ArticleCell reports methods2023
Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 11 citations in OpenAlex.
- Evolutionary Antifragile Therapy Is a Treatment Strategy to Suppress Drug Resistance by Exploiting Dose-Response Convexity.Cancer research · 2026Article
- T cell immunotherapy for solid tumors: limitations, progress, and future prospects.Frontiers in immunology · 2026Review
- A statistical framework for detecting therapy-induced resistance from drug screens.NPJ systems biology and applications · 2025Article
- Reducing phenotype-structured partial differential equations models of cancer evolution to systems of ordinary differential equations: a generalised moment dynamics approach.Journal of mathematical biology · 2025Article
- Estimating treatment sensitivity in synthetic and in vitro tumors using a random differential equation model.NPJ systems biology and applications · 2025Article
- Validating the predictions of mathematical models describing tumor growth and treatment response.ArXiv · 2025Article
- Nanomedicines harnessing cGAS-STING pathway: sparking immune revitalization to transform 'cold' tumors into 'hot' tumors.Molecular cancer · 2024Review
- Ultrasensitive Response Explains the Benefit of Combination Chemotherapy Despite Drug Antagonism.Molecular cancer therapeutics · 2024Article
- Second-Order Effects of Chemotherapy Pharmacodynamics and Pharmacokinetics on Tumor Regression and Cachexia.Bulletin of mathematical biology · 2024Article
- Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent.Bulletin of mathematical biology · 2024Article
- Using birth-death processes to infer tumor subpopulation structure from live-cell imaging drug screening data.PLoS computational biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.
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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.