OBJECTIVE: Recently, two genome-wide association studies identified single nucleotide polymorphisms (SNPs) significantly associated with the treatment response to tumor necrosis factor α (TNFα) inhibitors in patients with rheumatoid arthritis (RA). We aimed to replicate these results and identify SNPs and the possible biological pathways associated with the treatment response to TNFα inhibitors. METHODS: TNFα-naive patients with RA, who had available DNA and initiated TNFα inhibitor therapy between 1999 and 2008, were identified in the DANBIO registry and genotyped using the Illumina HumanHap550K Duo array. The associations between SNPs and changes in the absolute and the relative Disease Activity Score, and European League Against Rheumatism good versus no response after 14 weeks of treatment were tested. SNP data were combined with two independent cohorts in a meta-analysis. A gene-set enrichment analysis (GSEA) was carried out to identify the biological pathways associated with the treatment response. RESULTS: After genotyping and quality control, 486 450 SNPs were analyzed in 196 Danish patients with moderate to severe RA treated with infliximab (n=142), etanercept (n=12), and adalimumab (n=42). None of the previously identified SNPs were confirmed in our dataset or in meta-analyses of available studies. Other potential SNPs were identified, but none achieved genome-wide significance. A GSEA identified the transforming growth factor β, TNF, mitogen-activated protein kinase, and mammalian target of rapamycin pathways to have a potential influence on the treatment response. CONCLUSION: In a genome-wide association study of 196 genetically homogenous Danish patients with RA and in a meta-analysis, we found no SNPs associated with treatment response to TNFα inhibitors. A GSEA suggested that the transforming growth factor β, TNF, mitogen-activated protein kinase, and mammalian target of rapamycin pathways may be associated with treatment response.
OBJECTIVE: Recently, two genome-wide association studies identified single nucleotide polymorphisms (SNPs) significantly associated with the treatment response to tumor necrosis factor α (TNFα) inhibitors in patients with rheumatoid arthritis (RA). We aimed to replicate these results and identify SNPs and the possible biological pathways associated with the treatment response to TNFα inhibitors. METHODS:TNFα-naive patients with RA, who had available DNA and initiated TNFα inhibitor therapy between 1999 and 2008, were identified in the DANBIO registry and genotyped using the Illumina HumanHap550K Duo array. The associations between SNPs and changes in the absolute and the relative Disease Activity Score, and European League Against Rheumatism good versus no response after 14 weeks of treatment were tested. SNP data were combined with two independent cohorts in a meta-analysis. A gene-set enrichment analysis (GSEA) was carried out to identify the biological pathways associated with the treatment response. RESULTS: After genotyping and quality control, 486 450 SNPs were analyzed in 196 Danish patients with moderate to severe RA treated with infliximab (n=142), etanercept (n=12), and adalimumab (n=42). None of the previously identified SNPs were confirmed in our dataset or in meta-analyses of available studies. Other potential SNPs were identified, but none achieved genome-wide significance. A GSEA identified the transforming growth factor β, TNF, mitogen-activated protein kinase, and mammalian target of rapamycin pathways to have a potential influence on the treatment response. CONCLUSION: In a genome-wide association study of 196 genetically homogenous Danish patients with RA and in a meta-analysis, we found no SNPs associated with treatment response to TNFα inhibitors. A GSEA suggested that the transforming growth factor β, TNF, mitogen-activated protein kinase, and mammalian target of rapamycin pathways may be associated with treatment response.
Authors: A Julià; C Ferrándiz; E Dauden; E Fonseca; E Fernández-López; J L Sanchez-Carazo; F Vanaclocha; L Puig; D Moreno-Ramírez; J L Lopez-Estebaranz; E Herrera; P de la Cueva; G Ávila; A Alonso; R Tortosa; M López-Lasanta; S Marsal Journal: Pharmacogenomics J Date: 2014-11-18 Impact factor: 3.550
Authors: A Ferreiro-Iglesias; A Montes; E Perez-Pampin; J D Cañete; E Raya; C Magro-Checa; Y Vasilopoulos; T Sarafidou; R Caliz; M A Ferrer; B Joven; P Carreira; A Balsa; D Pascual-Salcedo; F J Blanco; M J Moreno-Ramos; A Fernández-Nebro; M C Ordóñez; J J Alegre-Sancho; J Narváez; F Navarro-Sarabia; V Moreira; L Valor; R García-Portales; A Marquez; J Martin; J J Gómez-Reino; A Gonzalez Journal: Pharmacogenomics J Date: 2015-04-21 Impact factor: 3.550
Authors: A Julià; A Fernandez-Nebro; F Blanco; A Ortiz; J D Cañete; J Maymó; M Alperi-López; B Fernández-Gutierrez; A Olivè; H Corominas; A Erra; I Acosta-Colman; A Alonso; M López-Lasanta; R Tortosa; J Tornero; S Marsal Journal: Pharmacogenomics J Date: 2015-04-21 Impact factor: 3.550
Authors: Maša Umiċeviċ Mirkov; Jing Cui; Sita H Vermeulen; Eli A Stahl; Erik J M Toonen; Remco R Makkinje; Annette T Lee; Tom W J Huizinga; Renee Allaart; Anne Barton; Xavier Mariette; Corinne Richard Miceli; Lindsey A Criswell; Paul P Tak; Niek de Vries; Saedis Saevarsdottir; Leonid Padyukov; S Louis Bridges; Dirk-Jan van Schaardenburg; Tim L Jansen; Ellen A J Dutmer; Mart A F J van de Laar; Pilar Barrera; Timothy R D J Radstake; Piet L C M van Riel; Hans Scheffer; Barbara Franke; Han G Brunner; Robert M Plenge; Peter K Gregersen; Henk-Jan Guchelaar; Marieke J H Coenen Journal: Ann Rheum Dis Date: 2012-12-11 Impact factor: 19.103
Authors: Amit Kishore; Britt-Sabina Petersen; Marcel Nutsua; Joachim Müller-Quernheim; Andre Franke; Annegret Fischer; Stefan Schreiber; Martin Petrek Journal: Hum Genet Date: 2018-07-27 Impact factor: 4.132