THU0190 The Prediction Model for Methotrexate Efficacy Consisting of 9 SNPS Selected by Dmet Microarray Profiling in Japanese RA Patients. (9th June 2015)

• Record Type: Journal Article
• Authors: Kumagai, S.; Tsuji, G.; Ichise, Y.; Umeda, R.; Muta, A.; Abe, K.; Izumi, M.; Uemura, Y.; Uga, H.; Kurata, H.; Misaki, K.; Onishi, A.
• Is Part Of: Annals of the rheumatic diseases. Volume 74(2015)Supplement 2
• Page Start: 263
• Page End: 263
• Publication Date: 2015-06-09
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2015-eular.3374
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Efficacy and toxicity of methotrexate (MTX) differs among individual patients, suggesting the influence of genetic variations in enzymes associated with MTX metabolism and folate metabolic pathway. We had investigated 1, 971 polymorphisms of 246 enzymes/transporters relevant to the hepatotoxicity of MTX by using the DMET microarray (Affymetrix Inc.) and direct-sequencing (DS) method, and had developed a predictive model of hepatotoxicity composed of 13 SNPs in 13 genes. The validity was confirmed using other cohort, and was reported in the 2014 EULAR meeting. We supposed that the SNPs identified in connection with hepatotoxicity might have a relation to efficacy of MTX. Objectives: To construct a genetic model for prediction of efficacy of MTX composed of small numbers of SNPs including hepatotoxicity-related SNPs. Methods: First, the 13 SNPs predictive model which discriminated MTX hepatotoxicity with AUC, sensitivity, and specificity of 0.85, 80%, and 86%, respectively, was shown to discriminate the responders from the nonresponders with AUC, sensitivity, and specificity of 0.82, 63%, and 89%, respectively. Next, we obtained a prediction model of MTX efficacy consisting of 9 SNPs by stepwise selection procedure after addition of 8 SNPs to the 13 SNPs. The model discriminated the responders from the nonresponders with sensitivity and specificity of 100% and 85% (Figure ). Finally, by backward elimination method, number of SNPs of the predictive model was able to decrease to 6 with sensitivity and specificity of 91% and 89%, respectively. Results: First, the 13 SNPs predictive model which could discriminate MTX hepatotoxicity with AUC, sensitivity, and specificity of 0.85, 80%, and 86%, respectively, could discriminate the responders from the nonresponders with AUC, sensitivity, and specificity of 0.82, 63%, and 89%, respectively. Next, after addition of 8 SNPs to the 13 SNPs, multiple logistic regression analysis yielded a prediction model of MTX efficacy consisting of 9 SNPs, which could discriminate the responders from the nonresponders with sensitivity and specificity of 100% and 85% (Figure 1 ). Finally, by backward elimination method, we obtained a predictive model by a combination of only 6 SNPs, which could discriminate the responders with sensitivity and specificity of 91% and 89%, respectively. Conclusions: Our 13 SNPs model for prediction of MTX hepatotoxicity was shown to discriminate the responders to MTX from the nonresponders. A predictive model for MTX efficacy consisting of 9 SNPs was established with a sensitivity of 100% and a specificity of 85% by stepwise method using 21 SNPs. The number of SNPs in the model could be reduced to 6 SNPs with satisfactory sensitivity and specificity, although the model should be validated with a larger scale of prospective study. Disclosure of Interest: S. Kumagai Consultant for: Sysmex Co, G. Tsuji: None declared, Y. Ichise: None declared, R. Umeda: None declared, A. Muta: None declared, K. Abe: None declared, M. Izumi: None declared, Y. Uemura: None declared, H. Uga Employee of: Sysmex Co, H. Kurata Employee of: Sysmex Co, K. Misaki: None declared, A. Onishi: None declared