Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response
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 t...
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| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0135797 |
| format | Article |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0135797</identifier><identifier>PMID: 26397099</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Allografts ; Analysis ; Breast cancer ; Breast Neoplasms - metabolism ; Breast Neoplasms - pathology ; Breast Neoplasms - therapy ; Cancer ; Cancer therapies ; Cancer treatment ; Carcinogenesis ; Carcinogens ; Cell cycle ; Cell self-renewal ; Cell Transformation, Neoplastic ; Complex systems ; Computer Simulation ; Cytokines ; Development and progression ; Epithelial-Mesenchymal Transition ; Eradication ; ErbB-2 protein ; Feedback loops ; Female ; Growth factors ; Humans ; Inhibition ; Interleukin 6 ; Mathematical models ; Medicine ; Mesenchyme ; Models, Biological ; Models, Statistical ; Models, Theoretical ; Neoplastic Stem Cells - drug effects ; Neoplastic Stem Cells - metabolism ; Neoplastic Stem Cells - pathology ; NF-κB protein ; Physiological aspects ; Population ; Populations ; Positive feedback ; Simulation ; Stat3 protein ; Stem Cell Niche ; Stem cell transplantation ; Stem cells ; Stochastic models ; Stochasticity ; Treatment Outcome ; Tumor Microenvironment ; Tumors ; Xenografts ; Xenotransplantation</subject><ispartof>PloS one, 2015-09, Vol.10 (9), p.e0135797</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Sehl et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Sehl et al 2015 Sehl et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-b46169e1cc0d3ccbf8fae46597ab854ccd2e1f48791fc9683387c2dc445a00da3</citedby><cites>FETCH-LOGICAL-c692t-b46169e1cc0d3ccbf8fae46597ab854ccd2e1f48791fc9683387c2dc445a00da3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4580445/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4580445/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26397099$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sehl, Mary E</creatorcontrib><creatorcontrib>Shimada, Miki</creatorcontrib><creatorcontrib>Landeros, Alfonso</creatorcontrib><creatorcontrib>Lange, Kenneth</creatorcontrib><creatorcontrib>Wicha, Max S</creatorcontrib><title>Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Cancer stem cells (CSCs) possess capacity to both self-renew and generate all cells within a tumor, and are thought to drive tumor recurrence. 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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.</description><subject>Algorithms</subject><subject>Allografts</subject><subject>Analysis</subject><subject>Breast cancer</subject><subject>Breast Neoplasms - metabolism</subject><subject>Breast Neoplasms - pathology</subject><subject>Breast Neoplasms - therapy</subject><subject>Cancer</subject><subject>Cancer therapies</subject><subject>Cancer treatment</subject><subject>Carcinogenesis</subject><subject>Carcinogens</subject><subject>Cell cycle</subject><subject>Cell self-renewal</subject><subject>Cell Transformation, Neoplastic</subject><subject>Complex systems</subject><subject>Computer Simulation</subject><subject>Cytokines</subject><subject>Development and progression</subject><subject>Epithelial-Mesenchymal Transition</subject><subject>Eradication</subject><subject>ErbB-2 protein</subject><subject>Feedback loops</subject><subject>Female</subject><subject>Growth factors</subject><subject>Humans</subject><subject>Inhibition</subject><subject>Interleukin 6</subject><subject>Mathematical models</subject><subject>Medicine</subject><subject>Mesenchyme</subject><subject>Models, Biological</subject><subject>Models, Statistical</subject><subject>Models, Theoretical</subject><subject>Neoplastic Stem Cells - 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metabolism</topic><topic>Breast Neoplasms - pathology</topic><topic>Breast Neoplasms - therapy</topic><topic>Cancer</topic><topic>Cancer therapies</topic><topic>Cancer treatment</topic><topic>Carcinogenesis</topic><topic>Carcinogens</topic><topic>Cell cycle</topic><topic>Cell self-renewal</topic><topic>Cell Transformation, Neoplastic</topic><topic>Complex systems</topic><topic>Computer Simulation</topic><topic>Cytokines</topic><topic>Development and progression</topic><topic>Epithelial-Mesenchymal Transition</topic><topic>Eradication</topic><topic>ErbB-2 protein</topic><topic>Feedback loops</topic><topic>Female</topic><topic>Growth factors</topic><topic>Humans</topic><topic>Inhibition</topic><topic>Interleukin 6</topic><topic>Mathematical models</topic><topic>Medicine</topic><topic>Mesenchyme</topic><topic>Models, Biological</topic><topic>Models, Statistical</topic><topic>Models, Theoretical</topic><topic>Neoplastic Stem Cells - drug effects</topic><topic>Neoplastic Stem Cells - metabolism</topic><topic>Neoplastic Stem Cells - pathology</topic><topic>NF-κB protein</topic><topic>Physiological aspects</topic><topic>Population</topic><topic>Populations</topic><topic>Positive feedback</topic><topic>Simulation</topic><topic>Stat3 protein</topic><topic>Stem Cell Niche</topic><topic>Stem cell transplantation</topic><topic>Stem cells</topic><topic>Stochastic models</topic><topic>Stochasticity</topic><topic>Treatment Outcome</topic><topic>Tumor Microenvironment</topic><topic>Tumors</topic><topic>Xenografts</topic><topic>Xenotransplantation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sehl, Mary E</creatorcontrib><creatorcontrib>Shimada, Miki</creatorcontrib><creatorcontrib>Landeros, Alfonso</creatorcontrib><creatorcontrib>Lange, Kenneth</creatorcontrib><creatorcontrib>Wicha, Max S</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26397099</pmid><doi>10.1371/journal.pone.0135797</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2015-09, Vol.10 (9), p.e0135797 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1719466084 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Algorithms Allografts Analysis Breast cancer Breast Neoplasms - metabolism Breast Neoplasms - pathology Breast Neoplasms - therapy Cancer Cancer therapies Cancer treatment Carcinogenesis Carcinogens Cell cycle Cell self-renewal Cell Transformation, Neoplastic Complex systems Computer Simulation Cytokines Development and progression Epithelial-Mesenchymal Transition Eradication ErbB-2 protein Feedback loops Female Growth factors Humans Inhibition Interleukin 6 Mathematical models Medicine Mesenchyme Models, Biological Models, Statistical Models, Theoretical Neoplastic Stem Cells - drug effects Neoplastic Stem Cells - metabolism Neoplastic Stem Cells - pathology NF-κB protein Physiological aspects Population Populations Positive feedback Simulation Stat3 protein Stem Cell Niche Stem cell transplantation Stem cells Stochastic models Stochasticity Treatment Outcome Tumor Microenvironment Tumors Xenografts Xenotransplantation |
| title | Modeling of Cancer Stem Cell State Transitions Predicts Therapeutic Response |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-05T12%3A44%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Modeling%20of%20Cancer%20Stem%20Cell%20State%20Transitions%20Predicts%20Therapeutic%20Response&rft.jtitle=PloS%20one&rft.au=Sehl,%20Mary%20E&rft.date=2015-09-23&rft.volume=10&rft.issue=9&rft.spage=e0135797&rft.pages=e0135797-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0135797&rft_dat=%3Cgale_plos_%3EA429601021%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1719466084&rft_id=info:pmid/26397099&rft_galeid=A429601021&rft_doaj_id=oai_doaj_org_article_5f96ede182a647c3a52111466b666cc6&rfr_iscdi=true |