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Growing heterogeneous tumors in silico

Jana Gevertz1,* and S. Torquato1,2,3,4,5,†

  • 1Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA
  • 2Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA
  • 3Princeton Center for Theoretical Science, Princeton University, Princeton, New Jersey 08544, USA
  • 4Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, New Jersey 08544, USA
  • 5School of Natural Sciences, Institute for Advanced Study, Princeton, New Jersey 08540, USA

  • *Present address: Department of Mathematics and Statistics, The College of New Jersey, Ewing, New Jersey 08628, USA
  • †torquato@princeton.edu
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Phys. Rev. E 80, 051910 – Published 16 November, 2009

DOI: https://doi.org/10.1103/PhysRevE.80.051910

Abstract

An in silico tool that can be utilized in the clinic to predict neoplastic progression and propose individualized treatment strategies is the holy grail of computational tumor modeling. Building such a tool requires the development and successful integration of a number of biophysical and mathematical models. In this paper, we work toward this long-term goal by formulating a cellular automaton model of tumor growth that accounts for several different inter-tumor processes and host-tumor interactions. In particular, the algorithm couples the remodeling of the microvasculature with the evolution of the tumor mass and considers the impact that organ-imposed physical confinement and environmental heterogeneity have on tumor size and shape. Furthermore, the algorithm is able to account for cell-level heterogeneity, allowing us to explore the likelihood that different advantageous and deleterious mutations survive in the tumor cell population. This computational tool we have built has a number of applications in its current form in both predicting tumor growth and predicting response to treatment. Moreover, the latent power of our algorithm is that it also suggests other tumor-related processes that need to be accounted for and calls for the conduction of new experiments to validate the model’s predictions.

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Phys. Rev. E 80, 051910– Published 16 November, 2009
  • Received 20 June 2009

© 2009 American Physical Society

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