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211 Identifying lupus patient subsets and specific pharmacodynamic changes through immune cell deconvolution of gene expression data in atacicept-treated patients in the APRIL-SLE study
  1. Eileen T Samy1,
  2. Matthew Studham1,
  3. Amy H Kao1,
  4. Philipp Haselmayer2,
  5. Peter Chang1,
  6. Alex Rolfe1,
  7. David Wofsy3,
  8. Julie DeMartino1 and
  9. Robert M Townsend1
  1. 1EMD Serono Research & Development Institute, Inc. (a business of Merck KGaA, Darmstadt, Germany), Billerica, MA, USA
  2. 2Merck KGaA, Darmstadt, Germany
  3. 3Russell/Engleman Rheumatology Research Center, University of California, San Francisco, CA, USA


Background The Phase II/III APRIL-SLE study evaluated the safety and efficacy of atacicept in systemic lupus erythematosus (SLE). The goal of this post-hoc analysis was to use cell-based gene signatures on the APRIL-SLE gene expression data to identify clusters of patients with potential to flare and to assess for difference in treatment effect of atacicept vs placebo.

Methods A published immune cell deconvolution algorithm was applied to whole-blood gene expression data from APRIL-SLE to identify relative proportions of 17 immune cell types. Patients were then grouped into clusters based on these immune cell profiles using a k-medoid clustering algorithm, and were compared to each other based on patient characteristics, biomarkers and clinical efficacy. In addition, the baseline expression and change in expression of putative APRIL-responder genes were compared among the clusters. APRIL-responder genes were identified by combining differential expression results from the APRIL-SLE study (Week 52 vs. Day 1 randomization) and tabalumab Phase III studies (Week 52 vs. Baseline; GSE88887).

Results Patient gene expression data (N=105; Placebo: N=30; atacicept 75 mg: N=40; atacicept 150 mg N=35) was used to group patients into 5 main clusters (P1-P5) by predominant characteristic cells: P1, T helper cells; P2, plasma cells; P3, neutrophils and B cells; P4, B cells; P5, activated dendritic cells. Patients in P2 and P5 were more likely to have positive anti-dsDNA antibodies (≥30 IU/ml) and elevated BLyS (≥1.6 ng/ml), as well as high IFN gene signature in the blood. Patients in P2 were more likely to have low complement C3 and C4 levels. In P2, P4, and P5 clusters the flare rate in the placebo group was significantly higher than in P1 and P3. In P2 and P4, atacicept 150mg treatment group showed delayed time to flare and reduced flare rate as compared with the placebo group. A comparison of differentially-expressed genes from clinical studies of SLE patients on atacicept (targets BLyS & APRIL) vs tabalumab (targets BLyS) revealed possible APRIL-responder genes: SDC1, PARM1 and MZB1. These genes have a higher baseline expression in the P2 and P4 compared to other clusters. SDC1 was reduced more in P2, P4, and P5 after atacicept treatment, while PARM1 and MZB1 decreased after atacicept treatment in P2 and P4.

Conclusions These post-hoc analyses revealed different subsets of SLE patients based on their molecular profiles, which identified patient subsets that might have differential treatment effect of atacicept vs placebo, and provided insights into potential mechanisms of flare.

Funding Source(s): Merck KGaA, Darmstadt, Germany

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