0534: Synovial Transcriptomic Profiles Correlate with Disease Activity in Early Untreated Rheumatoid Arthritis

Clément Triaille¹, Tatiana Sokolova¹, Stéphanie de Montjoye¹, Adrien NZEUSSEU TOUKAP², Laurent Meric de Bellefon¹, Axelle Loriot³, pierre coulie⁴, Bernard Lauwerys¹, Patrick Durez⁵ and Nisha Limaye⁶, ¹Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium, ²Cliniques Universitaires Saint-Luc, Brussels, Belgium, ³Computational Biology, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium, ⁴de Duve Institute, Université Catholique de Louvain, Brussels, Belgium, ⁵Institute de Recherche Expérimentale et Clinique (IREC), Cliniques Universitaires Saint-Luc - Université Catholique de Louvain (UCL), Brussels, Belgium, ⁶Genetics of Autoimmune Diseases and Cancer, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium

Background/Purpose: Synovitis is the common feature across all individuals with a diagnosis of rheumatoid arthritis (RA). Yet, cellular and transcriptomic alterations occuring in RA synovium are highly variable amongst patients. So far, most data on clinical-tissue correlations either rely on hypothesis-driven approaches or are potentially biased by heterogeneous clinical characteristics.

We used transcriptomic profiling of synovial tissue from early, untreated RA patients (ERA) to identify the genes with the most variable expression and to explore the ability of unbiased approaches to define clinically relevant ERA subgroups.

Methods: Synovial biopsies were harvested from clinically involved joints of ERA patients using needle arthroscopy or ultrasound-guided biopsy. Data on disease activity were collected at inclusion. For each sample, 350ng total RNA was sent for RNAsequencing using a standardized protocol (Macrogen Europe). After quality control and genome alignement (HiSat2), normalized read counts were analyzed on Qlucore Omics Explorer. To focus on inter-sample heterogeneity, genes were filtered based on variance (σ/σ_max). Unbiased approaches were applied to define patients' clusters. Pathway enrichment analysis were performed on Metascape. CibersortX was used to extrapolate the immune cell subsets relative composition from gene expression data.

Results: Total RNA was obtained from synovial biopsies from 74 patients. We first applied variance filtering to identify the genes whose expression showed the greatest variation between patients (n = 894 most variable genes). PCA analysis on the level of expression of these genes did not divide samples into distinct groups, instead yielding a continuous distribution broadly associated with baseline disease activity, as measured by DAS28CRP. Consequently, we used unsupervised clustering to allow for unbiased definition of two patient clusters (PtC): PtC1 (n=52) and PtC2 (n=22) based on their expression of these 894 genes. Pathway analysis of these genes revealed significant enrichment of immune system genes, in the Inflammatory response and Rheumatoid Arthritis pathways (gene cluster 1: GC1), B cell & plasma cell-related pathways (GC2) and metabolic processes-related genes (GC3). Interestingly, PtC1 and PtC2 were characterized by very different clinical features. More specifically, patients from the group with a strong B & plasma cell signature (PtC1) displayed higher baseline indices of all disease activity score components (DAS28CRP: 5.56 vs 4.09; p = 0.0003). They also had higher rates of baseline radiological erosions (34.6 % vs 10%; p = 0.0252) but similar rates of seropositive disease. In line with our pathway analyses, we found a higher signature (inferred relative frequency) of B & plasma cells, T cells and M1-like macrophages in PtC1 compared to PtC2 synovia. PtC2 synovia instead had relatively higher M2-like macrophage and resting mast cell signatures.

Conclusion: In this large study, we found that synovial transcriptomic profiles in ERA patients distribute continuously based on the expression of inflammatory and immune cell transcriptomic pathways. These synovial transcriptomic signatures correlate strongly with systemic disease activity.

Disclosures: C. Triaille, None; T. Sokolova, None; S. de Montjoye, None; A. NZEUSSEU TOUKAP, None; L. Meric de Bellefon, None; A. Loriot, None; p. coulie, None; B. Lauwerys, UCB; P. Durez, AbbVie, Galapagos, Lilly; N. Limaye, UCB.