessels, J. A. et al. Efficacy and toxicity of methotrexate in early rheumatoid arthritis are associated with single-nucleotide polymorphisms in genes coding for folate pathway enzymes. Arthritis Rheum. 54, 10871095 (2006).
  63. Maxwell, J. R. et al. Association of the tumour necrosis factor −308 variant with differential response to anti-TNF agents in the treatment of rheumatoid arthritis. Hum. Mol. Genet. 17, 35323538 (2008).
  64. O'Rielly, D. D., Roslin, N. M., Beyene, J., Pope, A. & Rahman, P. TNF-α −308 G/A polymorphism and responsiveness to TNF- α blockade therapy in moderate to severe rheumatoid arthritis: a systematic review and meta-analysis. Pharmacogenomics J. 9, 161167 (2009).
  65. Saccani, S. & Natoli, G. Dynamic changes in histone H3 Lys 9 methylation occurring at tightly regulated inducible inflammatory genes. Genes Dev. 16, 22192224 (2002).
  66. Vanden, B. W. et al. Keeping up NF-κB appearances: epigenetic control of immunity or inflammation-triggered epigenetics. Biochem. Pharmacol. 72, 11141131 (2006).
  67. Sullivan, K. E. et al. Epigenetic regulation of tumor necrosis factor α. Mol. Cell Biol. 27, 51475160 (2007).
  68. Bernstein, B. E. et al. Genomic maps and comparative analysis of histone modifications in human and mouse. Cell 120, 169181 (2005).
  69. Gowers, I. R. et al. Age-related loss of CpG methylation in the tumour necrosis factor promoter. Cytokine 56, 792797 (2011).
  70. Brinkman, B. M. et al. Tumour necrosis factor α gene polymorphisms in rheumatoid arthritis: association with susceptibility to, or severity of, disease? Br. J. Rheumatol. 36, 516521 (1997).
  71. Fabris, M. et al. Tumor necrosis factor- α gene polymorphism in severe and mild-moderate rheumatoid arthritis. J. Rheumatol. 29, 2933 (2002).
  72. Houseman, E. A. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012).
  73. Park, P. J. ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet. 10, 669680 (2009).
  74. Plant, D. et al. Estimating heritability of response to treatment with anti-TNF biologic agents using linear mixed models [abstract O46]. Rheumatology (Oxford) 52 (Suppl. 1), i48 (2013).
  75. Saleem, B. et al. Should imaging be a component of rheumatoid arthritis remission criteria? A comparison between traditional and modified composite remission scores and imaging assessments. Ann. Rheum. Dis. 70, 792798 (2011).
  76. Hirata, S. et al. A multi-biomarker score measures rheumatoid arthritis disease activity in the BeSt study. Rheumatology (Oxford) 52, 12021207 (2013).
  77. van der Helm-van Mil, A. H., Knevel, R., Cavet, G., Huizinga, T. W. & Haney, D. J. An evaluation of molecular and clinical remission in rheumatoid arthritis by assessing radiographic progression. Rheumatology (Oxford) 52, 839846 (2013).
  78. Centola, M. et al. Development of a multi-biomarker disease activity test for rheumatoid arthritis. PLoS ONE 8, e60635 (2013).
  79. Chen, R. et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 148, 12931307 (2012).
  80. James, H. M. et al. Common polymorphisms in the folate pathway predict efficacy of combination regimens containing methotrexate and sulfasalazine in early rheumatoid arthritis. J. Rheumatol. 35, 562571 (2008).
  81. Dervieux, T. et al. Polyglutamation of methotrexate with common polymorphisms in reduced folate carrier, aminoimidazole carboxamide ribonucleotide transformylase, and thymidylate synthase are associated with methotrexate effects in rheumatoid arthritis. Arthritis Rheum. 50, 27662774 (2004).
  82. Wessels, J. A. et al. Relationship between genetic variants in the adenosine pathway and outcome of methotrexate treatment in patients with recent-onset rheumatoid arthritis. Arthritis Rheum. 54, 28302839 (2006).
  83. Kooloos, W. M. et al. Optimalization of the clinical pharmacogenetic model to predict methotrexate treatment response: the influence of the number of haplotypes of MTHFR 1298A-677C alleles on probability to respond. Ann. Rheum. Dis. 68, 1371 (2009).
  84. Weisman, M. H. et al. Risk genotypes in folate-dependent enzymes and their association with methotrexate-related side effects in rheumatoid arthritis. Arthritis Rheum. 54, 607612 (2006).
  85. Dervieux, T. et al. Pharmacogenetic and metabolite measurements are associated with clinical status in patients with rheumatoid arthritis treated with methotrexate: results of a multicentred cross sectional observational study. Ann. Rheum. Dis. 64, 11801185 (2005).
  86. Lee, Y. C. et al. Investigation of candidate polymorphisms and disease activity in rheumatoid arthritis patients on methotrexate. Rheumatology (Oxford) 48, 613617 (2009).

Download references

Author information

Affiliations

  1. Arthritis Research UK Centre of Excellence for Musculoskeletal Genetics, Manchester Academy of Health Sciences, University of Manchester, Oxford Road, Manchester M13 9PT, UK.

    • Darren Plant &
    • Anne Barton
  2. School of Medicine & Medical Science, Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland.

    • Anthony G. Wilson

Contributions

All authors made substantial contributions to each stage of the preparation of this manuscript before submission.

Competing interests statement

The authors declare no competing interests.

Corresponding author

Correspondence to:

Author details

  • Darren Plant

    Darren Plant was awarded a PhD in Complex Disease Genetics from the University of Manchester, UK, in 2005. He is currently a Research Fellow at the University of Manchester. His research is focused on genetic and genomic predictors of responsiveness to treatment with biologic drugs in RA.

  • Anthony G. Wilson

    Anthony (Gerry) Wilson is Professor of Rheumatology at the School Of Medicine & Medical Science, Conway Institute, University of Dublin, Ireland. His research interests include genetic and epigenetic influences on severity of disease and therapeutic responses in rheumatoid arthritis (RA), and functional studies on novel RA related genetic loci.

  • Anne Barton

    Anne Barton is a Professor of Rheumatology and an Honourary Consultant Rheumatologist at the University of Manchester, UK. Her research interests include the genetic basis of susceptibility to psoriatic arthritis as well as genetic and genomic predictors of response to treatment. She is Principle Investigator for the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS), which was established to translate stratified medicine approaches into the clinical rheumatology setting.

Additional data