Model-Based Tumor Growth Dynamics and Therapy Response in a Mouse Model of De Novo Carcinogenesis
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Loizides-2015-Model-Based Tumor Gr.pdf
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Author(s)
Loizides, Charalambos
Iacovides, Demetris
Hadjiandreou, Marios M.
Rizki, Gizem
Achilleos, Achilleas
Strati, Katerina
Mitsis, Georgios D.
Date Issued
December 2015
Journal
PLOS ONE
Publisher
Public Library of Science
Citation
Loizides, Charalambos, Demetris Iacovides, Marios M. Hadjiandreou, Gizem Rizki, Achilleas Achilleos, Katerina Strati, and Georgios D. Mitsis. “Model-Based Tumor Growth Dynamics and Therapy Response in a Mouse Model of De Novo Carcinogenesis.” Edited by Fabrizio Mattei. PLoS ONE 10, no. 12 (December 9, 2015): e0143840.
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Final published version
Abstract
Tumorigenesis is a complex, multistep process that depends on numerous alterations within the cell and contribution from the surrounding stroma. The ability to model macroscopic tumor evolution with high fidelity may contribute to better predictive tools for designing tumor therapy in the clinic. However, attempts to model tumor growth have mainly been developed and validated using data from xenograft mouse models, which fail to capture important aspects of tumorigenesis including tumor-initiating events and interactions with the immune system. In the present study, we investigate tumor growth and therapy dynamics in a mouse model of de novo carcinogenesis that closely recapitulates tumor initiation, progression and maintenance in vivo. We show that the rate of tumor growth and the effects of therapy are highly variable and mouse specific using a Gompertz model to describe tumor growth and a two-compartment pharmacokinetic/ pharmacodynamic model to describe the effects of therapy in mice treated with 5-FU. We show that inter-mouse growth variability is considerably larger than intra-mouse variability and that there is a correlation between tumor growth and drug kill rates. Our results show that in vivo tumor growth and regression in a double transgenic mouse model are highly variable both within and between subjects and that mathematical models can be used to capture the overall characteristics of this variability. In order for these models to become useful tools in the design of optimal therapy strategies and ultimately in clinical practice, a subject-specific modelling strategy is necessary, rather than approaches that are based on the average behavior of a given subject population which could provide erroneous results.
MIT Department
Massachusetts Institute of Technology. Department of Biology
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DOI of Published Version
https://doi.org/10.1371/journal.pone.0143840
