Background/Purpose: Rheumatoid arthritis (RA) is characterized by high clinical variability and an underlying cellular and molecular heterogeneity. Efforts to find tools for the classification of disease phenotypes and patient stratification are key to develop tailored therapies and improve RA management. According to this, specific pathological phenotypes of synovial tissue (pathotypes) have arisen as possibly associated to diverse clinical evolution and response to therapy (Humby et al., 2017). In the present work, we aimed to identify signatures of circulating proteins that correlate with the synovial pathotype in patients with RA.

Methods: A proteomic analysis was carried out on samples from patients enrolled in the Pathobiology of Early Arthritis Cohort (PEAC). Ultrasound-guided synovial biopsies allowed their classification into three groups according to the phenotype of the synovial tissue(lymphoid, myeloid or fibroid), as described previously (Pitzalis et al., 2013). The study was performed using 54 serum samples at baseline: 22 of lymphoid phenotype (L), 18 myeloid (M), and 14 fibroid (F). Sera were depleted from the most abundant proteins by affinity chromatography to remove background, and analysed by reversed-phase nanoliquid chromatography coupled to mass spectrometry using a SWATH strategy and a tripleTOF MS (Sciex). The quantitative data were processed using the ProteinPilot 5.0.1 and PeakView 2.1 software (Sciex). A two-stage support vector machine (TSSVM) with RBF kernel and 10 cross-fold validation was applied using the Classyfire, e1071 and caret R packages.

Results: The proteomic analysis led to the identification and quantification of 229 proteins. The screening analysis was performed on the data obtained from a first group of 30 samples (Train set: 10 L, 10 M and 10 F). Data were pre-processed by PCA for dimension reduction. Then, application of machine learning tools led to the identification of a panel of 11 proteins whose different abundance is associated with a specific phenotype of the synovial tissue (either Lymphoid, Myeloid or Fibroid) in rheumatoid arthritis patients. Table 1 shows the most relevant metrics obtained with this analysis. A very high accuracy and Kappa coefficient were achieved using this classification tool. The results were then confirmed on an independent validation set of 24 samples (12 L, 8 M and 4 F) with also good performance. This protein signature allowed the correct classification of the samples into the three pathotypes with very high sensitivity and specificity.

Table 1. Metrics of the performance of the 11-protein panel to classify the synovial pathotype of the patient. Cut-off for significance was p-value < 0.05.

Conclusion: We have identified a signature of 11 circulating proteins associated with synovial pathotypes in RA patients. The putative correlation of this protein profile with the clinical evolution and/or response to therapy of the patients remains to be elucidated.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/identification-of-a-panel-of-circulating-proteins-associated-to-synovial-pathotypes-in-early-rheumatoid-arthritis-patients/