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OP0263 Synovial B-Cell Gene Signature Predicts Response To Rituximab Therapy
  1. A. Mahto,
  2. F. Humby,
  3. S. Gregoriadou,
  4. N. Ng,
  5. K. Blighe,
  6. L. Zou,
  7. M. Lewis,
  8. M. Bombardieri,
  9. S. Kelly,
  10. C. Pitzalis
  1. Experimental Medicine and Rheumatology, Queen Mary University, London, United Kingdom

Abstract

Background Predicting response to biologic therapies in RA remains a clinical challenge. The anti-CD20 monoclonal antibody Rituximab effectively depletes peripheral blood B-cells, however response rates are approximately 60%. We hypothesized that expression of CD20 in diseased tissue may be an important predictor of response, since up to 40% of patients have few/no B-cells present in the synovium.

Objectives To ascertain whether a synovial B-cell gene signature can enhance prediction of responsiveness to Rituximab therapy and identify genes involved in response/non-response to treatment.

Methods Synovial tissue was obtained using ultrasound-guided synovial biopsies from 20 patients with active RA who were treated with Rituximab therapy after failure of conventional DMARD and anti-TNF therapy. High-throughput quantitative real-time PCR for 190 genes was performed in collaboration with MedImmune (MedImmune, LLC) using the Fluidigm platform. Samples were classified as B-cell “rich” or “poor” according to levels of MS4A1 expression. Using an empirical Bayes statistical model, a further 17 significant (p<0.05) genes were identified to create a baseline B-cell gene expression signature. Hierarchical clustering and receiver operating characteristic curve analysis was used to assess ability to predict EULAR criteria response at 16 weeks. Gene expression was compared pre- and post-treatment in responders versus non-responders and correlated with delta DAS-28. Logistic regression with backward and forward selections using AIC was applied to identify further predictive models.

Results Seventeen genes were identified that clustered with MS4A1 expression to create the B-cell signature. Fifteen of these are directly involved in B-cell and plasma-cell signaling or differentiation pathways and immunoglobulin synthesis. Two genes identified within this signature play a pivotal role in antigen presentation and TH17 differentiation. The baseline B-cell signature had an area under ROC curve (AUC) of 0.87 (95%CI 0.76–1.0) to predict response to Rituximab therapy at 16 weeks. Three additional genes were identified segregating responders from non-responders; that when combined with the baseline B-cell signature the AUC improved to 0.95. Additionally, ten genes were identified that significantly correlated with delta DAS-28. High expression of genes involved in the formation of ectopic lymphoid structures and the TH17 lineage correlated with non-response to Rituximab therapy (all p<0.05).

Conclusions This study shows that a B-cell rich gene signature has high sensitivity to predict response to Rituximab therapy. High expression of TH17 related genes correlates with non-response to B-cell depletion, indicating that in a subset of patients, these pathways may play a more pivotal role in disease pathogenesis. The observed high expression of ELS associated genes in non-response may relate to variable synovial B-cell depletion; disruption of these follicles may be the key to response, particularly with repeat cycles. These findings warrant confirmation in a larger cohort of patients; however they strongly support the notion that a stratified approach to treatment relies upon the identification of biomarkers, both in the synovium and peripheral blood that reflect heterogenous molecular disease pathways in RA.

Disclosure of Interest None declared

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