ArticleScience advances2021
Patient-tailored design for selective co-inhibition of leukemic cell subpopulations.
Article in Science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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.
The trial behind it
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
24 citing papers in PubMed, 49 citations in OpenAlex.
- Discovery of predictive biomarkers for cancer therapy through computational approaches.Nature reviews. Clinical oncology · 2026Review
- Smart Nanoformulations for Oncology: A Review on Overcoming Biological Barriers with Active Targeting, Stimuli-Responsive, and Controlled Release for Effective Drug Delivery.Pharmaceutics · 2026Review
- Knowledgeable Language Models as Black-Box Optimizers for Personalized Medicine.... International Conference on Learning Representations · 2026Article
- A Machine Learning-Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia.Cancer research · 2025Article
- Cardamom synergizes with cisplatin against human osteosarcoma cells by mTOR-mediated autophagy.Cancer gene therapy · 2025Article
- CanSeer: a translational methodology for developing personalized cancer models and therapeutics.Scientific reports · 2025Article
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- Exome-wide association study reveals 7 functional variants associated with ex-vivo drug response in acute myeloid leukemia patients.BMC medical genomics · 2025Article
- Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones.Nature communications · 2024Article
- PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors.Nature cancer · 2024Article
- 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
- Designing patient-oriented combination therapies for acute myeloid leukemia based on efficacy/toxicity integration and bipartite network modeling.Oncogenesis · 2024Article
- Robust scoring of selective drug responses for patient-tailored therapy selection.Nature protocols · 2024Review
- Engineering CRISPR/Cas9 therapeutics for cancer precision medicine.Frontiers in genetics · 2024Review
- Personalized tumor combination therapy optimization using the single-cell transcriptome.Genome medicine · 2023Article
- Computational Advancements in Cancer Combination Therapy Prediction.JCO precision oncology · 2023Article
- How Azanucleosides Affect Myeloid Cell Fate.Cells · 2022Review
- Bipartite network models to design combination therapies in acute myeloid leukaemia.Nature communications · 2022Article
- Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data.Nature communications · 2022Article
- Systematic review of computational methods for drug combination prediction.Computational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The extensive drug resistance requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to selectively target disease-driving cell subpopulations. To solve the combinatorial explosion challenge, we implemented an effective machine learning approach that prioritizes patient-customized drug combinations with a desired synergy-efficacy-toxicity balance by combining single-cell RNA sequencing with ex vivo single-agent testing in scarce patient-derived primary cells. When applied to two diagnostic and two refractory acute myeloid leukemia (AML) patient cases, each with a different genetic background, we accurately predicted patient-specific combinations that not only resulted in synergistic cancer cell co-inhibition but also were capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Our functional precision oncology approach provides an unbiased means for systematic identification of personalized combinatorial regimens that selectively co-inhibit leukemic cells while avoiding inhibition of nonmalignant cells, thereby increasing their likelihood for clinical translation.
Identifiers
What OpenQuestion holds
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.