ArticleBMC medicine2023
Predicting drug response from single-cell expression profiles of tumours.
Article in BMC medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers.
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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
26 citing papers in PubMed, 32 citations in OpenAlex.
- DISCERN: inferring drug sensitivity from single-cell transcriptomes using cell-type-specific genetic interaction networks.Genome medicine · 2026Article
- FourierDrug: a domain generalization framework for robust drug response prediction via frequency-space asymmetric attention.Bioinformatics (Oxford, England) · 2026Article
- scRADAR: Dissecting intratumoral drug response heterogeneity at single-cell resolution via mechanism-guided prototype routing.PLoS computational biology · 2026Article
- Attention-Guided Multi-View Contrastive Learning for Predicting Sparse Drug-Gene Associations.Interdisciplinary sciences, computational life sciences · 2026Article
- Genetic barcoding uncovers the clonal makeup of solid and liquid biopsies and their ability to capture intra-tumoral heterogeneity.Molecular systems biology · 2026Article
- Analysis of the transcriptomic and metabolomic landscape of prostate cancer with different anatomical origins using snFLARE-seq and mxFRIZNGRND.Nature communications · 2026Article
- scDrugAtlas: an integrative single-cell drug response database for dissecting tumour heterogeneity in therapeutic efficacy.Database : the journal of biological databases and curation · 2026Article
- scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning.Communications biology · 2026Article
- Identification of Drug-resistant Cell Subpopulations in Colorectal Cancer Through Single-cell Analysis and Exploration of Potential Therapeutic Strategies.Current medicinal chemistry · 2026Article
- Challenges and opportunities for oncology drug repurposing informed by synthetic lethality.NPJ systems biology and applications · 2025Article
- scDrugMap: benchmarking large foundation models for drug response prediction.Nature communications · 2025Article
- Harnessing Single-Cell RNA-Seq for Computational Drug Repurposing in Cancer Immunotherapy.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Clinical application of single-cell RNA sequencing in disease and therapy.Clinical and translational medicine · 2025Review
- Multi-omics analysis unveils a four-gene prognostic signature in esophageal squamous carcinoma and the therapeutic potential of PKP1.BMC cancer · 2025Article
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- Identification of drug-resistant individual cells within tumors by semi-supervised transfer learning from bulk to single-cell transcriptome.Communications biology · 2025Article
- Digital twins as global learning health and disease models for preventive and personalized medicine.Genome medicine · 2025Review
- Machine learning to dissect perturbations in complex cellular systems.Computational and structural biotechnology journal · 2025Review
- Malignant epithelial cell marker-driven risk signature enables precise stratification in esophageal cancer.Frontiers in immunology · 2025Article
- Cancer genomics and bioinformatics in Latin American countries: applications, challenges, and perspectives.Frontiers in oncology · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundIntra-tumour heterogeneity (ITH) presents a significant obstacle in formulating effective treatment strategies in clinical practice. Single-cell RNA sequencing (scRNA-seq) has evolved as a powerful instrument for probing ITH at the transcriptional level, offering an unparalleled opportunity for therapeutic intervention.
resultsDrug response prediction at the single-cell level is an emerging field of research that aims to improve the efficacy and precision of cancer treatments. Here, we introduce DREEP (Drug Response Estimation from single-cell Expression Profiles), a computational method that leverages publicly available pharmacogenomic screens from GDSC2, CTRP2, and PRISM and functional enrichment analysis to predict single-cell drug sensitivity from transcriptomic data. We validated DREEP extensively in vitro using several independent single-cell datasets with over 200 cancer cell lines and showed its accuracy and robustness. Additionally, we also applied DREEP to molecularly barcoded breast cancer cells and identified drugs that can selectively target specific cell populations.
conclusionsDREEP provides an in silico framework to prioritize drugs from single-cell transcriptional profiles of tumours and thus helps in designing personalized treatment strategies and accelerating drug repurposing studies. DREEP is available at https://github.com/gambalab/DREEP .
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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.