Identifying therapeutic targets in a combined EGFR-TGF R signalling cascade using a multiscale agent-based cancer model

Complex Biosystems Modeling Laboratory, Harvard-MIT (HST) Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital-East, 13th Street, Charlestown, MA 02129, USA.
Mathematical Medicine and Biology (Impact Factor: 1.66). 12/2010; 29(1):95-108. DOI: 10.1093/imammb/dqq023
Source: PubMed


Applying a previously developed non-small cell lung cancer model, we assess 'cross-scale' the therapeutic efficacy of targeting a variety of molecular components of the epidermal growth factor receptor (EGFR) signalling pathway. Simulation of therapeutic inhibition and amplification allows for the ranking of the implemented downstream EGFR signalling molecules according to their therapeutic values or indices. Analysis identifies mitogen-activated protein kinase and extracellular signal-regulated kinase as top therapeutic targets for both inhibition and amplification-based treatment regimen but indicates that combined parameter perturbations do not necessarily improve the therapeutic effect of the separate parameter treatments as much as might be expected. Potential future strategies using this in silico model to tailor molecular treatment regimen are discussed.

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Available from: Zhihui Wang, Jan 16, 2015
    • "Agent-based modeling ABM has been widely accepted as a useful tool in a variety of cancer research fields[11,12,242526272829303132333435, as it offers many advantages over other methods in understanding cancer initiation, progression, and invasion mechanisms (for excellent reviews please see[1,17,19]). In particular, it is straightforward to use an ABM to examine the role of diversity in cell populations as well as within each individual cell and to determine the relationship of tumor behaviors between scales. "
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    ABSTRACT: Mathematical modeling has become a valuable tool that strives to complement conventional biomedical research modalities in order to predict experimental outcome, generate new medical hypotheses, and optimize clinical therapies. Two specific approaches, pharmacokinetic-pharmacodynamic (PK-PD) modeling, and agent-based modeling (ABM), have been widely applied in cancer research. While they have made important contributions on their own (e.g., PK-PD in examining chemotherapy drug efficacy and resistance, and ABM in describing and predicting tumor growth and metastasis), only a few groups have started to combine both approaches together in an effort to gain more insights into the details of drug dynamics and the resulting impact on tumor growth. In this review, we focus our discussion on some of the most recent modeling studies building on a combined PK-PD and ABM approach that have generated experimentally testable hypotheses. Some future directions are also discussed.
    No preview · Article · Jan 2015 · Journal of Pharmacokinetics and Pharmacodynamics
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    • "In the work at hand we analyse how the cellular scale of our model depends on the reaction parameters of the molecular interaction network. Motivated by the idea presented in [39] [40], parameters on the molecular scale are modified and the effect on the cellular scale is measured. "
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    ABSTRACT: Glioblastomas are highly malignant brain tumours. Mathematical models and their analysis provide a tool to support the understanding of the development of these tumours as well as the design of more effective treatment strategies. We have previously developed a multiscale model of glioblastoma progression that covers processes on the cellular and molecular scale. Here, we present a novel nutrient-dependent multiscale sensitivity analysis of this model that helps to identify those reaction parameters of the molecular interaction network that influence the tumour progression on the cellular scale the most. In particular, those parameters are identified that essentially determine tumour expansion and could be therefore used as potential therapy targets. As indicators for the success of a potential therapy target, a deceleration of the tumour expansion and a reduction of the tumour volume are employed. From the results, it can be concluded that no single parameter variation results in a less aggressive tumour. However, it can be shown that a few combined perturbations of two systematically selected parameters cause a slow-down of the tumour expansion velocity accompanied with a decrease of the tumour volume. Those parameters are primarily linked to the reactions that involve the microRNA-451 and the thereof regulated protein MO25.
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    • "Fig. 2depicts a brief schematic of this cellular phenotype decision algorithm. Recently, Wang and Deisboeck et al. also investigated, through a combined multiscale ABM and cross-scale target evaluation approach, which components of the implemented EGFR pathway in a NSCLC model are most crucial for the signaling cascade, and hence would have the highest therapeutic effect if targeted directly2728293031; they found that ERK proved to be the highest ranked molecular target. In addition, other groups have used this type of ABM to examine the effects of EGFR targeted therapy in brain cancer[32]. "
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