We sought to refine histologic scoring of rheumatoid arthritis synovial tissue by training with gene expression data and machine learning.

METHODS:
Twenty histologic features were assessed on 129 synovial tissue samples. Consensus clustering was performed on gene expression data from a subset of 45 synovial samples. Support vector machine learning was used to predict gene expression subtypes using histology data as input. Corresponding clinical data were compared across subtypes.

RESULTS:
Consensus clustering of gene expression data revealed three distinct synovial subtypes, including a highly inflammatory subtype characterized by extensive infiltration of leukocytes, a low inflammatory subtype characterized by enrichment in pathways including TGF-β, glycoproteins and neuronal genes, and a mixed subtype. Machine learning applied to histology features using gene expression subtypes as labels generated an algorithm for scoring histology features. Patients with highly inflammatory synovial subtypes exhibited higher levels of markers of systemic inflammation and autoantibodies. CRP was significantly correlated with pain in the high inflammatory group but not the others.

CONCLUSION:
Gene expression analysis of synovial tissue revealed three distinct synovial subtypes. We used these labels to generate a histology scoring algorithm that associates with levels of ESR, CRP and autoantibodies. Comparison of gene expression patterns to clinical features revealed a potentially clinically important distinction: mechanisms of pain may differ in patients with different synovial subtypes. This article is protected by copyright. All rights reserved.