: Despite the success of targeted therapies in rheumatoid arthritis, the lack of predictive biomarkers of response leads to an empirical treatment approach, often delaying effective intervention due to non-response to the initially selected individualized medication regimens in approximately 40% of patients. Cellular and molecular deconvolution of synovial tissue heterogeneity reveals discrete disease phenotypes, enabling disease stratification and response prediction. Recent advances in single-cell RNA sequencing and mass cytometry have defined more refined synovial molecular signatures and cell-type abundance phenotypes, uncovering novel pathogenic cellular subsets and inflammatory crosstalk networks. These insights hold profound implications for treatment response prediction and novel target development. While progress is notable, the field remains in its infancy. The integration of synovial multi-omics with multi-modal arrays of clinical data using artificial intelligence holds promise for developing clinically actionable algorithms. Accordingly, innovative pathology-informed clinical trials are likely to be increasingly adopted, paving the way toward more precise and individualized therapy within a precision medicine framework.

New learnings from the molecular pathology of the synovial tissue in rheumatoid arthritis: from pathogenesis to therapeutic targeting toward precision medicine

Pitzalis, Costantino
2026-01-01

Abstract

: Despite the success of targeted therapies in rheumatoid arthritis, the lack of predictive biomarkers of response leads to an empirical treatment approach, often delaying effective intervention due to non-response to the initially selected individualized medication regimens in approximately 40% of patients. Cellular and molecular deconvolution of synovial tissue heterogeneity reveals discrete disease phenotypes, enabling disease stratification and response prediction. Recent advances in single-cell RNA sequencing and mass cytometry have defined more refined synovial molecular signatures and cell-type abundance phenotypes, uncovering novel pathogenic cellular subsets and inflammatory crosstalk networks. These insights hold profound implications for treatment response prediction and novel target development. While progress is notable, the field remains in its infancy. The integration of synovial multi-omics with multi-modal arrays of clinical data using artificial intelligence holds promise for developing clinically actionable algorithms. Accordingly, innovative pathology-informed clinical trials are likely to be increasingly adopted, paving the way toward more precise and individualized therapy within a precision medicine framework.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11699/106883
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