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303 A stepwise transcriptomic analysis using gene modules and immune cell signatures to stratify systemic lupus erythematosus patients and identify potential treatment targets
  1. Jozsef Karman1,
  2. Marc C Levesque1 and
  3. Justin Wade Davis2
  1. 1AbbVie, Inc., Cambridge, MA, USA
  2. 2AbbVie, Inc. North Chicago, IL, USA


Background A major challenge in drug development for systemic lupus erythematosus (SLE) is the heterogeneous clinical presentation of SLE patients, which necessitates personalized treatment strategies. We aimed to identify clusters of SLE patients based on molecular transcriptomic signatures associated with clinical phenotypes to help address this challenge.

Methods To address this question, we developed an integrated pipeline that defines subsets of patients based on cell type-specific gene expression in blood. Gene expression profiles from two large independent SLE trials, ILLUMINATE-1 and ILLUMINATE-2, were analyzed to identify SLE patient clusters. We first performed a gene expression correlation network analysis to identify co-expressed gene modules. Then, unsupervised consensus clustering was performed on the modules to identify molecular clusters. We correlated cluster membership with clinical phenotypes and immune cell signatures from high resolution scRNA-seq data. We also determined whether immune cell signatures were stable over time.

Results We identified four molecular clusters of SLE patients. Cluster 1 exhibited high signature scores for T cells, B cells, plasma cells, macrophages, and monocytes. Conversely, Cluster 2 exhibited low signature scores for the aforementioned cells. Cluster 3 had high T and B cell signature scores. Cluster 4 had a high signature score for neutrophils. Clinically, Cluster 3 subjects exhibited the lowest disease severity compared to other clusters. We validated these four molecular clusters in three additional independent SLE cohorts. We identified four molecular clusters of SLE patients that were consistent across five independent genomics datasets totaling 2,100 patients. For individual patients, cluster membership was not necessarily stable over time.

Abstract 303 Figure 1

Overview of analysis

Conclusions We have established methods to address SLE heterogeneity in a data-driven, unbiased manner using transcriptomic data. We have uncovered reproducible patterns in stratifying SLE patients using this method and connected SLE patient subsets to cellular alterations in the blood. Our findings have important implications for personalized treatment of SLE and provide guidance for clinical trials in this highly heterogeneous disease.

Acknowledgments Yingtao Bi, Eric Yang, Jesus Paez-Cortez, Abel Suarez-Fueyo, Rui Wang (AbbVie; for suggestions on data analysis and review of results); Yingchun Liu, Stephen Clarke, Sherry Cao (Former AbbVie; for suggestions on data analysis and review of results).

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