ArticleProceedings of the National Academy of Sciences of the United States of America2018
DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A sequence knowledge-guided deep learning method for single-cell multi-omics translation.Genome biology · 2026Article
- CyTOF as a suitable tool for stratification and monitoring of cancer patients.Journal of translational medicine · 2025Review
- Fusion of spatiotemporal and network models to prioritize multiscale effects in single-cell perturbations.Briefings in bioinformatics · 2025Article
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Personalized tumor combination therapy optimization using the single-cell transcriptome.Genome medicine · 2023Article
- Review
- Achieving a Deeper Understanding of Drug Metabolism and Responses Using Single-Cell Technologies.Drug metabolism and disposition: the biological fate of chemicals · 2023Review
- IL-1β derived from mixed-polarized macrophages activates fibroblasts and synergistically forms a cancer-promoting microenvironment.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2023Article
- Leveraging laboratory and clinical studies to design effective antibiotic combination therapy.Current opinion in microbiology · 2021Review
- Extending the lymphoblastoid cell line model for drug combination pharmacogenomics.Pharmacogenomics · 2021Review
- Leveraging Single-Cell Approaches in Cancer Precision Medicine.Trends in cancer · 2021Review
- Patient-tailored design for selective co-inhibition of leukemic cell subpopulations.Science advances · 2021Article
- Article
- Progress and applications of mass cytometry in sketching immune landscapes.Clinical and translational medicine · 2020Review
- LEARNING GENERAL TRANSFORMATIONS OF DATA FOR OUT-OF-SAMPLE EXTENSIONS.IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing · 2020Article
- Single-Cell Proteomic Profiling Identifies Combined AXL and JAK1 Inhibition as a Novel Therapeutic Strategy for Lung Cancer.Cancer research · 2020Article
- Charting the Fragmented Landscape of Drug Synergy.Trends in pharmacological sciences · 2020Review
- Single-cell analysis targeting the proteome.Nature reviews. Chemistry · 2020Review
- Machine learning approaches to drug response prediction: challenges and recent progress.NPJ precision oncology · 2020Review
- A comparison framework and guideline of clustering methods for mass cytometry data.Genome biology · 2019Article
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9 authors.
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Abstract
An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple ([Formula: see text]40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples.
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