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Sci Rep. 2019 Oct 29;9(1):15502. doi: 10.1038/s41598-019-51616-9.

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

Author information

1
Centro de Genómica e Investigaciones Oncológicas Pfizer-Universidad de Granada-Junta de Andalucía (GENYO), Granada, Spain.
2
Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
3
GLADEL consultant, Rosario, Argentina.
4
Centro Regional de Enfermedades Autoinmunes y Reumáticas, Rosario, Argentina.
5
Centre for Rheumatology, Division of Medicine University College London, London, United Kingdom.
6
Department of Immunology and Rheumatology, Instituto Nacional de Ciencias Médicas y Nutrición "Salvador Zubirán", Mexico City, Mexico.
7
Centro de Genómica e Investigaciones Oncológicas Pfizer-Universidad de Granada-Junta de Andalucía (GENYO), Granada, Spain. pedro.carmona@genyo.es.
8
Centro de Genómica e Investigaciones Oncológicas Pfizer-Universidad de Granada-Junta de Andalucía (GENYO), Granada, Spain. marta.alarcon@genyo.es.
9
Unit of Chronic Inflammation, Institute for Environmental Medicine, Karolinska Institutet, Stockholm, Sweden. marta.alarcon@genyo.es.

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.

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