University of Sussex
s41591-022-01789-0.pdf (15.04 MB)

Rituximab versus tocilizumab in rheumatoid arthritis: synovial biopsy-based biomarker analysis of the phase 4 R4RA randomized trial

Download (15.04 MB)
journal contribution
posted on 2023-06-10, 06:04 authored by Felice Rivellese, Anna E A Surace, Katriona Goldmann, Elisabetta Sciacca, Cankut Çubuk, Giovanni Giorli, Christopher R John, Alessandra Nerviani, Liliane Fossati-Jimack, Georgina Thorborn, Manzoor Ahmed, Edoardo Prediletto, Sarah E Church, Briana M Hudson, Charlie ThompsonCharlie Thompson, others
Patients with rheumatoid arthritis (RA) receive highly targeted biologic therapies without previous knowledge of target expression levels in the diseased tissue. Approximately 40% of patients do not respond to individual biologic therapies and 5–20% are refractory to all. In a biopsy-based, precision-medicine, randomized clinical trial in RA (R4RA; n = 164), patients with low/absent synovial B cell molecular signature had a lower response to rituximab (anti-CD20 monoclonal antibody) compared with that to tocilizumab (anti-IL6R monoclonal antibody) although the exact mechanisms of response/nonresponse remain to be established. Here, in-depth histological/molecular analyses of R4RA synovial biopsies identify humoral immune response gene signatures associated with response to rituximab and tocilizumab, and a stromal/fibroblast signature in patients refractory to all medications. Post-treatment changes in synovial gene expression and cell infiltration highlighted divergent effects of rituximab and tocilizumab relating to differing response/nonresponse mechanisms. Using ten-by-tenfold nested cross-validation, we developed machine learning algorithms predictive of response to rituximab (area under the curve (AUC) = 0.74), tocilizumab (AUC = 0.68) and, notably, multidrug resistance (AUC = 0.69). This study supports the notion that disease endotypes, driven by diverse molecular pathology pathways in the diseased tissue, determine diverse clinical and treatment–response phenotypes. It also highlights the importance of integration of molecular pathology signatures into clinical algorithms to optimize the future use of existing medications and inform the development of new drugs for refractory patients.


Publication status

  • Published

File Version

  • Published version


Nature Medicine




Springer Science and Business Media LLC



Page range


Event location

United States

Department affiliated with

  • Clinical and Experimental Medicine Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date


First Open Access (FOA) Date


First Compliant Deposit (FCD) Date


Usage metrics

    University of Sussex (Publications)


    No categories selected