Proteomics to predict the response to tumour necrosis factor-α inhibitors in rheumatoid arthritis using a supervised cluster-analysis based protein score.

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.

Targeting CD1c-expressing classical dendritic cells to prevent thymus and activation-regulated chemokine (TARC)-mediated T-cell chemotaxis in rheumatoid arthritis.

OBJECTIVES: Thymus and activation-regulated chemokine (TARC) attracts cells that express the C-C chemokine receptor type 4 (CCR4), including CD4 T cells. As expression of CCR4 is increased on peripheral T cells and intra-articular interleukin (IL)-17-producing cells in patients with rheumatoid arthritis (RA), we investigated whether TARC plays a role in the attraction of T cells to the synovial compartment. In addition, we assessed the role of classical dendritic cells (cDCs) in the production of TARC in RA. METHOD: TARC was measured in synovial fluid (SF) samples from RA and osteoarthritis (OA) patients. Spontaneous and thymic stromal lymphopoietin (TSLP)-induced TARC production by mononuclear cells (MCs) and CD1c cDCs from peripheral blood (PB) and SF was assessed. The role of TARC in CD4 T-cell migration towards cDCs was assessed and the contribution of CD1c-expressing cells to TARC production was studied. RESULTS: TARC concentrations were higher in SF of RA patients compared to OA patients. MCs from SF produced TARC spontaneously and produced more TARC upon stimulation than paired PBMCs. Blocking TARC strongly inhibited CD4 T-cell chemotaxis by TSLP-stimulated cDCs, associated with decreased production of tumour necrosis factor (TNF)-α. Depletion of cDCs from SFMCs strongly reduced TARC production. CONCLUSIONS: TARC levels are increased in RA SF and our data indicate that this results from production by SFMCs and in particular CD1c cDCs. TARC attracts T cells and TARC secretion by MCs is crucially dependent on the presence of CD1c cDCs. Considering the potential of SF cDCs to activate T cells and induce pro-inflammatory cytokine secretion, targeting intra-articular cDCs constitutes a novel therapeutic approach in RA.