Cuppen Bvj, Fritsch-Stork Rde, Eekhout I, de Jager W, Marijnissen A C, Bijlsma Jwj, Custers M, van Laar J M, Lafeber Fpjg, Welsing Pmj
a Department of Rheumatology and Clinical Immunology , University Medical Center Utrecht , Utrecht , The Netherlands.
b 1st Medical Department and Ludwig Boltzmann Institute of Osteology , Hanusch Hospital of WGKK and AUVA Trauma Centre Meidling , Vienna , Austria.
Scand J Rheumatol. 2018 Jan;47(1):12-21. doi: 10.1080/03009742.2017.1309061. Epub 2017 Jun 26.
In rheumatoid arthritis (RA), it is of major importance to identify non-responders to tumour necrosis factor-α inhibitors (TNFi) before starting treatment, to prevent a delay in effective treatment. We developed a protein score for the response to TNFi treatment in RA and investigated its predictive value.
In RA patients eligible for biological treatment included in the BiOCURA registry, 53 inflammatory proteins were measured using xMAP® technology. A supervised cluster analysis method, partial least squares (PLS), was used to select the best combination of proteins. Using logistic regression, a predictive model containing readily available clinical parameters was developed and the potential of this model with and without the protein score to predict European League Against Rheumatism (EULAR) response was assessed using the area under the receiving operating characteristics curve (AUC-ROC) and the net reclassification index (NRI).
For the development step (n = 65 patient), PLS revealed 12 important proteins: CCL3 (macrophage inflammatory protein, MIP1a), CCL17 (thymus and activation-regulated chemokine), CCL19 (MIP3b), CCL22 (macrophage-derived chemokine), interleukin-4 (IL-4), IL-6, IL-7, IL-15, soluble cluster of differentiation 14 (sCD14), sCD74 (macrophage migration inhibitory factor), soluble IL-1 receptor I, and soluble tumour necrosis factor receptor II. The protein score scarcely improved the AUC-ROC (0.72 to 0.77) and the ability to improve classification and reclassification (NRI = 0.05). In validation (n = 185), the model including protein score did not improve the AUC-ROC (0.71 to 0.67) or the reclassification (NRI = -0.11).
No proteomic predictors were identified that were more suitable than clinical parameters in distinguishing TNFi non-responders from responders before the start of treatment. As the results of previous studies and this study are disparate, we currently have no proteomic predictors for the response to TNFi.
在类风湿关节炎（RA）中，在开始治疗前识别对肿瘤坏死因子-α抑制剂（TNFi）无反应者至关重要，以避免有效治疗的延迟。我们开发了一种用于评估RA患者对TNFi治疗反应的蛋白质评分，并研究了其预测价值。
在纳入BiOCURA注册库的适合生物治疗的RA患者中，使用xMAP®技术检测了53种炎症蛋白。采用一种监督聚类分析方法——偏最小二乘法（PLS）来选择最佳蛋白质组合。利用逻辑回归开发了一个包含易于获得的临床参数的预测模型，并使用接受者操作特征曲线下面积（AUC-ROC）和净重新分类指数（NRI）评估该模型在有无蛋白质评分情况下预测欧洲抗风湿病联盟（EULAR）反应的潜力。
在开发阶段（n = 65例患者），PLS显示了12种重要蛋白质：CCL3（巨噬细胞炎性蛋白，MIP1a）、CCL17（胸腺和活化调节趋化因子）、CCL19（MIP3b）、CCL22（巨噬细胞衍生趋化因子）、白细胞介素-4（IL-4）、IL-6、IL-7、IL-15、可溶性分化簇14（sCD14）、sCD74（巨噬细胞迁移抑制因子）、可溶性IL-1受体I和可溶性肿瘤坏死因子受体II。蛋白质评分几乎未改善AUC-ROC（从0.72至0.77）以及分类和重新分类能力（NRI = 0.05）。在验证阶段（n = 185例），包含蛋白质评分的模型未改善AUC-ROC（从0.71至0.67）或重新分类情况（NRI = -0.11）。
在治疗开始前，未发现比临床参数更适合区分TNFi无反应者和反应者的蛋白质组学预测指标。由于先前研究和本研究结果不一致，目前我们没有用于预测TNFi反应的蛋白质组学预测指标。
