Stratification of Systemic Lupus Erythematosus Patients Into Three Groups of Disease Activity Progression According to Longitudinal Gene Expression

Abstract

Objective: The highly heterogeneous clinical presentation of systemic lupus erythematosus (SLE) is characterized by the unpredictable occurrence of disease flares and organ damage. Attempts to stratify lupus patients have been limited to classification based on clinical information, leading to unsuccessful clinical trials and controversial research results. This study was undertaken to develop and validate a robust method to stratify patients with lupus according to longitudinal disease activity and whole-genome gene expression data in order to establish subgroups of patients who share disease progression mechanisms.

Methods: We used a cluster-based approach to stratify SLE patients based on the correlation between disease activity scores and longitudinal gene expression information. Clustering robustness was evaluated by the bootstrap method, and the clusters were characterized in terms of clinical and functional features.

Results: We observed a clear partition into 3 different disease clusters in 2 independent sets of patients, one pediatric and one adult, which was not influenced by treatment, race, or other source of bias. Two of the clusters differentiated into a group showing a correlation between the percentage of neutrophils and disease activity progression and a group showing a correlation between the percentage of lymphocytes and disease activity progression. The third cluster, in which the percentage of neutrophils correlated to a lesser degree with disease activity, was functionally more heterogeneous. Patients in the neutrophil-driven clusters had an increased risk of developing proliferative nephritis.

Conclusion: Our findings indicate that SLE patients can be stratified into 3 subgroups of patients who show different mechanisms of disease progression and are clinically differentiated. Our results have important implications for treatment options, the design of clinical trials, our understanding of the etiology of the disease, and the prediction of severe glomerulonephritis.