SAT0062 STRATIFIED MEDICINE FOR RHEUMATOID ARTHRITIS: PREDICTING RESPONSE TO BIOLOGIC THERAPY USING IMMUNE CELL SIGNATURES

Background Treatment selection of biologic therapy for patients with rheumatoid arthritis (RA) is currently a trial and error process, with approximately 40% failing to respond well to the first biologic. The lack of biomarkers to predict treatment response leads to further pain, joint damage, patient anxiety and is cost ineffective for those who are non-responders. Objectives We aim to identify immune signatures from a pre-treatment blood test to inform the choice of treatment strategies for stratified medicine. Methods RA patients with active disease (DAS28 > 5.1) who failed to respond to conventional DMARDs and were due to commence biologic treatment were included in the BRAGGSS cohort. Peripheral blood mononuclear cells (PBMCs) taken before the initiation of biologic treatment were available for 300 patients (good (60%), moderate (25%), and non-responders (15%) to various biologics (anti-TNFs (68%), Rituximab (16%), Tocilizumab (13%), Abatacept (3%)). PBMCs were left unstimulated or stimulated (anti-CD3/CD28 beads or 10 ng/mL LPS) and stained with three flow cytometry panels including specific surface, intracellular and intranuclear markers to deeply characterise the function of several subsets of monocytes, NK cells, T cells and B cells (e.g. disease-associated cell populations such as PD-1hiCXCR5- peripheral T helpers (Rao DA Nature 2017), CD27-HLA-DR+ effector CD4+ T cells (Fonseka CY Sci Transl Med 2018) and cytotoxic PD-1+CXCR5- CD8+ T cell subsets, Tbet+CD11c+ autoimmune-associated B cells, CD14+IL1β+ pro-inflammatory monocytes (Zhang F BioRxiv 2018)). Hypothesis-free clustering algorithms were also used for data analysis to identify unreported cell populations. Statistical association testing was preformed using mixed effects ordinal (EULAR response) or linear (DAS28 or its components) regression models. Results Preliminary interim analysis identified associations between pan-biologic non-response and spontaneous expression of pro-inflammatory cytokines (Th1, Th17 lineage) by CD4+ T cells (p Conclusion Ongoing work involves the replication of these results. The identification of immune cell types associated with response to a particular class of biologic drugs (e.g. anti-TNFs, but not other classes) might inform the choice of first line biologic drugs based on individual patients’ immune profiles (stratification to treatment response categories). Cells or cytokines generally associated with non-response to all biologics could represent new therapeutic targets, at least in some patients groups. Acknowledgement AMP RA/SLE Disclosure of Interests Gemma Radley: None declared, Ben Mulhearn: None declared, Laura Donlin: None declared, Jennifer H. Anolik: None declared, Michael Brenner Grant/research support from: Roche: sponsored research agreement on stromal cells (but has nothing to do with checkpoint related disease), Consultant for: GSK: consultant. (I am part of their immunology network, a group of about 8 immunologists who advise them regularly and broadly in the areas of inflammation and infection)., Soumya Raychaudhuri: None declared, Kimme Hyrich Grant/research support from: Grants to institution: BMS, Pfizer, UCB, Ann Morgan: None declared, Gerry Wilson: None declared, John Isaacs Grant/research support from: Pfizer, Grant/research support from: Pfizer, Consultant for: Abbvie, Pfizer, Roche, Galvani, Merck, Gilead, Eli Lilly, Amgen, Janssen, Celltrion, NAPP, Consultant for: Abbvie, Pfizer, Roche, Galvani, Merck, Gilead, Eli Lilly, Amgen, Janssen, Celltrion, NAPP, Speakers bureau: Abbvie, Pfizer, Eli Lilly, Speakers bureau: Abbvie, Pfizer, Eli Lilly, Tracy Hussell: None declared, Anne Barton: None declared, Sebastien Viatte: None declared