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

Felice Rivellese1,2, Anna E A Surace1,2, Katriona Goldmann1,2

  • 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.

Nature Medicine
|May 19, 2022
PubMed

Insights

Biomolecular signatures in rheumatoid arthritis (RA) tissue predict response to biologic therapies like rituximab and tocilizumab. Machine learning models can identify patients likely to respond or be resistant to treatment, improving precision medicine for RA.

Area of Science:

  • Rheumatology
  • Immunology
  • Precision Medicine
  • Molecular Pathology

Background:

  • Rheumatoid arthritis (RA) patients often receive targeted biologic therapies without prior knowledge of tissue-specific target expression.
  • A significant portion of RA patients (approx. 40%) exhibit insufficient response to individual biologic therapies, with 5-20% being refractory to all available treatments.

Purpose of the Study:

  • To investigate synovial biopsy molecular signatures for predicting treatment response in RA patients undergoing biologic therapy.
  • To identify distinct molecular pathways associated with response or resistance to rituximab and tocilizumab.
  • To develop machine learning algorithms for predicting treatment response and multidrug resistance in RA.

Main Methods:

  • In-depth histological and molecular analyses of synovial biopsies from the R4RA randomized clinical trial (n=164).
  • Identification of gene expression signatures related to humoral immune response and stromal/fibroblast activity.
  • Development and validation of machine learning algorithms using nested cross-validation to predict treatment response.

Main Results:

  • Humoral immune response gene signatures correlated with response to rituximab and tocilizumab.
  • A distinct stromal/fibroblast signature was identified in patients refractory to all medications.
  • Machine learning models achieved significant predictive accuracy for rituximab (AUC=0.74), tocilizumab (AUC=0.68), and multidrug resistance (AUC=0.69).

Conclusions:

  • Disease endotypes, driven by molecular pathology, dictate diverse clinical and treatment-response phenotypes in RA.
  • Integrating molecular pathology signatures into clinical algorithms can optimize current RA treatments and guide the development of novel therapies for refractory cases.

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