In silico cancer modeling: is it ready for prime time?


At the dawn of the era of personalized, systems-driven medicine, computational or in silico modeling and the simulation of disease processes is becoming increasingly important for hypothesis generation and data integration in both experiments and clinics alike. Arguably, the use of these techniques is nowhere more visible than in oncology. To illustrate the field's vast potential, as well as its current limitations, we briefly review selected studies on modeling malignant brain tumors. Implications for clinical practice, and for clinical trial design and outcome prediction, are also discussed.

Key Points

  • In generating experimentally testable hypotheses and facilitating multimodality data integration, in silico modeling is a driving force behind cancer systems biology

  • As exemplified by reviewing selected works on malignant brain tumors, practical applications for computational and mathematical cancer modeling reach from simulating aspects of tumor initiation and progression to modeling of treatment effect

  • In silico modeling is a tool geared to aiding experimental researchers and physicians in investigating the complex processes involved in tumorigenesis, thus supporting innovative discovery research and accelerating the identification of promising targets

  • Although there is no single simulator platform that fits all needs, discrete–continuum (hybrid) modeling, especially agent-based approaches, is particularly promising in integrating molecular, microscopic and macroscopic oncology data and in analyzing processes across scales in space and time

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Figure 1: Simulation snapshot depicting an advanced version of a model that predicts a tumor progression path.
Figure 2: Schematic displaying a simplified EGFR gene–protein interaction network within the three-compartment sub-cellular environment.


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This work has been supported, in part, by NIH grants CA 085139 and CA 113004 (CViT; and by the Harvard-MIT (HST) Athinoula A Martinos Center for Biomedical Imaging and the Department of Radiology at Massachusetts General Hospital.

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Correspondence to Thomas S Deisboeck.

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Deisboeck, T., Zhang, L., Yoon, J. et al. In silico cancer modeling: is it ready for prime time?. Nat Rev Clin Oncol 6, 34–42 (2009).

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