Model-based tumor growth dynamics and therapy response in a mouse model of de novo carcinogenesis
Date
2015Author
Loizides, Constantinos A.Iacovides, D.
Hadjiandreou, M. M.
Rizki, G.
Achilleos, Achilleas P.
Strati, Katerina
Mitsis, Georgios D.
ISSN
1932-6203Source
PLoS ONEVolume
10Google Scholar check
Keyword(s):
Metadata
Show full item recordAbstract
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. © 2015 Loizides et al. 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 credited.
Collections
Cite as
Related items
Showing items related by title, author, creator and subject.
-
Article
Energy-based model reduction methodology for automated modeling
Louca, Loucas S.; Stein, J. L.; Hulbert, G. M. (2010)In recent years, algorithms have been developed to help automate the production of dynamic system models. Part of this effort has been the development of algorithms that use modeling metrics for generating minimum complexity ...
-
Article
A review of proper modeling techniques
Ersal, T.; Fathy, H. K.; Rideout, D. G.; Louca, Loucas S.; Stein, J. L. (2008)A dynamic system model is proper for a particular application if it achieves the accuracy required by the application with minimal complexity. Because model complexity often-but not always-correlates inversely with simulation ...
-
Conference Object
A model accuracy and validation algorithm
Sendur, P.; Stein, J. L.; Peng, H.; Louca, Loucas S. (American Society of Mechanical Engineers (ASME), 2002)Dynamic models of physical systems with physically meaningful states and parameters have become increasingly important, for design, control and even procurement decisions. The successful use of models in these contexts ...