Quantitative Modeling of Tumor Dynamics and Radiotherapy
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Quantitative Modeling of Tumor Dynamics and Radiotherapy Heiko Enderling • Mark A. J. Chaplain Philip Hahnfeldt
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Received: 14 June 2010 / Accepted: 5 July 2010 / Published online: 24 July 2010 Ó Springer Science+Business Media B.V. 2010
Abstract Cancer is a complex disease, necessitating research on many different levels; at the subcellular level to identify genes, proteins and signaling pathways associated with the disease; at the cellular level to identify, for example, cell-cell adhesion and communication mechanisms; at the tissue level to investigate disruption of homeostasis and interaction with the tissue of origin or settlement of metastasis; and finally at the systems level to explore its global impact, e.g. through the mechanism of cachexia. Mathematical models have been proposed to identify key mechanisms that underlie dynamics and events at every scale of interest, and increasing effort is now being paid to multi-scale models that bridge the different scales. With more biological data becoming available and with increased interdisciplinary efforts, theoretical models are rendering suitable tools to predict the origin and course of the disease. The ultimate aims of cancer models, however, are to enlighten our concept of the carcinogenesis process and to assist in the designing of treatment protocols that can reduce mortality and improve patient quality of life. Conventional treatment of cancer is surgery combined with radiotherapy or chemotherapy for localized tumors or systemic treatment of advanced cancers, respectively. Although radiation is widely used as treatment, most scheduling is based on empirical knowledge and less on the predictions of sophisticated growth dynamical models of treatment response. Part of the failure to translate modeling research to the clinic may stem from language barriers, exacerbated by often esoteric model renderings with inaccessible parameterization. Here we discuss some ideas for combining tractable dynamical tumor growth models with radiation response models using biologically accessible parameters to provide a more H. Enderling (&) P. Hahnfeldt Center of Cancer Systems Biology, Caritas St. Elizabeth’s Medical Center, Tufts University School of Medicine, 736 Cambridge Street, Boston, MA 02135, USA e-mail: [email protected] M. A. J. Chaplain Division of Mathematics, University of Dundee, Nethergate, Dundee DD14HN, UK
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intuitive and exploitable framework for understanding the complexity of radiotherapy treatment and failure. Keywords Mathematical model Cellular automaton Radiotherapy Accelerated repopulation cancer stem cells
1 Radiotherapy Radiotherapy is a common treatment for cancer, either alone or in combination with surgery. The aim of radiotherapy is to destroy cancer cells with radiation while limiting the damage to nearby healthy cells (Oldham 2001). If tumor cells respond to irradiation at lower doses than normal tissue, then a therapeutic window exists in which a variety of treatment protocols can be used to
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