A MOLECULAR SIGNATURE RESPONSE CLASSIFIER PREDICTS THE LIKELIHOOD OF EULAR NON-RESPONSE TO TNF INHIBITOR THERAPIES IN RA: RESULTS FROM A RETROSPECTIVE COHORT ANALYSIS

Background: Following RA treatment recommendations, most people with rheumatoid arthritis (RA) begin targeted therapy with TNF inhibitors (TNFi), even though inadequate response to TNFi therapies is widespread. Treatment changes from one medication to the next are currently fueled by disease-activity measures and eventually result in disease control for most patients; however, this “trial-and-error” approach wastes precious time on ineffective treatments. A delay in reaching treat-to-target goals has a negative effect on patient burden and, possibly, disease progression. Useful predictors for TNFi response have been challenging to identify but a specific molecular signature response classifier (MSRC) test was shown to be predictive for inadequate response to TNFi therapies. The impact of such identification has the potential to result in improved patient outcomes, but further validation would be welcome, especially for response criteria other than ACR50, and in a stringent treat-to-target setting with lower baseline disease activity. Objectives: To validate the predictive value of the MSRC test in identifying those patients who do not meet EULAR good response criteria after 6 months of TNFi treatment. Methods: Data from a prospective cohort study conducted in the Sint Maartenskliniek (Nijmegen, the Netherlands) of RA patients who started adalimumab or etanercept TNFi as their first biologic were included. Baseline RNA samples and clinical assessments were used to identify patients who had a molecular signature of non-response to TNFi therapy. Outcomes were calculated at six months using DAS28-CRP-based EULAR good response, and high and low confidence responders and non-responders were identified using Monte Carlo simulation with 2,000 repeats and 70% precision cut off. Outcome measurements were blinded for test results. Treatment switch before 6 months was imputed as non-response. Odds ratios and area under the ROC curve (AUC) assessments were used to evaluate the ability of the MSRC test to predict inadequate response at 6 months against EULAR good response criteria. Results: A total of 68 out of 88 RA patients were identified to have a high-confidence response status and were included in analyses ( Table 1 ). EULAR good response was observed in 45.5% (31/68) of patients. Patients were stratified according to detection of a molecular signature of non-response with an AUC of 0.61. The odds that a patient with the molecular signature of non-response at baseline failed to achieve a EULAR good response at 6 months was four times greater than that of a patient lacking the molecular signature (odds ratio 4.0, 95% confidence interval 1.2-13.3). Table 1. Patient demographics Characteristic RA patients (N = 68 ) Age, median (SD) 57 (11) Female, n (%) 43 (63.2) CCP positive, n (%) 34 (50.0) RF positive, n (%) 38 (55.9) Prescribed adalimumab at baseline, n (%) 11 (16.2) Prescribed etanercept at baseline, n (%) 57 (83.8) Conclusion: In this validation study, the molecular signature of non-response identified patients who did not fulfill the EULAR good response criteria to TNFi therapies. The patient selection process for this study had limitations; additional analysis in an alternative cohort would further verify the performance of the MSRC test. Nevertheless, the test, previously validated for ACR50, now has been validated using EULAR good response in a treat-to-target setting. REFERENCES: [1]Schipper LG et al, Time to achieve remission determines time to be in remission. Arthritis Res Ther 201 [2]Mellors T, et al. Clinical Validation of a Blood-Based Predictive Test for Stratification of Response to Tumor Necrosis Factor Inhibitor Therapies in Rheumatoid Arthritis Patients. Network and Systems Medicine 2020 [3]Tweehuysen L et al. Predictive value of ex-vivo drug-inhibited cytokine production for clinical response to biologic DMARD therapy in rheumatoid arthritis. Clin Exp Rheumatol 2019 Disclosure of Interests: Lixia Zhang Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Celeste van der Tog: None declared, Alfons den Broeder Consultant of: Abbvie, Amgen, Cellgene, Roche, Biogen, Lilly, Novartis, Celltrion Sanofi, Gilead., Grant/research support from: Abbvie, Amgen, Cellgene, Roche, Biogen, Lilly, Novartis, Celltrion Sanofi, Gilead., Ted Mellors Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Erin Connolly-Strong Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Johanna Withers Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Alex Jones Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation, Viatcheslav Akmaev Shareholder of: Scipher Medicine Corporation, Employee of: Scipher Medicine Corporation Citation: Ann Rheum Dis, volume 80, supplement 1, year 2021, page 478Session: Rheumatoid arthritis - prognosis, predictors and outcome (POSTERS only)