ArticleScientific reports2019
Differential Treatments Based on Drug-induced Gene Expression Signatures and Longitudinal Systemic Lupus Erythematosus Stratification.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed, 38 citations in OpenAlex.
- Systematic dysregulation of immune-related alternative polyadenylation in systemic lupus erythematosus contributes to patient stratification.Journal of translational autoimmunity · 2026Article
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- Single-cell Unperturbed Activation Profiling by mass cytometry capturesFrontiers in immunology · 2026Article
- Systemic lupus erythematosus genetics: insights into pathogenesis and implications for therapy.Nature reviews. Rheumatology · 2024Review
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Emerging biologic frontiers for Sjogren's syndrome: Unveiling novel approaches with emphasis on extra glandular pathology.Frontiers in pharmacology · 2024Review
- Precision medicine in systemic lupus erythematosus.Nature reviews. Rheumatology · 2023Review
- Restoration of aberrant gene expression of monocytes in systemic lupus erythematosus via a combined transcriptome-reversal and network-based drug repurposing strategy.BMC genomics · 2023Article
- Serum profiling identifies CCL8, CXCL13, and IL-1RA as markers of active disease in patients with systemic lupus erythematosus.Frontiers in immunology · 2023Article
- Making inroads to precision medicine for the treatment of autoimmune diseases: Harnessing genomic studies to better diagnose and treat complex disorders.Cambridge prisms. Precision medicine · 2023Review
- Tailored treatment strategies and future directions in systemic lupus erythematosus.Rheumatology international · 2022Review
- The promise of precision medicine in rheumatology.Nature medicine · 2022Review
- A comprehensive survey on computational learning methods for analysis of gene expression data.Frontiers in molecular biosciences · 2022Review
- Molecular Taxonomy of Systemic Lupus Erythematosus Through Data-Driven Patient Stratification: Molecular Endotypes and Cluster-Tailored Drugs.Frontiers in immunology · 2022Article
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- Mayan alleles of the HLA-DRB1 major histocompatibility complex might contribute to the genetic susceptibility to systemic lupus erythematosus in Mexican patients from Tapachula, Chiapas.Clinical rheumatology · 2021Article
- A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome.Nature communications · 2021Article
- Current Status of the Evaluation and Management of Lupus Patients and Future Prospects.Frontiers in medicine · 2021Review
- Protecting the kidney in systemic lupus erythematosus: from diagnosis to therapy.Nature reviews. Rheumatology · 2020Review
Corrections and comments
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Authors and funding
18 authors at 6 institutions in 6 countries.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.