THU0010 Polymorphisms in phase i-metabolising enzyme and hormone receptor genes influence the response to anti-tnf therapy
Authors: L.M. Canet, J.M. Sánchez-Maldonado, A. Rodríguez-Ramos, C.B. Lupiañez, H. Canhão, M. Martínez-Bueno, A. Escudero, J. Segura-Catena, S.B. Sørensen, M.L. Hetland, M.J. Soto-Pino, M. Á. Ferrer, A. García, B. Glintborg, I. Filipescu, E. Pérez-Pampin, A. González-Utrilla, M. Á. López-Nevot, P. Conesa-Zamora, A.A. den Broeder, S. da Vita, S.E. Hove Jacobsen, E. Collantes, L. Quartuccio, J.E. Fonseca, M.J. Coenen, V. Andersen, R. Cáliz-Cáliz, J. Sainz
Journal: Annals of the Rheumatic Diseases (2018)

<h3>Background</h3> Although the etiology of rheumatoid arthritis (RA) remains unclear, there are evidences suggesting a role of sex steroid hormones in determining the onset and progression of the disease. <h3>Objectives</h3> The aim of this study was to evaluate whether 47 single nucleotide polymorphisms (SNPs) in steroid hormone-related genes are associated with the risk of RA and anti-TNF drug response. <h3>Methods</h3> We conducted a case-control study in 3 European populations including 2936 RA patients and 2197 healthy controls. Of those a total of 1985 RA patients were treated with anti-TNF blockers. The association of potentially interesting markers with RA risk or drug response in the discovery population was validated through meta-analysis with data from DREAM and DANBIO registries. Results were corrected for multiple testing (Meff method; p=0.0011). The value of steroid hormone-related variants for prediction of anti-TNF drug response was also assessed using stepwise logistic regression analyses. The area under the curve (AUC) of a receiver operating characteristic (ROC) curve analysis and a −2 log likelihood ratio (LR) test were used to assess whether the genetic model fitted significantly better the data compared to the reference model. A randomization test (50.000 iterations) was run to confirm the consistency of the results. <h3>Results</h3> Although none of the selected variants played a relevant role in modulating RA risk, the meta-analysis of the linear regression data with those from the DREAM and DANBIO registries showed a significant correlation of the <i>CYP3A4</i><sub>rs11773597</sub> and <i>CYP2C9</i><sub>rs1799853</sub> variants with changes in DAS28 after the administration of anti-TNF drugs (p=0.00074, and p=0.006). An overall haplotype analysis also showed that the <i>ESR2</i><sub>GGG</sub> haplotype significantly associated with a reduced chance of having poor response to anti-TNF drugs (p=0.0009). Finally, a ROC curve analysis confirmed that a model built with 8 steroid hormone-related variants significantly improved the ability to predict drug response compared with the reference model including demographic and clinical variables (AUC=0.633 vs. 0.556; <i>P</i><sub>LRtest</sub>=1.52·10–6). <h3>Conclusions</h3> These data suggest that steroid hormone-related genes play a role in determining the response to anti-TNF drugs. <h3>Disclosure of Interest</h3> L. Canet: None declared, J. Sánchez-Maldonado: None declared, A. Rodríguez-Ramos: None declared, C. Lupiañez: None declared, H. Canhão: None declared, M. Martínez-Bueno: None declared, A. Escudero: None declared, J. Segura-Catena: None declared, S. Sørensen: None declared, M. Hetland: None declared, M. Soto-Pino: None declared, M. Ferrer: None declared, A. García: None declared, B. Glintborg: None declared, I. Filipescu: None declared, E. Pérez-Pampin: None declared, A. González-Utrilla: None declared, M. López-Nevot: None declared, P. Conesa-Zamora: None declared, A. den Broeder: None declared, S. da Vita: None declared, S. Hove Jacobsen: None declared, E. Collantes: None declared, L. Quartuccio: None declared, J. Fonseca: None declared, M. Coenen: None declared, V. Andersen Consultant for: VA has received compensation for consultancy and for being a member of an advisory board from MSD (Merck) and Janssen, R. Cáliz-Cáliz: None declared, J. Sainz: None declared