Genetic variants within the MAP kinase signalling network and anti-TNF treatment response in rheumatoid arthritis

Lydia R Coulthard1, John C Taylor, Steve Eyre

  • 1NIHR-Leeds Musculoskeletal Biomedical Research Unit (NIHR-LMBRU), University of Leeds, Leeds, UK.

Abstract

Insights

Genetic variations in the p38 signalling network are linked to rheumatoid arthritis (RA) treatment response. Certain single nucleotide polymorphisms (SNPs) correlate with effectiveness of anti-tumour necrosis factor (TNF) therapies, particularly infliximab and adalimumab.

Area of Science:

  • Immunogenetics
  • Pharmacogenomics
  • Rheumatology

Background:

  • Rheumatoid arthritis (RA) treatment response to tumour necrosis factor (TNF) antagonists is variable.
  • Investigating genetic factors influencing anti-TNF therapy efficacy is crucial for personalized medicine.

Purpose of the Study:

  • To explore the association between genetic variations in the p38 signalling network and patient response to anti-TNF therapies in RA.
  • To identify specific single nucleotide polymorphisms (SNPs) that predict treatment outcomes.

Main Methods:

  • Genotyping of 1102 UK Caucasian RA patients receiving infliximab, adalimumab, or etanercept for 38 SNPs in 12 p38 network genes.
  • Regression analyses assessed genotype-treatment response correlation using DAS28 and EULAR criteria at 6 months.
  • Stratified analyses examined associations with individual anti-TNF agents.

Main Results:

  • Seven SNPs in five genes within the p38 network were associated with improved DAS28 scores, collectively explaining 3% of outcome variance.
  • SNPs in genes encoding upstream (MKK6) and downstream (MAPKAPK2, MSK1, MSK2) proteins, and p38-α MAPK (MAPK14), showed associations.
  • One SNP (rs2716191 in MAP2K6) correlated with EULAR response; associations were stronger for infliximab and adalimumab than etanercept.

Conclusions:

  • The p38 signalling network contains SNPs associated with anti-TNF response in RA, particularly for infliximab and adalimumab.
  • These genetic markers may contribute to predicting treatment efficacy.
  • Independent cohort validation is recommended to confirm these pharmacogenetic findings.

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