ArticlePLoS computational biology2019
Contrasting the impact of cytotoxic and cytostatic drug therapies on tumour progression.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
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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
19 citing papers in PubMed.
- Systematic modeling of phenotypic drug response profiles in patient-derived organoids.bioRxiv : the preprint server for biology · 2026Article
- Modulation ofInternational journal of molecular sciences · 2026Article
- Novel pyridine-based chalcone analogs and triple negative breast cancer: potential therapy & molecular pathways.Scientific reports · 2026Article
- C-6-Modified 2-F-Fucose Derivatives as Inhibitors of Fucosyltransferases.Chembiochem : a European journal of chemical biology · 2025Article
- A Pharmacologic Approach Against Glioblastoma-A Synergistic Combination of a Quinoxaline-Based and a PI3K/mTOR Dual Inhibitor.International journal of molecular sciences · 2025Article
- Molecular Targets of Plant-based Alkaloids and Polyphenolics in Liver and Breast Cancer- An Insight into Anticancer Drug Development.Anti-cancer agents in medicinal chemistry · 2025Review
- Repurposing Osimertinib and Gedatolisib for Glioblastoma Treatment: Evidence of Synergistic Effects in an In Vitro Phenotypic Study.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Transcriptomic analysis revealsOncology letters · 2024Article
- 2,2-Difluoro Derivatives of Fucose Can Inhibit Cell Surface Fucosylation without Causing Slow Transfer to Acceptors.JACS Au · 2024Article
- Enhanced anti-angiogenic effects of aprepitant-loaded nanoparticles in human umbilical vein endothelial cells.Scientific reports · 2024Article
- Perspective on quantitative phase imaging to improve precision cancer medicine.Journal of biomedical optics · 2024Article
- Cotargeting Phosphoinositide 3-Kinase and Focal Adhesion Kinase Pathways Inhibits Proliferation of NF2 Schwannoma Cells.Molecular cancer therapeutics · 2023Article
- Targeting epigenetic features in clear cell sarcomas based on patient-derived cell lines.Journal of translational medicine · 2023Article
- Article
- Ultrastructural and Morphological Effects in T-Lymphoblastic Leukemia CEM-SS Cells Following Treatment with Nordamnacanthal and Damnacanthal from Roots ofMolecules (Basel, Switzerland) · 2022Article
- Article
- Drug-induced resistance evolution necessitates less aggressive treatment.PLoS computational biology · 2021Article
- Rapamycin Plus Doxycycline Combination Affects Growth Arrest and Selective Autophagy-Dependent Cell Death in Breast Cancer Cells.International journal of molecular sciences · 2021Article
- How Macrophages Become Transcriptionally Dysregulated: A Hidden Impact of Antitumor Therapy.International journal of molecular sciences · 2021Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
A tumour grows when the total division (birth) rate of its cells exceeds their total mortality (death) rate. The capability for uncontrolled growth within the host tissue is acquired via the accumulation of driver mutations which enable the tumour to progress through various hallmarks of cancer. We present a mathematical model of the penultimate stage in such a progression. We assume the tumour has reached the limit of its present growth potential due to cell competition that either results in total birth rate reduction or death rate increase. The tumour can then progress to the final stage by either seeding a metastasis or acquiring a driver mutation. We influence the ensuing evolutionary dynamics by cytotoxic (increasing death rate) or cytostatic (decreasing birth rate) therapy while keeping the effect of the therapy on net growth reduction constant. Comparing the treatments head to head we derive conditions for choosing optimal therapy. We quantify how the choice and the related gain of optimal therapy depends on driver mutation, metastasis, intrinsic cell birth and death rates, and the details of cell competition. We show that detailed understanding of the cell population dynamics could be exploited in choosing the right mode of treatment with substantial therapy gains.
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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.