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Introduction: Rheumatoid arthritis (RA) is a complex and clinically heterogeneous autoimmune disease. Currently, the relationship between pathogenic molecular drivers of disease in RA and therapeutic response is poorly understood., Methods: We analyzed synovial tissue samples from two RA cohorts of 49 and 20 patients using a combination of global gene expression, histologic and cellular analyses, and analysis of gene expression data from two further publicly available RA cohorts. To identify candidate serum biomarkers that correspond to differential synovial biology and clinical response to targeted therapies, we performed pre-treatment biomarker analysis compared with therapeutic outcome at week 24 in serum samples from 198 patients from the ADACTA (ADalimumab ACTemrA) phase 4 trial of tocilizumab (anti-IL-6R) monotherapy versus adalimumab (anti-TNFα) monotherapy., Results: We documented evidence for four major phenotypes of RA synovium - lymphoid, myeloid, low inflammatory, and fibroid - each with distinct underlying gene expression signatures. We observed that baseline synovial myeloid, but not lymphoid, gene signature expression was higher in patients with good compared with poor European league against rheumatism (EULAR) clinical response to anti-TNFα therapy at week 16 (P =0.011). We observed that high baseline serum soluble intercellular adhesion molecule 1 (sICAM1), associated with the myeloid phenotype, and high serum C-X-C motif chemokine 13 (CXCL13), associated with the lymphoid phenotype, had differential relationships with clinical response to anti-TNFα compared with anti-IL6R treatment. sICAM1-high/CXCL13-low patients showed the highest week 24 American College of Rheumatology (ACR) 50 response rate to anti-TNFα treatment as compared with sICAM1-low/CXCL13-high patients (42% versus 13%, respectively, P =0.05) while anti-IL-6R patients showed the opposite relationship with these biomarker subgroups (ACR50 20% versus 69%, P =0.004)., Conclusions: These data demonstrate that underlying molecular and cellular heterogeneity in RA impacts clinical outcome to therapies targeting different biological pathways, with patients with the myeloid phenotype exhibiting the most robust response to anti-TNFα. These data suggest a path to identify and validate serum biomarkers that predict response to targeted therapies in rheumatoid arthritis and possibly other autoimmune diseases., Trial Registration: ClinicalTrials.gov NCT01119859

Objective: Personalised healthcare is contingent on the identification of biomarkers that represent disease relevant pathways and predict drug response. The authors aimed to develop a gene expression signature in synovial tissue that could enrich clinical response of rheumatoid arthritis (RA) patients to rituximab., Methods: The authors studied synovial gene expression using high-throughput quantitative real-time-PCR in 20 RA patients who underwent arthroscopy before and after treatment with rituximab. Several objective approaches were used to explore patterns in the data and to find genes associated with changes in disease activity due to treatment., Results: This analysis revealed two patient populations associated with distinct clinical, laboratory and histological features and, importantly, showed enrichment for response (60% non-responders vs 90% responders). A composite baseline gene score (GS) correlated with change in disease activity score (ΔDAS) between baseline and month 3 (r=0.74, p=0.0002), but also with ΔDAS at later time-points (month 9, r=0.54, p=0.016; month 15, r=0.45, p=0.06; month 21, r=0.72, p=0.003). Notably, the GS significantly correlated with baseline erythrocyte sedimentation rate (r=0.69, p=0.0008), but not with other DAS components. The GS genes represented T cell, macrophage, remodelling and interferon-α biology. Responders demonstrated higher expression of macrophage and T cell genes, while non-responders showed higher expression of interferon-α and remodelling genes., Conclusions: This study reveals a baseline synovial GS that correlates with early and late clinical responses to rituximab. The GS biology suggests that T cells and macrophages are important for response to B cell depleting therapy, while expression of remodelling and interferon-α genes correlates with poor response.