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05.02 Differentiating patient responses in rheumatoid arthritis – systems analysis of key molecular networks
  1. Kate Killick1,
  2. Walter Kolch1,2,
  3. Des Higgins2,
  4. Trudy McGarry2,3,
  5. Carl Orr3,
  6. Alice M Walsh4,
  7. Sunil Nagpal4,
  8. Ursula Fearon3,
  9. Douglas J Veale5
  1. 1Systems Biology Ireland, Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
  2. 2Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, Ireland
  3. 3Molecular Rheumatology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Dublin, Ireland
  4. 4Immunology, Janssen R&D, Spring House PA, USA
  5. 5Department of Rheumatology, Centre for Arthritis and Rheumatic Diseases, St Vincent’s University Hospital, Dublin Academic Medical Centre, Dublin, Ireland


Background Rheumatoid Arthritis (RA) is a progressive autoimmune disease characterised by synovial hyperplasia resulting in destruction of adjacent articular cartilage and bone. While new therapeutic strategies over the last 15 years represent an advance in treatment, a significant proportion of patients do not respond, have sub-optimal responses or suffer adverse effects. Therefore identification of baseline markers that can predict clinical response along with new therapies will allow for better treatment stratification for RA patients.

Materials and methods Peripheral blood mononuclear cells were obtained from patients with RA at baseline. Patients were followed clinically and categorised into responders (n=10) vs non responders (n=9) based on clinical disease outcome. mRNA was isolated from PBMC and RNAseq performed. Ingenuity® Pathway Analysis (IPA) software was used to examine gene enrichment within differentially expressed genes. Interaction network analysis and identification of key hub nodes was performed using InnateDB and Cytoscape.

Results Initial analysis identified differential expression of 155 genes between responders vs non responders. IPA pathway and Gene Ontology analysis demonstrated these were associated with several relevant pathways eg, antigen presentation, with top GO functions related to autoimmune disease, specifically RA. Differential gene expression was associated with leukocyte trafficking and alterations in extracellular matrix components, which was further confirmed by additional IPA network analysis of the RA dataset, identifying a key Inflammatory response/cell movement network. Potential upstream target regulators of these pathways incude TREM-1, IL-17A and HIF1A, which are known to be significantly involved in regulating synovial proliferation in the RA joint. Finally an interaction network analysis identifed CXCL5, ADAMTSL4, CSTB, PPBP, CBLB, ELANE, PTGDS, CXCL2, SP1, CXCL1 as key hub genes preferentially connected to the 155 differentially expressed genes.

Conclusion These data demonstrate differential gene expression and gene networks in responders vs non responder patients, specifically associated with leukocyte trafficking and synovial extravasation. However, given the small sample size, these results need to be validated in independent cohorts. Understanding differences in responder vs nonresponder patients at baseline represents an opportunity to both better understand the mechanisms of current drugs and investigate new therapeutic targets in those who don’t respond to current treatment.

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