Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity

Phenotypic switching in cancer cells has been found to be present across tumor types. Recent studies on Glioblastoma report a remarkably common architecture of four well-defined phenotypes coexisting within high levels of intra-tumor genetic heterogeneity. Similar dynamics have been shown to occur in breast cancer and melanoma and are likely to be found across cancer types. Given the adaptive potential of phenotypic switching (PHS) strategies, understanding how it drives tumor evolution and therapy resistance is a major priority. Here we present a mathematical framework uncovering the ecological dynamics behind PHS. The model is able to reproduce experimental results, and mathematical conditions for cancer progression reveal PHS-specific features of tumors with direct consequences on therapy resistance. In particular, our model reveals a threshold for the resistant-to-sensitive phenotype transition rate, below which any cytotoxic or switch-inhibition therapy is likely to fail. The model is able to capture therapeutic success thresholds for cancers where nonlinear growth dynamics or larger PHS architectures are in place, such as glioblastoma or melanoma. By doing so, the model presents a novel set of conditions for the success of combination therapies able to target replication and phenotypic transitions at once. Following our results, we discuss transition therapy as a novel scheme to target not only combined cytotoxicity but also the rates of phenotypic switching.

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Bibliographic Details
Main Authors: Aguadé-Gorgorió, Guim, Kauffman, Stuart A., Solé, Ricard V.
Other Authors: Fundación Botín
Format: artículo biblioteca
Language:English
Published: Springer Nature 2022
Subjects:Cancer ecology, Phenotypic switching, Epigenetic plasticity, Combination therapies, Transition therapy,
Online Access:http://hdl.handle.net/10261/271792
http://dx.doi.org/10.13039/501100011033
http://dx.doi.org/10.13039/501100003030
http://dx.doi.org/10.13039/100010784
http://dx.doi.org/10.13039/501100000780
http://dx.doi.org/10.13039/501100006373
http://dx.doi.org/10.13039/501100003329
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spelling dig-ibe-es-10261-2717922022-07-16T02:18:42Z Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity Aguadé-Gorgorió, Guim Kauffman, Stuart A. Solé, Ricard V. Fundación Botín Banco Santander Ministerio de Economía y Competitividad (España) Agencia Estatal de Investigación (España) European Commission Agència de Gestió d'Ajuts Universitaris i de Recerca Cancer ecology Phenotypic switching Epigenetic plasticity Combination therapies Transition therapy Phenotypic switching in cancer cells has been found to be present across tumor types. Recent studies on Glioblastoma report a remarkably common architecture of four well-defined phenotypes coexisting within high levels of intra-tumor genetic heterogeneity. Similar dynamics have been shown to occur in breast cancer and melanoma and are likely to be found across cancer types. Given the adaptive potential of phenotypic switching (PHS) strategies, understanding how it drives tumor evolution and therapy resistance is a major priority. Here we present a mathematical framework uncovering the ecological dynamics behind PHS. The model is able to reproduce experimental results, and mathematical conditions for cancer progression reveal PHS-specific features of tumors with direct consequences on therapy resistance. In particular, our model reveals a threshold for the resistant-to-sensitive phenotype transition rate, below which any cytotoxic or switch-inhibition therapy is likely to fail. The model is able to capture therapeutic success thresholds for cancers where nonlinear growth dynamics or larger PHS architectures are in place, such as glioblastoma or melanoma. By doing so, the model presents a novel set of conditions for the success of combination therapies able to target replication and phenotypic transitions at once. Following our results, we discuss transition therapy as a novel scheme to target not only combined cytotoxicity but also the rates of phenotypic switching. This work was supported by the Botín Foundation by Banco Santander through its Santander Universities Global Division, the Spanish Ministry of Economy and Competitiveness, grant FIS2016-77447-R MINECO/AEI/FEDER, an AGAUR FI 2018 grant, and the Santa Fe Institute where most of this work was done. Peer reviewed 2022-06-07T11:16:47Z 2022-06-07T11:16:47Z 2022 artículo Bulletin of Mathematical Biology 84: 24 (2022) 0092-8240 http://hdl.handle.net/10261/271792 10.1007/s11538-021-00970-9 1522-9602 http://dx.doi.org/10.13039/501100011033 http://dx.doi.org/10.13039/501100003030 http://dx.doi.org/10.13039/100010784 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100006373 http://dx.doi.org/10.13039/501100003329 en Publisher's version https://doi.org/10.1007/s11538-021-00970-9 Sí open application/pdf Springer Nature
institution IBE ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-ibe-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del IBE España
language English
topic Cancer ecology
Phenotypic switching
Epigenetic plasticity
Combination therapies
Transition therapy
Cancer ecology
Phenotypic switching
Epigenetic plasticity
Combination therapies
Transition therapy
spellingShingle Cancer ecology
Phenotypic switching
Epigenetic plasticity
Combination therapies
Transition therapy
Cancer ecology
Phenotypic switching
Epigenetic plasticity
Combination therapies
Transition therapy
Aguadé-Gorgorió, Guim
Kauffman, Stuart A.
Solé, Ricard V.
Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity
description Phenotypic switching in cancer cells has been found to be present across tumor types. Recent studies on Glioblastoma report a remarkably common architecture of four well-defined phenotypes coexisting within high levels of intra-tumor genetic heterogeneity. Similar dynamics have been shown to occur in breast cancer and melanoma and are likely to be found across cancer types. Given the adaptive potential of phenotypic switching (PHS) strategies, understanding how it drives tumor evolution and therapy resistance is a major priority. Here we present a mathematical framework uncovering the ecological dynamics behind PHS. The model is able to reproduce experimental results, and mathematical conditions for cancer progression reveal PHS-specific features of tumors with direct consequences on therapy resistance. In particular, our model reveals a threshold for the resistant-to-sensitive phenotype transition rate, below which any cytotoxic or switch-inhibition therapy is likely to fail. The model is able to capture therapeutic success thresholds for cancers where nonlinear growth dynamics or larger PHS architectures are in place, such as glioblastoma or melanoma. By doing so, the model presents a novel set of conditions for the success of combination therapies able to target replication and phenotypic transitions at once. Following our results, we discuss transition therapy as a novel scheme to target not only combined cytotoxicity but also the rates of phenotypic switching.
author2 Fundación Botín
author_facet Fundación Botín
Aguadé-Gorgorió, Guim
Kauffman, Stuart A.
Solé, Ricard V.
format artículo
topic_facet Cancer ecology
Phenotypic switching
Epigenetic plasticity
Combination therapies
Transition therapy
author Aguadé-Gorgorió, Guim
Kauffman, Stuart A.
Solé, Ricard V.
author_sort Aguadé-Gorgorió, Guim
title Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity
title_short Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity
title_full Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity
title_fullStr Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity
title_full_unstemmed Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity
title_sort transition therapy: tackling the ecology of tumor phenotypic plasticity
publisher Springer Nature
publishDate 2022
url http://hdl.handle.net/10261/271792
http://dx.doi.org/10.13039/501100011033
http://dx.doi.org/10.13039/501100003030
http://dx.doi.org/10.13039/100010784
http://dx.doi.org/10.13039/501100000780
http://dx.doi.org/10.13039/501100006373
http://dx.doi.org/10.13039/501100003329
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