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dc.contributor.authorLorenzi, Tommaso
dc.contributor.authorChisholm, Rebecca H.
dc.contributor.authorClairambault, Jean
dc.date.accessioned2016-08-23T14:30:22Z
dc.date.available2016-08-23T14:30:22Z
dc.date.issued2016-08-23
dc.identifier.citationLorenzi , T , Chisholm , R H & Clairambault , J 2016 , ' Tracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equations ' , Biology Direct , vol. 11 , 43 . https://doi.org/10.1186/s13062-016-0143-4en
dc.identifier.issn1745-6150
dc.identifier.otherPURE: 244420569
dc.identifier.otherPURE UUID: 9b8370d2-c18f-4f8b-8ce1-e7590440da9c
dc.identifier.otherScopus: 84983048000
dc.identifier.otherWOS: 000383133500004
dc.identifier.urihttp://hdl.handle.net/10023/9363
dc.descriptionThis work was supported in part by the French National Research Agency through the “ANR blanche” project Kibord [ANR-13-BS01-0004].en
dc.description.abstractBackground: A thorough understanding of the ecological and evolutionary mechanisms that drive the phenotypic evolution of neoplastic cells is a timely and key challenge for the cancer research community. In this respect, mathematical modelling can complement experimental cancer research by offering alternative means of understanding the results of in vitro and in vivo experiments, and by allowing for a quick and easy exploration of a variety of biological scenarios through in silico studies. Results: To elucidate the roles of phenotypic plasticity and selection pressures in tumour relapse, we present here a phenotype-structured model of evolutionary dynamics in a cancer cell population which is exposed to the action of a cytotoxic drug. The analytical tractability of our model allows us to investigate how the phenotype distribution, the level of phenotypic heterogeneity, and the size of the cell population are shaped by the strength of natural selection, the rate of random epimutations, the intensity of the competition for limited resources between cells, and the drug dose in use. Conclusions: Our analytical results clarify the conditions for the successful adaptation of cancer cells faced with environmental changes. Furthermore, the results of our analyses demonstrate that the same cell population exposed to different concentrations of the same cytotoxic drug can take different evolutionary trajectories, which culminate in the selection of phenotypic variants characterised by different levels of drug tolerance. This suggests that the response of cancer cells to cytotoxic agents is more complex than a simple binary outcome, i.e., extinction of sensitive cells and selection of highly resistant cells. Also, our mathematical results formalise the idea that the use of cytotoxic agents at high doses can act as a double-edged sword by promoting the outgrowth of drug resistant cellular clones. Overall, our theoretical work offers a formal basis for the development of anti-cancer therapeutic protocols that go beyond the ‘maximum-tolerated-dose paradigm’, as they may be more effective than traditional protocols at keeping the size of cancer cell populations under control while avoiding the expansion of drug tolerant clones.
dc.format.extent17
dc.language.isoeng
dc.relation.ispartofBiology Directen
dc.rights© 2016 The Author(s). Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.subjectCancer cell populationsen
dc.subjectPhenotypic evolutionen
dc.subjectNatural selectionen
dc.subjectPhenotype plasticityen
dc.subjectEpimutationsen
dc.subjectCytotoxic-drug resistanceen
dc.subjectPhenotypic heterogeneityen
dc.subjectMathematical oncologyen
dc.subjectPhenotype-structured equationsen
dc.subjectQR Microbiologyen
dc.subjectQA Mathematicsen
dc.subjectT-NDASen
dc.subject.lccQRen
dc.subject.lccQAen
dc.titleTracking the evolution of cancer cell populations through the mathematical lens of phenotype-structured equationsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Applied Mathematicsen
dc.identifier.doihttps://doi.org/10.1186/s13062-016-0143-4
dc.description.statusPeer revieweden


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