OBJECTIVE: 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.
METHOD: 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).
RESULTS: 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).
CONCLUSION: 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.