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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 i...

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Autores principales: Aguadé-Gorgorió, Guim, Kauffman, Stuart, Solé, Ricard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8712307/
https://www.ncbi.nlm.nih.gov/pubmed/34958403
http://dx.doi.org/10.1007/s11538-021-00970-9
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author Aguadé-Gorgorió, Guim
Kauffman, Stuart
Solé, Ricard
author_facet Aguadé-Gorgorió, Guim
Kauffman, Stuart
Solé, Ricard
author_sort Aguadé-Gorgorió, Guim
collection PubMed
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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11538-021-00970-9.
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spelling pubmed-87123072022-01-11 Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity Aguadé-Gorgorió, Guim Kauffman, Stuart Solé, Ricard Bull Math Biol Original Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11538-021-00970-9. Springer US 2021-12-27 2022 /pmc/articles/PMC8712307/ /pubmed/34958403 http://dx.doi.org/10.1007/s11538-021-00970-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Article
Aguadé-Gorgorió, Guim
Kauffman, Stuart
Solé, Ricard
Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity
title 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_short Transition Therapy: Tackling the Ecology of Tumor Phenotypic Plasticity
title_sort transition therapy: tackling the ecology of tumor phenotypic plasticity
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8712307/
https://www.ncbi.nlm.nih.gov/pubmed/34958403
http://dx.doi.org/10.1007/s11538-021-00970-9
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