Background: Tumor necrosis factor (TNF) blockers have a high efficacy in treating Ankylosing Spondylitis (AS), yet up to 40% of AS patients show poor or even no response to this treatment. In this paper, we aim to build an approach to predict the response prior to clinical treatment.
Methods: AS patients during the active progression were included and treated with TNF blocker for 3 months. Patients who do not fulfill ASASAS40 were considered as poor responders. The Immunoglobulin G galactosylation (IgG-Gal) ratio representing the quantity of IgG galactosylation was calculated and candidate single nucleotide polymorphisms (SNPs) in patients treated with etanercept was obtained. Machine-learning models and cross-validation were conducted to predict responsiveness.
Results: Both IgG-Gal ratio at each time point and differential IgG-Gal ratios between week 0 and weeks 2, 4, 8, 12 showed significant difference between responders and poor-responders. Area under curve (AUC) of the IgG-Gal ratio prediction model was 0.8 after cross-validation, significantly higher than current clinical indexes (C-reactive protein (CRP) = 0.65, erythrocyte sedimentation rate (ESR) = 0.59). The SNP MYOM2-rs2294066 was found to be significantly associated with responsiveness of etanercept treatment. A three-stage approach consisting of baseline IgG-Gal ratio, differential IgG-Gal ratio in 2 weeks, and rs2294066 genotype demonstrated the ability to precisely predict the response of anti-TNF therapy (100% for poor-responders, 98% for responders).
Conclusions: Combination of different omics can more precisely to predict the response of TNF blocker and it is potential to be applied clinically in the future.
Keywords: Ankylosing spondylitis; Drug response prediction; IgG-gal ratio; MYOM2-rs2294066; TNF blocker.