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RHEUMATOID ARTHRITIS

Unravelling the pharmacogenomics of TNF inhibition

Despite the previous identification of genes involved in the treatment response to TNF inhibition in rheumatoid arthritis, no genetic biomarkers are currently used in clinical decision-making. Might the heterogeneous nature of the disease activity score, which is often used as the outcome measure in genetic studies, partly explain this gap?

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References

  1. Massey, J. et al. Genome-wide association study of response to tumour necrosis factor inhibitor therapy in rheumatoid arthritis. Pharmacogenomics J. 18, 657–664 (2018).

    CAS  Article  Google Scholar 

  2. Umicevic Mirkov, M. et al. Estimation of heritability of different outcomes for genetic studies of TNFi response in patients with rheumatoid arthritis. Ann. Rheum. Dis. 74, 2183–2187 (2015).

    Article  Google Scholar 

  3. Loos, R. J. & Yeo, G. S. The bigger picture of FTO: the first GWAS-identified obesity gene. Nat. Rev. Endocrinol. 10, 51–61 (2014).

    CAS  Article  Google Scholar 

  4. Singh, S. et al. Obesity and response to anti-tumor necrosis factor-alpha agents in patients with select immune-mediated inflammatory diseases: a systematic review and meta-analysis. PLOS ONE 13, e0195123 (2018).

    Article  Google Scholar 

  5. Iles, M. M. et al. A variant in FTO shows association with melanoma risk not due to BMI. Nat. Genet. 45, 428–432 (2013).

    CAS  Article  Google Scholar 

  6. MacGregor, A. J. et al. Characterizing the quantitative genetic contribution to rheumatoid arthritis using data from twins. Arthritis Rheum. 43, 30–37 (2000).

    CAS  Article  Google Scholar 

  7. Nielsen, C. S. et al. Individual differences in pain sensitivity: genetic and environmental contributions. Pain 136, 21–29 (2008).

    Article  Google Scholar 

  8. Wen, H. et al. Comparison of expectations of physicians and patients with rheumatoid arthritis for rheumatology clinic visits: a pilot, multicenter, international study. Int. J. Rheum. Dis. 15, 380–389 (2012).

    Article  Google Scholar 

  9. Canhao, H. et al. TRAF1/C5 but not PTPRC variants are potential predictors of rheumatoid arthritis response to anti-tumor necrosis factor therapy. Biomed. Res. Int. 2015, 490295 (2015).

    Article  Google Scholar 

  10. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    CAS  Article  Google Scholar 

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Correspondence to Marieke J. H. Coenen.

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Coenen, M.J.H. Unravelling the pharmacogenomics of TNF inhibition. Nat Rev Rheumatol 14, 689–690 (2018). https://doi.org/10.1038/s41584-018-0114-5

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  • DOI: https://doi.org/10.1038/s41584-018-0114-5

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