The capacity to use synovial pathobiological markers to refine early clinical classification criteria in rheumatoid arthritis (RA) creates the potential to predict disease outcomes and stratify therapeutic interventions by patient need, according to a study published in Annals of the Rheumatic Diseases.
Although better outcomes have resulted from the early diagnosis and treatment of RA produced by the introduction of American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) RA classification criteria, these criteria have also led to the diagnosis of patients with more heterogeneous and milder forms of the disease. More than ever, it is important to find ways to identify high-risk patients and fast-track more aggressive therapies for those likely to experience accelerated progression of structural damage. The current study was designed to assess whether synovial cellular and molecular signatures improve current clinical classification and prognostic algorithms in patients with early inflammatory arthritis and to identify predictors of appropriate biological therapy.
The study included 200 patients with early inflammatory arthritis (<1 year of symptoms) who were treatment-naïve for conventional synthetic disease-modifying antirheumatic drugs and steroids; patients were recruited at Barts Health NHS Trust as part of the multicenter pathobiology of early arthritis cohort (http://www.peac-mrc.mds.qmul.ac.uk). These patients were classified according to RA1987 ACR or undifferentiated arthritis (UA) criteria. The patients in the UA group were further classified by RA2010 ACR/EULAR criteria for RA, which created 3 study groups: (1) RA1987 (RA1987+/RA2010+), (2) RA2010 (RA1987-/RA2010+), and (3) UA (RA1987-/RA2010-).
Ultrasound-guided, minimally invasive biopsy was used to retrieve synovial tissue, which was processed for immunohistochemical (IHC) and molecular characterization. Samples were analyzed for macrophage, plasma- and B- and T-cell markers, pathotype classification (diffuse-myeloid, lympho-myeloid, or pauci-immune) by IHC, and gene expression profiling by Nanostring.
Among the total 200 patients, 128 (64%) were classified as RA1987, 25 as RA2010 (12.5%), and 47 (23.5%) as UA. Although there was no significant between-group differences in disease duration, mean age, or erythrocyte sedimentation rate, RA1987 patients showed significantly higher DAS28, rheumatoid factor (RF), C reactive protein, anticitrullinated protein antibody (ACPA), swollen joint count, tender joint count, and Visual Analogue Score. Compared with the RA2010 and UA groups, the RA1987 group also had significantly more patients who were seropositive for RF and ACPA.
At 12 months, a significantly higher percentage of RA1987 patients required biologic therapy compared with RA2010 and UA patients (27.82% vs 20.83% vs 10.63%; P <.001), and the lympho-myeloid pathotype (vs diffuse-myeloid or pauci-immune) was significantly associated with a 12-month requirement for biologic therapy (57% vs 21% vs 21%; P =.02) Investigators created a clinical prediction model for biological therapy requirement with a predictive performance evaluated at an area under the curve (AUC) of 0.78 (95% CI, 0.70-0.87). The apparent prediction performance was improved in both the penalized and unpenalized clinical model by the integration of synovial pathobiological markers (apparent AUC, 0.89; 95% CI, 0.83-0.95 and AUC, 0.90; 95% CI, 0.84-0.95).
Study limitations included the failure to examine the disease’s natural history and outcome as well as treatment that followed the UK National Institute for Clinical Excellence guidelines rather than study protocol. However, the study investigators concluded, “The capacity to refine early clinical classification criteria through the application of synovial pathobiological markers and the ability to identify patients who subsequently require biological therapy at disease onset offer the opportunity to stratify therapeutic intervention to the patients most in need. This present study adds weight to the need to change current therapeutic algorithms and start biological therapies at disease onset in patients with poor prognosis. This is likely to have a major impact on disease control/remission and long-term disability, as notionally supported by numerous early intervention studies using biological therapies.”
Lliso-Ribera G, Humby F, Lewis M, et al. Synovial tissue signatures enhance clinical classification and prognostic/treatment response algorithms in early inflammatory arthritis and predict requirement for subsequent biological therapy: results from the pathobiology of early arthritis cohort (PEAC). Ann Rheum Dis. 2019;78(12):1642–1652.