The Importance of Spatial Distribution of Stemness and Proliferation State in Determining Tumor Radioresponse

Enderling, Heiko; Park, D.; Hlatky, Lynn; Hahnfeldt, Philip

doi:10.1051/mmnp/20094305

Cited by 66 publications

(62 citation statements)

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“…In recent years the number of mathematical models for tumor growth, avascular and vascular, has increased dramatically (Araujo and McElwin 2004;Lowengrub et al 2009), and a straightforward application of such models has been to simulate treatment response and predict novel strategies (Basse and Ubezio 2007). Although chemotherapy has been simulated extensively through the modeling of tumor-induced vasculature (Liu et al 2007;Frieboes et al 2009;Gordon et al 2009), much less attention has been devoted to formulating and analyzing dynamic models of conventional tumor Enderling et al (2009a) radiotherapy. In this paper we have presented some promising modeling approaches to simulate the radiation response of cancer cells and whole tumors.…”

Section: Discussionmentioning

confidence: 98%

“…7 Accelerated repopulation of cancer stem cells and the whole tumor population during and after radiotherapy due to cancer stem cell radioresistance. Shown are the averages of 10 independent simulations each response of tumors with different stem cell fractions and stem cell proliferation patterns (Enderling et al 2009a). These simulations can be compared with clinical data on proliferation dynamical responses to different fractionations (Powathil et al 2007;Basse and Ubezio 2007) and to estimate the size of the stem cell population, and infer its potential response to new treatment scenarios.…”

Section: Discussionmentioning

confidence: 99%

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Quantitative Modeling of Tumor Dynamics and Radiotherapy

2010

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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 intuitive and exploitable framework for understanding the complexity of radiotherapy treatment and failure.

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Section: Discussionmentioning

confidence: 98%

Section: Discussionmentioning

confidence: 99%

Quantitative Modeling of Tumor Dynamics and Radiotherapy

2010

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“…We have implemented a cellular automaton model (23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33) based on rules for independent cells in response to their local environment (see ref. 34 for a review of similar model approaches).…”

Section: Methodsmentioning

confidence: 99%

Paradoxical Dependencies of Tumor Dormancy and Progression on Basic Cell Kinetics

et al. 2009

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Even after a tumor is established, it can early on enter a state of dormancy marked by balanced cell proliferation and cell death. Disturbances to this equilibrium may affect cancer risk, as they may cause the eventual lifetime clinical presentation of a tumor that might otherwise have remained asymptomatic. Previously, we showed that cell death, proliferation, and migration can play a role in shifting this dynamic, making the understanding of their combined influence on tumor development essential. We developed an individual cell-based computer model of the interaction of cancer stem cells and their nonstem progeny to study early tumor dynamics. Simulations of tumor growth show that three basic components of tumor growth-cell proliferation, migration, and death-combine in unexpected ways to control tumor progression and, thus, clinical cancer risk. We show that increased proliferation capacity in nonstem tumor cells and limited cell migration overall lead to space constraints that inhibit proliferation and tumor growth. By contrast, increasing the rate of cell death produces the expected tumor size reduction in the short term, but results ultimately in paradoxical accelerated long-term growth owing to the liberation of cancer stem cells and formation of self-metastases.

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“…They used ordinary and partial differential equations (ODE and PDE) to model tumor growth and the effects of radiotherapy. Enderling et al [13] developed a model for simulation of radiotherapy to evaluate treatment outcomes with several stem cell pool sizes and different quiescence radiosensitivities. At first they considered homogeneous radiosensitivity in tumor.…”

Section: Radiotherapy Simulation and Optimizationmentioning

confidence: 99%