BackgroundTumor 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.MethodsAS 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.ResultsBoth 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).ConclusionsCombination of different omics can more precisely to predict the response of TNF blocker and it is potential to be applied clinically in the future.

中文翻译：

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IgG半乳糖基化状态结合MYOM2-rs2294066精确预测强直性脊柱炎的抗TNF反应

背景肿瘤坏死因子 (TNF) 阻滞剂在治疗强直性脊柱炎 (AS) 方面具有很高的疗效，但高达 40% 的 AS 患者对此治疗反应不佳甚至没有反应。在本文中，我们旨在建立一种方法来预测临床治疗前的反应。方法 包括积极进展期间的 AS 患者并用 TNF 阻滞剂治疗 3 个月。不符合 ASASAS40 的患者被视为反应差。计算代表 IgG 半乳糖基化量的免疫球蛋白 G 半乳糖基化 (IgG-Gal) 比率，并获得依那西普治疗的患者的候选单核苷酸多态性 (SNP)。进行了机器学习模型和交叉验证来预测响应能力。结果每个时间点的 IgG-Gal 比率和第 0 周和第 2、4、8、12 周之间的差异 IgG-Gal 比率均显示出反应者和反应差者之间的显着差异。交叉验证后，IgG-Gal比值预测模型的曲线下面积（AUC）为0.8，显着高于目前的临床指标（C反应蛋白（CRP）= 0.65，红细胞沉降率（ESR）= 0.59）。发现 SNP MYOM2-rs2294066 与依那西普治疗的反应性显着相关。由基线 IgG-Gal 比值、2 周内差异 IgG-Gal 比值和 rs2294066 基因型组成的三阶段方法证明能够精确预测抗 TNF 治疗的反应（反应差者为 100%，反应者为 98% ）。