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THU0486 Correlating Whole Genome Expression Profiling with Treatment Response in Rheumatoid Arthritis Patients
  1. S.L. Smith1,
  2. D. Plant2,
  3. S. Eyre1,
  4. K. Hyrich1,
  5. A.W. Morgan3,
  6. A.G. Wilson4,
  7. J. Isaacs5,
  8. A. Barton1,2
  9. on behalf of Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate
  1. 1Centre for Genetics and Genomics, Arthritis Research UK Epidemiology Unit, Centre for Musculoskeletal Research, University of Manchester
  2. 2NIHR Manchester Musculoskeletal BRU, Central Manchester Foundation Trust and University of Manchester, Manchester Academic Health Science Centre, Manchester
  3. 3Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, NIHR Leeds Musculoskeletal Biomedical Research Unit, Leeds
  4. 4Academic Unit of Rheumatology, Department of Infection and Immunity, The University of Sheffield Medical School, Sheffield
  5. 5NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne NHS Foundation Trust and Newcastle University, Newcastle upon Tyne, United Kingdom

Abstract

Background Before the introduction of biologic drugs rheumatoid arthritis (RA) was traditionally treated with synthetic disease modifying anti-rheumatic drugs (DMARDs) whose mode of action is unclear. Biologics on the other hand, target specific components of the immune system and, unlike DMARDs, are capable of slowing disease progression and preventing irreversible joint damage. Although highly effective in the majority of patients, a notable proportion still fail to respond (30-40%). Categorising patients into sub-populations based on response to any given drug is the ultimate aim in stratified medicine. To attain this goal, a suitable biomarker/panel of biomarkers must be identified to successfully stratify treatment regimes. We hypothesise that whole genome expression profiling of whole blood samples from RA patients about to commence treatment with the biologic drug etanercept will yield such candidates.

Objectives To identify biomarkers with sufficient discriminatory power to differentiate between responder (R) and non-responder (NR) RA patients to the biologic drug etanercept.

Methods Sixty patients about to undergo treatment with the biologic etanercept were chosen for analysis from the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate (BRAGGSS) longitudinal cohort. This equated to 29 NR and 31 R; defined using EULAR response criteria at three-month follow-up. Total RNA was extracted from baseline (BL) whole blood samples previously collected into TEMPUS™ and PaxGene® blood tubes upon recruitment to this study. Following biotin labelling and RNA amplification using the Illumina® TotalPrep™ RNA Amplification Kit (Ambion®), cRNA was subsequently hybridised to Illumina® HumanHT-12 v4 Expression BeadChips which target over 47,000 probes. GenomeStudio software summarised the bead level data, followed by quality control and differential expression analysis using the Bioconductor package Limma.

Results Whole genome expression data of whole blood RNA taken from RA patients about to commence treatment with etanercept yielded a number of differentially expressed genes. The gene with the most discriminatory power in this analysis was BTN3A2, with a log2 fold-change of -0.544 in R as compared to NR (p-value 9.42x10–6). This gene is a potentially interesting plausible candidate gene, as the encoded protein is a member of the butyrophilin (BTN) family and the immunoglobulin (Ig) superfamily which acts by inhibiting release of interferon gamma from activated T-cells. Additionally, the gene itself lies within the juxta-telomeric region of the major histocompatability class 1 locus on chromosome 6. It is also interesting to note that a single nucleotide polymorphism in the BTN3A2 gene region has been associated with type 1 diabetes and that BTN genes exhibit a strong domain similarity to the co-stimulatory factors CD80 and CD86 on antigen presenting cells.

Conclusions Preliminary data suggests that whole genome expression profiling of BL whole blood samples could be a potentially useful tool in the identification of biomarkers to discriminate between response phenotypes to the biologic drug etanercept. Ongoing work in planned in a larger study cohort to confirm these findings.

Acknowledgements SLS is a PhD student funded by an investigator-initiated award to AB from Pfizer (award number WS1940162).

Disclosure of Interest : None declared

DOI 10.1136/annrheumdis-2014-eular.4184

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