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1602 Transcriptomic profiles predict response to rituximab in SLE
  1. Lucy M Carter1†,
  2. Adewonuola Alase1†,
  3. Zoe Wigston1,
  4. Antony Psarras1,
  5. Agata Burska1,
  6. Yuzaiful Yusof1,
  7. John A Reynolds2,
  8. Paul Emery1,
  9. Miriam Wittmann1,
  10. Ian N Bruce3 and
  11. Edward Vital1
  1. 1NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds
  2. 2Rheumatology Research Group, Institute of Inflammation and Ageing, University of Birmingham and Sandwell and West Birmingham NHS Trust, Birmingham
  3. 3Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester
  4. 4Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds


Background B cells are a common therapeutic target in SLE but responses are mixed suggesting that some aspects of disease are less B cell-dependent. Transcriptomic analyses have revealed gene-expression profiles that stratify clinical and demographic aspects of lupus. However, these have not been yet linked to response to targeted therapies. Such linkages can elucidate critical pathogenic mediators that differ between transcriptomic subsets.

Methods We developed a 96-gene Taqman assay including scores for: Interferon Score A (M1.2 and M3.4), Interferon Score B (M3.4 and M5.12), neutrophils (M4.9), plasmablasts (M4.11), myeloid (M5.7), inflammation (M4.2) and erythropoiesis (M4.4). Each was the median normalised dCT of transcripts representative of the module. This was assessed in whole blood from 123 active SLE patients starting new immunosuppresion. After exploring baseline associations, we then evaluated clinical response to a first cycle of rituximab 2x1000mg, using a BILAG-based endpoint (As reduce to B or better, ≤1 persistent B at 6 months, no new A/B)

Results Transcriptomic profiles markedly differed between patients with European Ancestry (EA, n=128) compared to African and Asian Ancestries (n=85). EA had significantly lower expression for IFN Score A (p<0.001), IFN Score B (p=0.039), and plasmablasts (p=0.001). No substantive differences were seen in neutrophil (p=0.26), erythropoiesis (p=0.26) and inflammation (p=0.85) scores. In EA, IFN Score B was highly correlated with neutrophil (R=0.554, p<0.001), myeloid (R=0.725, p<0.001), plasmablast (R=0.323, p<0.001), inflammation (R=0.599, p<0.001) and erythropoiesis (R=0.0.376, p<0.001) scores. In NEA, IFN Score B correlated with myeloid (R=0.724, p<0.001) and inflammation (R=0.463, p<0.001) but only weakly with erythropoiesis (R=0.316, p=0.003) and no correlation with neutrophils (R=0.192, p=0.078) or plasmablasts (R=0.023, p=0.832).

Since in NEA these weaker correlations presented a more heterogeneous transcriptomic picture, we further analysed this group using hierarchical clustering of individual transcript expression. This revealed 3 clusters; cluster 1 (low IFN, low plasmablast); cluster 2 (globally high); and cluster 3 (low neutrophil and myeloid, high plasmablast). In the rituximab study these clusters differed in clinical response, which was not explained by other clinical features. Cluster 1 were older with higher glucocorticoid dose and low rituximab response rate. Clusters 2 and 3 were similar in clinical features but rituximab response was significantly higher for cluster 2 (table 1). IFN Score B was the strongest predictor of rituximab response (OR=3.021/unit (95% CI 1.4, 6.6, p=0.006).

Abstract 1602 Table 1

Conclusions NEA SLE patients have more heterogeneous transcriptomic profiles, which predict clinical response to B cell targeted therapy independent of clinical features.

Acknowledgments Rituximab treatment data were provided as part of the MRC funded MASTERPLANS consortium.

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