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A Mathematical-Biological Joint Effort to Investigate the Tumor-Initiating Ability of Cancer Stem Cells

The involvement of Cancer Stem Cells (CSCs) in tumor progression and tumor recurrence is one of the most studied subjects in current cancer research. The CSC hypothesis states that cancer cell populations are characterized by a hierarchical structure that affects cancer progression. Due to the compl...

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Autores principales: Fornari, Chiara, Beccuti, Marco, Lanzardo, Stefania, Conti, Laura, Balbo, Gianfranco, Cavallo, Federica, Calogero, Raffaele A., Cordero, Francesca
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4153566/
https://www.ncbi.nlm.nih.gov/pubmed/25184361
http://dx.doi.org/10.1371/journal.pone.0106193
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author Fornari, Chiara
Beccuti, Marco
Lanzardo, Stefania
Conti, Laura
Balbo, Gianfranco
Cavallo, Federica
Calogero, Raffaele A.
Cordero, Francesca
author_facet Fornari, Chiara
Beccuti, Marco
Lanzardo, Stefania
Conti, Laura
Balbo, Gianfranco
Cavallo, Federica
Calogero, Raffaele A.
Cordero, Francesca
author_sort Fornari, Chiara
collection PubMed
description The involvement of Cancer Stem Cells (CSCs) in tumor progression and tumor recurrence is one of the most studied subjects in current cancer research. The CSC hypothesis states that cancer cell populations are characterized by a hierarchical structure that affects cancer progression. Due to the complex dynamics involving CSCs and the other cancer cell subpopulations, a robust theory explaining their action has not been established yet. Some indications can be obtained by combining mathematical modeling and experimental data to understand tumor dynamics and to generate new experimental hypotheses. Here, we present a model describing the initial phase of ErbB2(+) mammary cancer progression, which arises from a joint effort combing mathematical modeling and cancer biology. The proposed model represents a new approach to investigate the CSC-driven tumorigenesis and to analyze the relations among crucial events involving cancer cell subpopulations. Using in vivo and in vitro data we tuned the model to reproduce the initial dynamics of cancer growth, and we used its solution to characterize observed cancer progression with respect to mutual CSC and progenitor cell variation. The model was also used to investigate which association occurs among cell phenotypes when specific cell markers are considered. Finally, we found various correlations among model parameters which cannot be directly inferred from the available biological data and these dependencies were used to characterize the dynamics of cancer subpopulations during the initial phase of ErbB2(+) mammary cancer progression.
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spelling pubmed-41535662014-09-05 A Mathematical-Biological Joint Effort to Investigate the Tumor-Initiating Ability of Cancer Stem Cells Fornari, Chiara Beccuti, Marco Lanzardo, Stefania Conti, Laura Balbo, Gianfranco Cavallo, Federica Calogero, Raffaele A. Cordero, Francesca PLoS One Research Article The involvement of Cancer Stem Cells (CSCs) in tumor progression and tumor recurrence is one of the most studied subjects in current cancer research. The CSC hypothesis states that cancer cell populations are characterized by a hierarchical structure that affects cancer progression. Due to the complex dynamics involving CSCs and the other cancer cell subpopulations, a robust theory explaining their action has not been established yet. Some indications can be obtained by combining mathematical modeling and experimental data to understand tumor dynamics and to generate new experimental hypotheses. Here, we present a model describing the initial phase of ErbB2(+) mammary cancer progression, which arises from a joint effort combing mathematical modeling and cancer biology. The proposed model represents a new approach to investigate the CSC-driven tumorigenesis and to analyze the relations among crucial events involving cancer cell subpopulations. Using in vivo and in vitro data we tuned the model to reproduce the initial dynamics of cancer growth, and we used its solution to characterize observed cancer progression with respect to mutual CSC and progenitor cell variation. The model was also used to investigate which association occurs among cell phenotypes when specific cell markers are considered. Finally, we found various correlations among model parameters which cannot be directly inferred from the available biological data and these dependencies were used to characterize the dynamics of cancer subpopulations during the initial phase of ErbB2(+) mammary cancer progression. Public Library of Science 2014-09-03 /pmc/articles/PMC4153566/ /pubmed/25184361 http://dx.doi.org/10.1371/journal.pone.0106193 Text en © 2014 Fornari et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Fornari, Chiara
Beccuti, Marco
Lanzardo, Stefania
Conti, Laura
Balbo, Gianfranco
Cavallo, Federica
Calogero, Raffaele A.
Cordero, Francesca
A Mathematical-Biological Joint Effort to Investigate the Tumor-Initiating Ability of Cancer Stem Cells
title A Mathematical-Biological Joint Effort to Investigate the Tumor-Initiating Ability of Cancer Stem Cells
title_full A Mathematical-Biological Joint Effort to Investigate the Tumor-Initiating Ability of Cancer Stem Cells
title_fullStr A Mathematical-Biological Joint Effort to Investigate the Tumor-Initiating Ability of Cancer Stem Cells
title_full_unstemmed A Mathematical-Biological Joint Effort to Investigate the Tumor-Initiating Ability of Cancer Stem Cells
title_short A Mathematical-Biological Joint Effort to Investigate the Tumor-Initiating Ability of Cancer Stem Cells
title_sort mathematical-biological joint effort to investigate the tumor-initiating ability of cancer stem cells
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4153566/
https://www.ncbi.nlm.nih.gov/pubmed/25184361
http://dx.doi.org/10.1371/journal.pone.0106193
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