A stochastic model for immunotherapy of cancer

We propose an extension of a standard stochastic individual-based model in population dynamics which broadens the range of biological applications. Our primary motivation is modelling of immunotherapy of malignant tumours. In this context the different actors, T-cells, cytokines or cancer cells, are...

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Main Authors:	Baar, Martina, Coquille, Loren, Mayer, Hannah, Hölzel, Michael, Rogava, Meri, Tüting, Thomas, Bovier, Anton
Format:	Online
Language:	English
Published:	Nature Publishing Group 2016
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827069/

id	pubmed-4827069
recordtype	oai_dc
spelling	pubmed-48270692016-04-19 A stochastic model for immunotherapy of cancer Baar, Martina Coquille, Loren Mayer, Hannah Hölzel, Michael Rogava, Meri Tüting, Thomas Bovier, Anton Article We propose an extension of a standard stochastic individual-based model in population dynamics which broadens the range of biological applications. Our primary motivation is modelling of immunotherapy of malignant tumours. In this context the different actors, T-cells, cytokines or cancer cells, are modelled as single particles (individuals) in the stochastic system. The main expansions of the model are distinguishing cancer cells by phenotype and genotype, including environment-dependent phenotypic plasticity that does not affect the genotype, taking into account the effects of therapy and introducing a competition term which lowers the reproduction rate of an individual in addition to the usual term that increases its death rate. We illustrate the new setup by using it to model various phenomena arising in immunotherapy. Our aim is twofold: on the one hand, we show that the interplay of genetic mutations and phenotypic switches on different timescales as well as the occurrence of metastability phenomena raise new mathematical challenges. On the other hand, we argue why understanding purely stochastic events (which cannot be obtained with deterministic models) may help to understand the resistance of tumours to therapeutic approaches and may have non-trivial consequences on tumour treatment protocols. This is supported through numerical simulations. Nature Publishing Group 2016-04-11 /pmc/articles/PMC4827069/ /pubmed/27063839 http://dx.doi.org/10.1038/srep24169 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Baar, Martina Coquille, Loren Mayer, Hannah Hölzel, Michael Rogava, Meri Tüting, Thomas Bovier, Anton
spellingShingle	Baar, Martina Coquille, Loren Mayer, Hannah Hölzel, Michael Rogava, Meri Tüting, Thomas Bovier, Anton A stochastic model for immunotherapy of cancer
author_facet	Baar, Martina Coquille, Loren Mayer, Hannah Hölzel, Michael Rogava, Meri Tüting, Thomas Bovier, Anton
author_sort	Baar, Martina
title	A stochastic model for immunotherapy of cancer
title_short	A stochastic model for immunotherapy of cancer
title_full	A stochastic model for immunotherapy of cancer
title_fullStr	A stochastic model for immunotherapy of cancer
title_full_unstemmed	A stochastic model for immunotherapy of cancer
title_sort	stochastic model for immunotherapy of cancer
description	We propose an extension of a standard stochastic individual-based model in population dynamics which broadens the range of biological applications. Our primary motivation is modelling of immunotherapy of malignant tumours. In this context the different actors, T-cells, cytokines or cancer cells, are modelled as single particles (individuals) in the stochastic system. The main expansions of the model are distinguishing cancer cells by phenotype and genotype, including environment-dependent phenotypic plasticity that does not affect the genotype, taking into account the effects of therapy and introducing a competition term which lowers the reproduction rate of an individual in addition to the usual term that increases its death rate. We illustrate the new setup by using it to model various phenomena arising in immunotherapy. Our aim is twofold: on the one hand, we show that the interplay of genetic mutations and phenotypic switches on different timescales as well as the occurrence of metastability phenomena raise new mathematical challenges. On the other hand, we argue why understanding purely stochastic events (which cannot be obtained with deterministic models) may help to understand the resistance of tumours to therapeutic approaches and may have non-trivial consequences on tumour treatment protocols. This is supported through numerical simulations.
publisher	Nature Publishing Group
publishDate	2016
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4827069/
_version_	1613564541154623488

A stochastic model for immunotherapy of cancer

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