Galectin-9 and CXCL10 as biomarkers for disease activity in juvenile dermatomyositis: a longitudinal cohort study and multi-cohort validation

OBJECTIVE: Objective evaluation of disease activity is challenging in patients with juvenile dermatomyositis (JDM) due to lack of biomarkers, but crucial to avoid both under- and overtreatment. Recently, we identified two proteins that highly correlate with JDM disease activity: galectin-9 and CXCL10. Here, we validate galectin-9 and CXCL10 as biomarkers for disease activity, assess disease-specificity and investigate their potency to predict flares. METHODS: Galectin-9 and CXCL10 were measured in serum samples of 125 unique JDM patients in three international cross-sectional cohorts and a local longitudinal cohort, by multiplex immunoassay. Disease-specificity was examined in 50 adults with (dermato)myositis and 61 patients with other systemic autoimmune diseases. RESULTS: Galectin-9 and CXCL10 outperformed the currently used marker creatine kinase (CK) to distinguish between JDM patients with active disease and remission, both cross-sectionally and longitudinally (area ROC curve: 0.86-0.90 for galectin-9 and CXCL10, 0.66-0.68 for CK). The sensitivity and specificity were 0.84 and 0.92 for galectin-9, and 0.87 and 1.00 for CXCL10. In 10 prospectively followed patients with a flare, continuously elevated or rising biomarker levels suggested an imminent flare up to several months before symptoms, even in absence of elevated CK. Galectin-9 and CXCL10 distinguished between active disease and remission in adults with (dermato)myositis and were suited for measurement in minimally-invasive dried blood spots. CONCLUSIONS: Galectin-9 and CXCL10 were validated as sensitive and reliable biomarkers for disease activity in (J)DM. Implementation of these biomarkers into clinical practice, as tools to monitor disease activity and guide treatment, might facilitate personalized treatment strategies

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

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

<p><b>Objective</b>: 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.</p> <p><b>Method</b>: 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).</p> <p><b>Results</b>: 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).</p> <p><b>Conclusion</b>: 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.</p