ArticlePloS one2015
Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 38 citations in OpenAlex.
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- Predicting anti-cancer drug combination responses with a temporal cell state network model.PLoS computational biology · 2023Article
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- A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks.Patterns (New York, N.Y.) · 2021Review
- Oxidized ATM promotes breast cancer stem cell enrichment through energy metabolism reprogram-mediated acetyl-CoA accumulation.Cell death & disease · 2020Article
- Modeling heterogeneous tumor growth dynamics and cell-cell interactions at single-cell and cell-population resolution.Current opinion in systems biology · 2019Article
- Dynamics of Phenotypic Heterogeneity Associated with EMT and Stemness during Cancer ProgressionJournal of clinical medicine · 2019Review
- AKT2 siRNA delivery with amphiphilic-based polymeric micelles show efficacy against cancer stem cells.Drug delivery · 2018Article
- Extranuclear signaling by sex steroid receptors and clinical implications in breast cancer.Molecular and cellular endocrinology · 2018Review
- Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response.Methods in molecular biology (Clifton, N.J.) · 2018Review
- Viscoelasticity Measurements Reveal Rheological Differences Between Stem-like and Non-stem-like Breast Cancer Cells.Cellular and molecular bioengineering · 2017Article
- Characterization of a Dual Rac/Cdc42 Inhibitor MBQ-167 in Metastatic Cancer.Molecular cancer therapeutics · 2017Article
- Deciphering Epithelial-Mesenchymal Transition Regulatory Networks in Cancer through Computational Approaches.Frontiers in oncology · 2017Review
- Early mutation bursts in colorectal tumors.PloS one · 2017Article
- Oncofetal Epigenetic Bivalency in Breast Cancer Cells: H3K4 and H3K27 Tri-Methylation as a Biomarker for Phenotypic Plasticity.Journal of cellular physiology · 2016Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
Cancer stem cells (CSCs) possess capacity to both self-renew and generate all cells within a tumor, and are thought to drive tumor recurrence. Targeting the stem cell niche to eradicate CSCs represents an important area of therapeutic development. The complex nature of many interacting elements of the stem cell niche, including both intracellular signals and microenvironmental growth factors and cytokines, creates a challenge in choosing which elements to target, alone or in combination. Stochastic stimulation techniques allow for the careful study of complex systems in biology and medicine and are ideal for the investigation of strategies aimed at CSC eradication. We present a mathematical model of the breast cancer stem cell (BCSC) niche to predict population dynamics during carcinogenesis and in response to treatment. Using data from cell line and mouse xenograft experiments, we estimate rates of interconversion between mesenchymal and epithelial states in BCSCs and find that EMT/MET transitions occur frequently. We examine bulk tumor growth dynamics in response to alterations in the rate of symmetric self-renewal of BCSCs and find that small changes in BCSC behavior can give rise to the Gompertzian growth pattern observed in breast tumors. Finally, we examine stochastic reaction kinetic simulations in which elements of the breast cancer stem cell niche are inhibited individually and in combination. We find that slowing self-renewal and disrupting the positive feedback loop between IL-6, Stat3 activation, and NF-κB signaling by simultaneous inhibition of IL-6 and HER2 is the most effective combination to eliminate both mesenchymal and epithelial populations of BCSCs. Predictions from our model and simulations show excellent agreement with experimental data showing the efficacy of combined HER2 and Il-6 blockade in reducing BCSC populations. Our findings will be directly examined in a planned clinical trial of combined HER2 and IL-6 targeted therapy in HER2-positive breast cancer.
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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.