ArticleBMC cancer2018
Modeling treatment-dependent glioma growth including a dormant tumor cell subpopulation.
Article in BMC cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Cribriform and intraductal carcinoma in prostate cancer - implications for risk stratification and therapeutic decision-making.Nature reviews. Urology · 2026Review
- Understanding and leveraging phenotypic plasticity during metastasis formation.NPJ systems biology and applications · 2023Article
- Agent-based computational modeling of glioblastoma predicts that stromal density is central to oncolytic virus efficacy.iScience · 2022Article
- From cells to tissue: How cell scale heterogeneity impacts glioblastoma growth and treatment response.PLoS computational biology · 2020Article
- Glioblastoma Recurrence and the Role of OJCO clinical cancer informatics · 2019Article
- Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundTumors comprise a variety of specialized cell phenotypes adapted to different ecological niches that massively influence the tumor growth and its response to treatment.
methodsIn the background of glioblastoma multiforme, a highly malignant brain tumor, we consider a rapid proliferating phenotype that appears susceptible to treatment, and a dormant phenotype which lacks this pronounced proliferative ability and is not affected by standard therapeutic strategies. To gain insight in the dynamically changing proportions of different tumor cell phenotypes under different treatment conditions, we develop a mathematical model and underline our assumptions with experimental data.
resultsWe show that both cell phenotypes contribute to the distinct composition of the tumor, especially in cycling low and high dose treatment, and therefore may influence the tumor growth in a phenotype specific way.
conclusionOur model of the dynamic proportions of dormant and rapidly growing glioblastoma cells in different therapy settings suggests that phenotypically different cells should be considered to plan dose and duration of treatment schedules.
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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.