Differential Treatments Based on Drug-induced Gene Expression Signatures and Longitudinal Systemic Lupus Erythematosus Stratification

Abstract

Systemic lupus erythematosus (SLE) is a heterogeneous disease with unpredictable patterns of activity. Patients with similar activity levels may have different prognosis and molecular abnormalities. In this study, we aimed to measure the main differences in drug-induced gene expression signatures across SLE patients and to evaluate the potential for clinical data to build a machine learning classifier able to predict the SLE subset for individual patients. SLE transcriptomic data from two cohorts were compared with drug-induced gene signatures from the CLUE database to compute a connectivity score that reflects the capability of a drug to revert the patient signatures. Patient stratification based on drug connectivity scores revealed robust clusters of SLE patients identical to the clusters previously obtained through longitudinal gene expression data, implying that differential treatment depends on the cluster to which patients belongs. The best drug candidates found, mTOR inhibitors or those reducing oxidative stress, showed stronger cluster specificity. We report that drug patterns for reverting disease gene expression follow the cell-specificity of the disease clusters. We used 2 cohorts to train and test a logistic regression model that we employed to classify patients from 3 independent cohorts into the SLE subsets and provide a clinically useful model to predict subset assignment and drug efficacy.

A new molecular classification to drive precision treatment strategies in primary Sjögren’s syndrome

“…C4 was also characterized by a massive lymphopenia and high levels of neutrophils. The neutrophil-to-lymphocyte ratio (NLR) has been previously shown to correlate with disease activity in systemic autoimmunity 48 , 49 and elevated NLR are thought to represent a pro-inflammatory state. Indeed, in a study of 483 adult patients with multiple sclerosis, NLR could differentiate between relapsing-remitting and primary progressive multiple sclerosis and predict worsening disability 50 .…”

Molecular Taxonomy of Systemic Lupus Erythematosus Through Data-Driven Patient Stratification: Molecular Endotypes and Cluster-Tailored Drugs

“…Over the last years, in silico drug repositioning studies for SLE have been published, based on gene expression and genetic profiles ( 47 – 49 ). Furthermore, efforts have been made to individualize drug repurposing results, according to the molecular features of lupus patients ( 49 ), whereas several studies have applied literature mining approaches, in order to prioritize the most promising compounds ( 50 , 51 ). Herein, we performed personalized drug repurposing analysis using two robust, high-throughput platforms (iLINCS and CLUE).…”

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus

“…Although machine learning has yet to be applied for matching individual patients with lupus with the most appropriate treatment or treatment dose clinically, advancements in machine learning-derived prediction of flares and subsetting of patients can help in the identification of specific treatments. For example, after a machine learning clustering model identified patient subsets using clinical data, the gene expression profiles of the subsets were input into a program [Connectivity Map Linked User Environment (CLUE)] that identifies drugs that reverse the disease gene expression signature of the subset [25].…”