Functional FCGR3A 158 V/F and IL-6 -174 C/G polymorphisms predict response to biologic therapy in patients with
Young Ho Lee1, Sang-Cheol Bae, Gwan Gyu Song
1Division of Rheumatology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, Korea University Medical Center, 126-1 ga, Anam-dong, Seongbuk-gu, Seoul, 136-705, Korea, lyhcgh@korea.ac.kr.
Abstract:
The aim of this study was to investigate whether the Fc gamma receptor 3A (FCGR3A) 158 V/F and interleukin-6 (IL-6) promoter -174 G/C polymorphisms can predict the response to biologic-based therapy in patients with rheumatoid arthritis (RA). We conducted a meta-analysis of studies on the association between the FCGR3A V/F polymorphism or the IL-6 -174 C/G polymorphism and non-responsiveness to biologic therapy in RA patients. A total of 10 studies involving 1,427 patients were considered. These studies consisted of seven studies on the FCGR3A polymorphism and three studies on the IL-6 polymorphism. Meta-analysis showed no association between the FCGR3A VV+VF genotype and non-responders to biologic therapy [odds ratio (OR) 0.881, 95 % confidence interval (CI) 0.505-1.537, p = 0.655]. However, stratification by biologic type indicated an association between the FCGR3A VV+VF genotype and non-responders to rituximab (OR 0.566, 95 % CI 0.373-0.857, p = 0.007), but no association was found in non-responders to tumor necrosis factor (TNF)-blockers (OR 1.337, 95 % CI 0.869-2.056, p = 0.186). Meta-analysis revealed no association between the IL-6 CC+CG genotype and non-responders to the biologics (OR 3.233, 95 % CI 0.766-13.64, p = 0.110). However, an association was found between the IL-6 CC+CG genotype and non-responders to anti-TNF therapy (OR 8.030, 95 % CI 1.807-33.68, p = 0.006). This meta-analysis demonstrates that FCGR3A V allele carriers show a better response to rituximab, and individuals carrying the IL-6 -174 C allele show a poorer response to anti-TNF therapy for RA. Genotyping for these polymorphisms may be a useful tool for predicting the response to biologics with respect to personalized medicine.
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