Background/Purpose: In clinical practice in rheumatoid arthritis (RA), it has been noted that each anti-rheumatic therapy delivers a different outcome for individual RA patients and this makes it difficult to prescribe the most efficacious treatment for them. Being able to predict a patient’s response/outcome before they are treated would allow doctors to prescribe the cytokine therapy that is the most efficacious for each RA patient. It is critical to identify molecular biomarkers that can predict patient response to anti-TNF-a or anti-IL-6 based therapies before patients are treated so that non-effective therapies are eliminated and more effective ones can be prescribed for patients at an earlier stage. We believe that identifying reliable predictive biomarkers will make it easier to follow EULAR’s treat-to-target recommendation by allowing clinicians to know in advance if a treatment strategy will achieve the treatment goal (target) that has been pre-determined for each RA patient. 

Methods: We enrolled 138 RA patients (naïve and non-naïve to anti-cytokine therapy) and measured 31 cytokines/chemokines/soluble receptors in their serum before administering tocilizumab or etanercept for 16 weeks. We selected parameters that correlated with patient’s week 16 DAS28-CRP score by multiple linear analyses and identified biomarkers that could predict if patients would experience complete remission or non-remission by multiple logistic analyses.

Results: Multiple linear regression analysis based on patients’ week 16 DAS28 revealed that sgp130, logIL-6, logIL-8, logEotaxin, logIP-10, logVEGF, logsTNFR-I and logsTNFR-II serum levels before therapy were potential biomarkers to predict biologic naïve patients’ week 16 DAS28. Sgp130, logIP-10, logsTNFR-II and logIL-6 were predictive of complete remission or non-remission to tocilizumab therapy by multiple logistic analyses. A high sgp130 level was the most reliable predictor of patients’ outcome. Additionally, we found logIL-9, logVEGF and logTNF-a to be less reliable at predicting the week 16 DAS28 score in naïve etanercept patients. Most of these biomarkers, especially sgp130, are involved in RA pathogenesis and IL-6 signal transduction, which further suggests that they are highly reliable.

Conclusion: We discovered reliable biomarkers that can predict clinical disease activity and outcome before RA patients undergo anti-IL-6 and anti TNF-a therapy. Most of the predictive biomarkers are involved in RA pathogenesis. Predicting treatment disease activity and outcome for anti-IL-6 reagent can assist doctors to more effectively pursue EULAR’s treat-to-target approach by identifying in advance if IL-6 blockades will allow individual RA patients to reach their treatment target.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/pretreatment-prediction-of-response-to-anti-cytokine-therapy-using-serum-cytokinechemokinesoluble-receptors-in-individual-rheumatoid-arthritis-patient/