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Adaptive dynamics of unstable cancer populations: The canonical equation

In most instances of tumour development, genetic instability plays a role in allowing cancer cell populations to respond to selection barriers, such as physical constraints or immune responses, and rapidly adapt to an always changing environment. Modelling instability is a nontrivial task, since by...

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Detalles Bibliográficos
Autores principales: Aguadé‐Gorgorió, Guim, Solé, Ricard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6099832/
https://www.ncbi.nlm.nih.gov/pubmed/30151040
http://dx.doi.org/10.1111/eva.12625
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author Aguadé‐Gorgorió, Guim
Solé, Ricard
author_facet Aguadé‐Gorgorió, Guim
Solé, Ricard
author_sort Aguadé‐Gorgorió, Guim
collection PubMed
description In most instances of tumour development, genetic instability plays a role in allowing cancer cell populations to respond to selection barriers, such as physical constraints or immune responses, and rapidly adapt to an always changing environment. Modelling instability is a nontrivial task, since by definition evolving instability leads to changes in the underlying landscape. In this article, we explore mathematically a simple version of unstable tumour progression using the formalism of adaptive dynamics (AD) where selection and mutation are explicitly coupled. Using a set of basic fitness landscapes, the so‐called canonical equation for the evolution of genetic instability on a minimal scenario associated with a population of unstable cells is derived. We obtain explicit expressions for the evolution of mutation probabilities, and the implications of the model on further experimental studies and potential mutagenic therapies are discussed.
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spelling pubmed-60998322018-08-27 Adaptive dynamics of unstable cancer populations: The canonical equation Aguadé‐Gorgorió, Guim Solé, Ricard Evol Appl Original Articles In most instances of tumour development, genetic instability plays a role in allowing cancer cell populations to respond to selection barriers, such as physical constraints or immune responses, and rapidly adapt to an always changing environment. Modelling instability is a nontrivial task, since by definition evolving instability leads to changes in the underlying landscape. In this article, we explore mathematically a simple version of unstable tumour progression using the formalism of adaptive dynamics (AD) where selection and mutation are explicitly coupled. Using a set of basic fitness landscapes, the so‐called canonical equation for the evolution of genetic instability on a minimal scenario associated with a population of unstable cells is derived. We obtain explicit expressions for the evolution of mutation probabilities, and the implications of the model on further experimental studies and potential mutagenic therapies are discussed. John Wiley and Sons Inc. 2018-04-17 /pmc/articles/PMC6099832/ /pubmed/30151040 http://dx.doi.org/10.1111/eva.12625 Text en © 2018 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Aguadé‐Gorgorió, Guim
Solé, Ricard
Adaptive dynamics of unstable cancer populations: The canonical equation
title Adaptive dynamics of unstable cancer populations: The canonical equation
title_full Adaptive dynamics of unstable cancer populations: The canonical equation
title_fullStr Adaptive dynamics of unstable cancer populations: The canonical equation
title_full_unstemmed Adaptive dynamics of unstable cancer populations: The canonical equation
title_short Adaptive dynamics of unstable cancer populations: The canonical equation
title_sort adaptive dynamics of unstable cancer populations: the canonical equation
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6099832/
https://www.ncbi.nlm.nih.gov/pubmed/30151040
http://dx.doi.org/10.1111/eva.12625
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