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Model-Based Tumor Growth Dynamics and Therapy Response in a Mouse Model of De Novo Carcinogenesis
Authors:Charalambos Loizides  Demetris Iacovides  Marios M. Hadjiandreou  Gizem Rizki  Achilleas Achilleos  Katerina Strati  Georgios D. Mitsis
Affiliation:1. Department of Electrical & Electronic Engineering & KIOS Research Center for Intelligent Systems & Networks, University of Cyprus, Nicosia, Cyprus.; 2. Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus.; 3. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, United States of America.; 4. Department of Bioengineering, McGill University, Montreal QC, Canada.; Istituto Superiore di Sanità, ITALY,
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.
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