Chaplain, Mark A J, Lorenzi, Tommaso, Lorz, Alexander and Venkataraman, Chandrasekhar (2019) Mathematical modelling of phenotypic selection with solid tumours. ENUMATH 2017, Voss, Norway, 25-29 September 2017. Published in: Radu, Florin Adrian, Kumar, Kundan, Berre, Inga, Nordbotten, Jan Martin and Pop, Iuliu Sorin, (eds.) Numerical Mathematics and Advanced Applications: Proceedings of ENUMATH 2017. 126 237-245. Springer ISSN 1439-7358 ISBN 9783319964140

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Abstract

We present a space- and phenotype-structured model of selection dynamics between cancer cells within a solid tumour. In the framework of this model, we combine formal analyses with numerical simulations to investigate in silico the role played by the spatial distribution of oxygen and therapeutic agents in mediating phenotypic selection of cancer cells. Numerical simulations are performed on the 3D geometry of an in vivo human hepatic tumour, which was imaged using computerised tomography. Our modelling extends our previous work in the area through the inclusion of multiple therapeutic agents, one that is cytostatic, whilst the other is cytotoxic. In agreement with our previous work, the results show that spatial inhomogeneities in oxygen and therapeutic agent concentrations, which emerge spontaneously in solid tumours, can promote the creation of distinct local niches and lead to the selection of different phenotypic variants within the same tumour. A novel conclusion we infer from the simulations and analysis is that, for the same total dose, therapeutic protocols based on a combination of cytotoxic and cytostatic agents can be more effective than therapeutic protocols relying solely on cytotoxic agents in reducing the number of viable cancer cells.

Item Type:	Conference Proceedings
Schools and Departments:	School of Mathematical and Physical Sciences > Mathematics
Research Centres and Groups:	Mathematics Applied to Biology Research Group
Subjects:	Q Science > QA Mathematics
Depositing User:	Alice Jackson
Date Deposited:	02 Jul 2019 13:32
Last Modified:	05 Jan 2020 02:00
URI:	http://sro.sussex.ac.uk/id/eprint/84688

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