Article Text
Abstract
Background Anti-tumor necrosis factor (anti-TNF) agents have drastically improved the clinical course of Rheumatoid Arthritis (RA). Despite its success, there is a substantial number of patients (20-40%) who do not respond to this therapy. The high costs of these therapies as well as the availability of alternative biological treatments have increased the interest in identifying markers that help predict the response to anti-TNF therapy.
Objectives The present study was undertaken to identify genes associated with anti-TNF response in Rheumatoid Arthritis. Candidate genes were selected from a recent Genome-Wide Association Study (GWAS) performed in Denmark population.
Methods Genomic DNA was obtained from 315 Spanish RA patients having received an anti-TNF agent as their first biologic therapy. Treatment response was defined at 12 weeks using both the reduction in the DAS28 activity score and the EULAR treatment response criteria. SNPs the loci having the strongest evidence for statistical association for anti-TNF response (P<5x10-6 in the Danish population) were selected and genotyped using the Taqman genotyping platform. Statistical association analyses were performed using the chi-square test and linear regression. The statistical results from the two RA patient cohorts were integrated by metaanalysis.
Results The PDE3A-SLOC1C1 locus SNP rs3794271 showed a highly significant association with anti-TNF treatment response in our patient cohort (P < 5e-5). Combining the statistical evidence from the Spanish and Danish RA cohorts, the associated SNP rs3794271 reached a genome-wide significant level of association with the response to anti-TNF therapy (P <5 x10-8).
Conclusions The present findings establish the PDE3A-SLOC1C1 locus as a strong genetic marker of anti-TNF therapy response.
References Krintel, SB, G Palermo, JS Johansen et al.: Investigation of single nucleotide polymorphisms and biological pathways associated with response to TNFalpha inhibitors in patients with rheumatoid arthritis. Pharmacogenet Genomics 22, 577-89 (2012)
Acknowledgements This work was supported by the Spanish Ministry of Economy and Competitiveness
Project grants (PSE-010000-2006-6, IPT-010000-2010-36).
Disclosure of Interest None Declared