ArticleBulletin of mathematical biology2014
A multicompartment mathematical model of cancer stem cell-driven tumor growth dynamics.
Article in Bulletin of mathematical biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Stochastic journeys of cell progenies through compartments and the role of self-renewal, symmetric and asymmetric division.Scientific reports · 2024Article
- Article
- Growth dynamics of breast cancer stem cells: effects of self-feedback and EMT mechanisms.Theory in biosciences = Theorie in den Biowissenschaften · 2022Article
- Effect of feedback regulation on stem cell fractions in tissues and tumors: Understanding chemoresistance in cancer.Journal of theoretical biology · 2021Article
- Article
- Article
- Modeling and Analyzing Stem-Cell Therapy toward Cancer: Evolutionary Game Theory Perspective.Iranian journal of public health · 2020Article
- Engineered 3D Model of Cancer Stem Cell Enrichment and Chemoresistance.Neoplasia (New York, N.Y.) · 2019Article
- The invasion of de-differentiating cancer cells into hierarchical tissues.PLoS computational biology · 2019Article
- Estimating the number of genetic mutations (hits) required for carcinogenesis based on the distribution of somatic mutations.PLoS computational biology · 2019Article
- Heritable tumor cell division rate heterogeneity induces clonal dominance.PLoS computational biology · 2018Article
- Revealing age-related changes of adult hippocampal neurogenesis using mathematical models.Development (Cambridge, England) · 2018Article
- Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response.Methods in molecular biology (Clifton, N.J.) · 2018Review
- A mathematical model for IL-6-mediated, stem cell driven tumor growth and targeted treatment.PLoS computational biology · 2018Article
- Somatic mutation driven codon transition bias in human cancer.Scientific reports · 2017Article
- A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition.Journal of the Royal Society, Interface · 2017Article
- Quantifying replicative senescence as a tumor suppressor pathway and a target for cancer therapy.Scientific reports · 2015Article
- The mathematics of cancer: integrating quantitative models.Nature reviews. Cancer · 2015Review
- An agent-based model of cancer stem cell initiated avascular tumour growth and metastasis: the effect of seeding frequency and location.Journal of the Royal Society, Interface · 2014Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Tumors are appreciated to be an intrinsically heterogeneous population of cells with varying proliferation capacities and tumorigenic potentials. As a central tenet of the so-called cancer stem cell hypothesis, most cancer cells have only a limited lifespan, and thus cannot initiate or reinitiate tumors. Longevity and clonogenicity are properties unique to the subpopulation of cancer stem cells. To understand the implications of the population structure suggested by this hypothesis--a hierarchy consisting of cancer stem cells and progeny non-stem cancer cells which experience a reduction in their remaining proliferation capacity per division--we set out to develop a mathematical model for the development of the aggregate population. We show that overall tumor progression rate during the exponential growth phase is identical to the growth rate of the cancer stem cell compartment. Tumors with identical stem cell proportions, however, can have different growth rates, dependent on the proliferation kinetics of all participating cell populations. Analysis of the model revealed that the proliferation potential of non-stem cancer cells is likely to be small to reproduce biologic observations. Furthermore, a single compartment of non-stem cancer cell population may adequately represent population growth dynamics only when the compartment proliferation rate is scaled with the generational hierarchy depth.
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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.