In 2022, advances in the prediction of the response to treatment in rheumatoid arthritis resulted from gene-expression profiling in synovial biopsy samples, assessment of the expression of interferon-response genes in the blood, and the application of machine learning to patients’ clinical parameters and genetic variance.
Key advances
-
Gene expression in synovial biopsies predicts response to rituximab and tocilizumab and treatment-refractory state in rheumatoid arthritis (RA)2.
-
Expression of interferon-response genes in the blood correlates with responsiveness to mixed DMARDs in early RA3.
-
Machine learning applied to genotype data can be used for prediction of the response to methotrexate in early RA4.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 /Â 30Â days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Nam, J. L. et al. Efficacy of biological disease-modifying antirheumatic drugs: a systematic literature review informing the 2016 update of the EULAR recommendations for the management of rheumatoid arthritis. Ann. Rheum. Dis. 76, 1113–1136 (2017).
Rivellese, F. et al. Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial. Nat. Med. 28, 1256–1268 (2022).
Cooles, F. A. H. et al. Interferon-α-mediated therapeutic resistance in early rheumatoid arthritis implicates epigenetic reprogramming. Ann. Rheum. Dis. 81, 1214–1223 (2022).
Myasoedova, E. et al. Toward individualized prediction of response to methotrexate in early rheumatoid arthritis: a pharmacogenomics-driven machine learning approach. Arthritis Care Res. (Hoboken) 74, 879–888 (2022).
Humby, F. et al. Rituximab versus tocilizumab in anti-TNF inadequate responder patients with rheumatoid arthritis (R4RA): 16-week outcomes of a stratified, biopsy-driven, multicentre, open-label, phase 4 randomised controlled trial. Lancet 397, 305–317 (2021).
Zhang, F. et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat. Immunol. 20, 928–942 (2019).
Lewis, M. J. et al. Molecular portraits of early rheumatoid arthritis identify clinical and treatment response phenotypes. Cell Rep. 28, 2455–2470 (2019).
Humby, F. et al. Synovial cellular and molecular signatures stratify clinical response to csDMARD therapy and predict radiographic progression in early rheumatoid arthritis patients. Ann. Rheum. Dis. 78, 761–772 (2019).
de la Calle-Fabregat, C. et al. The synovial and blood monocyte DNA methylomes mirror prognosis, evolution, and treatment in early arthritis. JCI Insight 7, e158783 (2022).
Pitzalis, C., Choy, E. H. S. & Buch, M. H. Transforming clinical trials in rheumatology: towards patient-centric precision medicine. Nat. Rev. Rheumatol. 16, 590–599 (2020).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors are named inventors on a patent application (no. GB 2100821.4), submitted by Queen Mary University of London, that covers methods used to select treatments in rheumatoid arthritis.
Rights and permissions
About this article
Cite this article
Lewis, M.J., Pitzalis, C. Progress continues in prediction of the response to treatment of RA. Nat Rev Rheumatol 19, 68–69 (2023). https://doi.org/10.1038/s41584-022-00890-5
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41584-022-00890-5