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, 9 (1), 15502

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

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

Daniel Toro-Domínguez et al. Sci Rep.

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.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Clustering based on drug connectivity scores. (A) The assignment of each patient to the clusters based on the drug connectivity scores (violet and golden clusters), the assignment of each patient to each of the 3 longitudinal clusters, and the percentage of neutrophils and lymphocytes for each patient. (B) Summary of the comparison done with ANOVA of NLR and of the correlation between NLR and SLEDAI score values of patients from the different SLE subgroups. NLR: Neutrophil to Lymphocyte ratio. dNLR: correlation between NLR and SLEDAI across diferent time points.
Figure 2
Figure 2
Analysis of drugs commonly used in SLE. (A) The connectivity scores of the MOAs on which the drugs commonly used in SLE act, shown for each subgroup and for a gene signature derived from all the patients together. On the left, the drugs are shown for each MOA. (B) The expression of the targets of each drug in the different cell types. The values are obtained by multiplying a binary matrix that contains the information of the targets for each drug and a matrix that contains the expression of each target in the different cell types.
Figure 3
Figure 3
Drug-repurposing on SLE subgroups. Top) Significant drugs (score > = |90|) for at least one cluster. Right) Significant MOAs obtained using matrix multiplication. The green color represents the similarity level between MOAs and drugs obtained by GSEA. Bottom) Red color intensity represents the expression of targets for each drug in the different blood cell types.
Figure 4
Figure 4
Classification model specificity and sensitivity. (A) ROC curve obtained when testing the classification model on the test set. (B) Odds-ratios (OR) and confidence intervals at 95% of suffering from nephritis when assigned to the neutrophil or to the lymphocyte-driven subgroup in each study, and in the meta-analysis.
Figure 5
Figure 5
Summary and flow of the drug connectivity analyses. Based on the connectivity scores obtained in CLUE for each drug within each cluster, we analyzed on the one hand the drugs commonly used in SLE (red branch) and on the other hand, the drugs with score > = 90 in absolute value (blue branch), from which we extracted the significant MOAs by means of multiplication of matrices (MM). Subsequently, by matrix multiplication we obtained the relative expression of the targets of each drug in the blood cell types. The pairs of matrices that are multiplied are linked in the diagram by a shaded circle.

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References

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