Background/Purpose: Rheumatoid arthritis (RA) is a complex immune mediated disease that affects 1% of the population. Even with modern therapeutic strategies, many patients with RA still experience a poor prognosis; each of the marketed biologic drugs improves joint inflammation in only 70% of patients. Due to lack of understanding of RA pathophysiology, it is currently impossible to predict which patients will respond to specific treatments, with the current approach being that of trial-and-error. RA comprises different endotypes, each associated with different disease pathways and responding to different biologic drugs. Here we aim to provide personalised treatment options to patients with RA by predicting their response to treatment using deep immunophenotyping by mass cytometry.

Methods: Patients enrolled in the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS) have failed to respond to conventional synthetic DMARDs and glucocorticoids for at least 6 months. Prior to biologic treatment, blood samples were taken from patients and peripheral blood mononuclear cells (PBMCs) were isolated. Clinical data for these patients was collected at 3, 6 and 12 months post-biologic treatment to assess patient response using EULAR criteria. PBMCs from 10 RA (treated with a single or combination biologic treatment) and 10 disease-free-controls (DFC) were deeply immunophenotyped using mass cytometry panels (total of 62 markers) covering the majority of T, B and monocyte populations. Cells were stimulated with Phorbol 12-myristate 13-acetate (PMA), ionomycin, brefeldin A, and monensin for 3h, stained with isotope labelled antibodies and acquired by Helios. Data were analysed by an established R-based workflow. After quality control, T cells were clustered with FlowSOM, and visualised with dimensionality reduction (t-SNE). Clusters with differential abundance were identified with logistic mixed models (Fixed variable: age, sex. Random variable: Sample ID).

Results: Using unsupervised machine learning algorithms, our preliminary data identified various leukocyte subsets enriched in RA compared to DFC samples, including Th1-like (IL-2⁺ T-bet⁺ CD4⁺ TCM) (P ≤ 0.05) and Th1- like Th17 (IL-17⁺ T-bet⁺ CD4⁺ TCM) (P ≤ 0.05) subsets. However, a Treg-like subset (IL-2⁺ HLA-DR⁺ FoxP3⁺ CD4⁺ TEM) (P ≤ 0.001) and a Th2-like (IL-10⁺ Gata3⁺ CD4⁺ TCM) (P ≤ 0.01) subset were decreased in RA. Additionally, when studying response to treatment we observed an increase in circulating CD56^dimCD16⁺ NK cells in these RA patients that is associated with EULAR response (p< 0.01). Validation of these data in a larger sample size of 400 RA patients and 100 DFC is ongoing.

Conclusion: This study shows that aside from Treg subset, which is decreased in RA patients, conventionally defined T cell subsets are unchanged in RA vs DFC when applying traditional gating strategies. The newly defined subsets require further investigation to determine whether they could be used to guide diagnosis or therapy with biologic treatment. Ultimately, the results from this study could potentially allow clinicians to guide decisions for personalised medicine based upon the result of a blood test.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/identification-of-immune-cell-types-in-the-peripheral-blood-of-rheumatoid-arthritis-patients-associated-with-susceptibility-and-response-to-treatment/