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)

Abstract

Objective: To establish whether synovial pathobiology improves current clinical classification and prognostic algorithms in early inflammatory arthritis and identify predictors of subsequent biological therapy requirement.

Methods: 200 treatment-naïve patients with early arthritis were classified as fulfilling RA1987 American College of Rheumatology (ACR) criteria (RA1987) or as undifferentiated arthritis (UA) and patients with UA further classified into those fulfilling RA2010 ACR/European League Against Rheumatism (EULAR) criteria. Treatment requirements at 12 months (Conventional Synthetic Disease Modifying Antirheumatic Drugs (csDMARDs) vs biologics vs no-csDMARDs treatment) were determined. Synovial tissue was retrieved by minimally invasive, ultrasound-guided biopsy and underwent processing for immunohistochemical (IHC) and molecular characterisation. Samples were analysed for macrophage, plasma-cell and B-cells and T-cells markers, pathotype classification (lympho-myeloid, diffuse-myeloid or pauci-immune) by IHC and gene expression profiling by Nanostring.

Results: 128/200 patients were classified as RA1987, 25 as RA2010 and 47 as UA. Patients classified as RA1987 criteria had significantly higher levels of disease activity, histological synovitis, degree of immune cell infiltration and differential upregulation of genes involved in B and T cell activation/function compared with RA2010 or UA, which shared similar clinical and pathobiological features. At 12-month follow-up, a significantly higher proportion of patients classified as lympho-myeloid pathotype required biological therapy. Performance of a clinical prediction model for biological therapy requirement was improved by the integration of synovial pathobiological markers from 78.8% to 89%-90%.

Conclusion: The capacity to refine early clinical classification criteria through synovial pathobiological markers offers the potential to predict disease outcome and stratify therapeutic intervention to patients most in need.

Keywords: Early Rheumatoid Arthritis; Synovitis; Ultrasonography.

Figure 1

Baseline patient demographics. (A) Baseline classification of patients. Two hundred patients were classified into RA1987 versus UA. RA2010 ACR/EULAR criteria were then applied to patients with UA. Final 3 groups obtained showed 47 patients UA (RA1987−/RA2010−), RA2010 (RA1987−/RA2010+), RA1987 (RA1987+/RA2010+). (B) Demographics according to classification criteria. Data are presented as mean (SD) for continue variables and frequency and percentages for categorical variables. Baseline characteristics between the three groups were compared using the Kruskal-Wallis or Fisher’s exact test as appropriate. For post hoc comparison, Dunn tests were run and p value from pairwise comparison reported in the last three columns of the table. ACPA titre, anticitrullinated protein antibody titre (IU/L); ACPA +ve, anticitrullinated protein antibody (>20 IU/L); CRP, C reactive protein; DAS28, Disease Activity Score 28 joints; ESR, erythrocyte sedimentation rate; RF titre, rheumatoid factor titre (IU/mL); RF +ve, rheumatoid factor serum positive (>15 IU/L); 28TJC, 28 tender joint count; 28SJC, 28 swollen joint count.

Figure 2

Patient demographics and disease activity: comparison between pathotypes. (A) Number of biopsy procedures per joint. MCP, metacarpophalangeal; MTP, metatarsophalangeal; PIP, proximal interphalangeal. (B) Representative images of synovial pathotypes. Sections underwent immunohistochemical staining and semiquantitative scoring (0–4) to determine the degree of CD20 +B cells, CD3 +T cells, CD68 +lining) and sublining macrophage and CD138 +plasma cell infiltration. Sections were categorised into three pathotypes: (1) pauci-immune (CD68 SL <2 and or CD3, CD20, CD138 <1), (2) diffuse myeloid: (CD68SL >2, CD20 <1 and or CD3 >1) and (3) lymphomyeloid: (grade 2–3 CD20 +aggregates, CD20 >2). Arrow heads indicate positive stain cells. Empty arrows indicate B cell aggregates. (C) Demographic analysis by pathotype. Data are presented as mean and SD for numerical variables and frequency and percentage for categorical variables. Baseline characteristics between the three pathotypes were compared using a Kruskall-Wallis test and Fisher test (RF and ACPA positivity) as appropriate. Post hoc analysis for significant differences using the Dunn test for multiple comparison. A p value of <0.05 was considered statistically significant. (D) Pathotype according to disease duration (months) at diagnosis. Absolute values (N) and percentage. A p value of <0.05 was considered statistically significant. ACPA titre, anticitrullinated protein antibody titre (IU/L); ACPA +ve, anticitrullinated protein antibody (>20 IU/L); CRP, C reactive protein; DAS28, Disease Activity Score 28 joints; ESR, erythrocyte sedimentation rate; RF titre, rheumatoid factor titre (IU/mL); RF +ve, rheumatoid factor serum positive (>15 IU/L); 28TJC, 28 tender joint count; 28SJC, 28 swollen joint count.

Figure 3

Variation in synovial pathobiology according to clinical classification of patients. (A) Baseline clinical classification compared with pathotype. Baseline subgroups (RA1987, RA2010 and UA) were compared with pathotype. Fisher test used for analysis. (B) Immune cell infiltration for each clinical subgroup. Kruskal-Wallis test for comparison between three groups. Post hoc analysis for significant differences using the Dunn test for multiple comparison. (C) (C–E) Gene expression analysis for comparison between subgroups. t-Test for comparison and volcano plot for representative image. Positive values represent upregulation and negative values downregulation. Green circles above green horizontal line represents non-corrected for multiple analysis expressed genes between groups. Red circles above red line represents corrected p values (Benjamini-Hochberg (BH) method) for multiple analysis. (C) Volcano plot RA1987 versus RA2010: difference in gene expression between patient fulfilling RA 1987 ACR criteria and RA 2010 ACR/EULAR criteria. (D) Volcano plot RA1987 versus UA: difference in gene expression between patient fulfilling RA 1987 ACR criteria and UA. (E) Volcano plot RA 2010 versus UA: differences in gene expression between patient fulfilling RA 2010 ACR/EULAR criteria and UA. RA, rheumatoid arthritis; UA, undifferentiated arthritis.

Figure 4

Disease evolution. (A) Patient classification after 12-month follow-up. Disease outcome after 12 months of follow-up for each of the initial baseline subgroups (RA1987/RA2010/UA). Disease evolution classified as self-limiting or persistent disease. Other diagnosis as described for those who were reclassified after 1 year form UA cohort. (B) Disease evolution by subgroups. Disease evolution was compared with baseline subgroups (RA1987, RA2010 and UA). Fisher test used for analysis. (C) Disease evolution by pathotype. Disease evolution was compared with pathotype (pauci-immune vs diffuse myeloid vs lymphomyeloid). Fisher test used for analysis. A p value of <0.05 was considered statistically significant. RA, rheumatoid arthritis; UA, undifferentiated arthritis.

Figure 5

(A) Comparison between diagnostic subgroups and treatment outcome at 12-month follow-up. Treatment required was divided in three groups: (1) no treatment; (2) csDMARDs only, and (3) csDMARDs ±biologics. Fisher test for analysis. (B) Comparison between pathotype and treatment outcome at 12 months. (C) Gene expression analysis, represented in a volcano plot comparison between patient requiring biologics versus non-biological group. t-Test comparison for gene difference expression between groups. Positive values represent upregulation and negative values downregulation. An adjusted (Benjamini-Hochberg (BH) correction for multiple analysis) p value of <0.01 was considered statistically significant, represented as dots above red line. Green dots above green line for gene expression significance when no correction applied for multiple analysis (p<0.05). (D) Treatment outcome according to baseline disease duration. Fisher test for analysis. (E) Pathotype according to baseline disease duration for biological patient cohort. Fisher test for analysis. A p value of <0.05 was considered statistically significant unless otherwise stated. RA, rheumatoid arthritis; UA, undifferentiated arthritis.

Figure 6

Prediction model. (A) and (B) Identification of clinical and gene expression features predictive of biological therapy use at 1 year. Logistic regression, coupled with backward and stepwise model selection, was applied to baseline clinical parameters against a dependent variable of biological therapy use or not at 12 months to select which clinical covariate contributed the most to the prediction. Selected covariates (119 genes+4 clinical covariates) were entered simultaneously into a logistic model with an L1 regularisation penalty (LASSO) in order to determine the optimal sparse prediction model. A similar predictive performance of the model when clinical was seen when results were penalised (blue-dashed line, A) than when they were not penalised (red-dotted line, A) with a slightly different set of selected covariates (B). (B) Non-zero weights associated with the final variables selected by the LASSO regression. The grey spaces represent the variables that were not selected by the model. (C) and (D) Lambda training curve from the final glmnet fitted model. The red dots represent mean binomial deviance using 10-fold cross validation. The error bars represent SE of binomial deviance. The vertical dotted lines indicate minimum binomial deviance (λ_min) and a more regularised model for which the binomial deviance error is within one SE of the minimum binomial deviance (λ_1se). λ_min was selected, corresponding to 11 non-zero coefficients in the final model for the LASSO where clinical were penalised (C) and 13 non-zero coefficients in the final model for the LASSO where clinical were not penalised (D). AUC, area under the receiver operating characteristic curve; CRP, C reactive protein; DAS28, Disease Activity Score 28 joints; TJC, tender joint count.