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SLE patients exhibit considerable clinical and molecular heterogeneity. A robust patient stratification approach can help to characterize individual lupus patients more effectively and aid patient care.

We employed gene set variation analysis (GSVA) of informative gene modules and k-means clustering to identify molecular endotypes of SLE patients based on dysregulation of specific biologic pathways and interrogated them for clinical utility. We utilized machine learning (ML) of these molecular profiles to classify individual lupus patients into singular molecular subsets and used logistic regression with ridge penalization to develop a novel, composite metric estimating the severity of disease based on lupus-related immunologic activity. Shapley Additive Explanation (SHAP) was employed to understand the impact of specific molecular features on the patient sub-setting.

Six molecular …