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dc.contributor.authorVilla, Chiara
dc.contributor.authorChaplain, Mark Andrew Joseph
dc.contributor.authorLorenzi, Tommaso
dc.date.accessioned2020-10-12T10:30:01Z
dc.date.available2020-10-12T10:30:01Z
dc.date.issued2020-10-06
dc.identifier.citationVilla , C , Chaplain , M A J & Lorenzi , T 2020 , ' Evolutionary dynamics in vascularised tumours under chemotherapy : mathematical modelling, asymptotic analysis and numerical simulations ' , Vietnam Journal of Mathematics , vol. First Online . https://doi.org/10.1007/s10013-020-00445-9en
dc.identifier.issn0866-7179
dc.identifier.otherPURE: 269967305
dc.identifier.otherPURE UUID: 72279180-451c-442a-9b0e-7de674c2b9b8
dc.identifier.otherORCID: /0000-0001-5727-2160/work/82179661
dc.identifier.otherWOS: 000576130300001
dc.identifier.otherScopus: 85092095732
dc.identifier.urihttps://hdl.handle.net/10023/20767
dc.description.abstractWe consider a mathematical model for the evolutionary dynamics of tumour cells in vascularised tumours under chemotherapy. The model comprises a system of coupled partial integro-differential equations for the phenotypic distribution of tumour cells, the concentration of oxygen and the concentration of a chemotherapeutic agent. In order to disentangle the impact of different evolutionary parameters on the emergence of intra-tumour phenotypic heterogeneity and the development of resistance to chemotherapy, we construct explicit solutions to the equation for the phenotypic distribution of tumour cells and provide a detailed quantitative characterisation of the long-time asymptotic behaviour of such solutions. Analytical results are integrated with numerical simulations of a calibrated version of the model based on biologically consistent parameter values. The results obtained provide a theoretical explanation for the observation that the phenotypic properties of tumour cells in vascularised tumours vary with the distance from the blood vessels. Moreover, we demonstrate that lower oxygen levels may correlate with higher levels of phenotypic variability, which suggests that the presence of hypoxic regions supports intra-tumour phenotypic heterogeneity. Finally, the results of our analysis put on a rigorous mathematical basis the idea, previously suggested by formal asymptotic results and numerical simulations, that hypoxia favours the selection for chemoresistant phenotypic variants prior to treatment. Consequently, this facilitates the development of resistance following chemotherapy.
dc.language.isoeng
dc.relation.ispartofVietnam Journal of Mathematicsen
dc.rightsCopyright © The Author(s) 2020. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en
dc.subjectVascularised tumoursen
dc.subjectEvolutionary dynamicsen
dc.subjectIntra-tumour heterogeneityen
dc.subjectResistance to chemotherapyen
dc.subjectMathematical oncologyen
dc.subjectNon-local partial differential equationsen
dc.subjectQA Mathematicsen
dc.subjectRC0254 Neoplasms. Tumors. Oncology (including Cancer)en
dc.subjectT-NDASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subject.lccQAen
dc.subject.lccRC0254en
dc.titleEvolutionary dynamics in vascularised tumours under chemotherapy : mathematical modelling, asymptotic analysis and numerical simulationsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Applied Mathematicsen
dc.identifier.doihttps://doi.org/10.1007/s10013-020-00445-9
dc.description.statusPeer revieweden
dc.date.embargoedUntil2020-10-06


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