ArticleGenome medicine2021
Predicting heterogeneity in clone-specific therapeutic vulnerabilities using single-cell transcriptomic signatures.
Article in Genome medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed, 62 citations in OpenAlex.
- A systematic single-cell analysis of enhancer RNAs across human and mouse.Nucleic acids research · 2026Article
- scGPA: an LLM-assisted workflow for directional virtual gene perturbation analysis from single-cell transcriptomes.BMC genomics · 2026Article
- Interpretable graph deep learning framework for drug synergy prediction by integrating functional and clinical similarities.NPJ digital medicine · 2026Article
- Decoding drug-responsive cell subpopulations in triple-negative breast cancer using single-cell multiomics.iScience · 2026Article
- Single-Cell Computational Frameworks for Quantifying BET Bromodomain Inhibitor Resistance and Screening Re-Sensitizer Drugs in Triple-Negative Breast Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning.Communications biology · 2026Article
- Recombinant α-Toxin BmK-M9 Inhibits Breast Cancer Progression by Regulating β-Catenin In Vivo.Cell biochemistry and biophysics · 2025Article
- Personalizing chemotherapy drug selection using a novel transcriptomic chemogram.PLoS computational biology · 2025Article
- Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.Scientific reports · 2025Article
- scATD: a high-throughput and interpretable framework for single-cell cancer drug resistance prediction and biomarker identification.Briefings in bioinformatics · 2025Article
- A drug response prediction method for single-cell tumors combining attention networks and transfer learning.Frontiers in medicine · 2025Article
- Machine learning to dissect perturbations in complex cellular systems.Computational and structural biotechnology journal · 2025Review
- scPharm: Identifying Pharmacological Subpopulations of Single Cells for Precision Medicine in Cancers.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A mini-review on perturbation modelling across single-cell omic modalities.Computational and structural biotechnology journal · 2024Review
- Longitudinal multiomics analysis of aggressive pituitary neuroendocrine tumors: comparing primary and recurrent tumors from the same patient, reveals genomic stability and heterogeneous transcriptomic profiles with alterations in metabolic pathways.Acta neuropathologica communications · 2024Article
- iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data.BMC bioinformatics · 2024Article
- Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors.Cancer research · 2024Article
- PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors.Nature cancer · 2024Article
- Multi-omics and artificial intelligence predict clinical outcomes of immunotherapy in non-small cell lung cancer patients.Clinical and experimental medicine · 2024Review
- scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.Genome medicine · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc's monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc .
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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.