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A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer

Jorge G.T. Zañudo, Réka Albert
doi: https://doi.org/10.1101/176214
Jorge G.T. Zañudo
1Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, 16802-6300, USA
2Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA
3Broad Institute of Harvard and Massachusetts Institute of Technology, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA
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Réka Albert
1Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, 16802-6300, USA
4Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, 16802-6300, USA
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Abstract

Background Mechanistic models of within-cell signal transduction networks can explain how these networks integrate internal and external inputs to give rise to the appropriate cellular response. These models can be fruitfully used in cancer cells, whose aberrant decision-making regarding their survival or death, proliferation or quiescence can be connected to errors in the state of nodes or edges of the signal transduction network.

Results Here we present a comprehensive network, and discrete dynamic model, of signal transduction in breast cancer based on the literature of ER+, HER2+, and PIK3CA-mutant breast cancers. The network model recapitulates known resistance mechanisms to PI3K inhibitors and suggests other possibilities for resistance. The model also reveals known and novel combinatorial interventions that are more effective than PI3K inhibition alone.

Conclusions The use of a logic-based, discrete dynamic model enables the identification of results that are mainly due to the organization of the signaling network, and those that also depend on the kinetics of individual events. Network-based models such as this will play an increasing role in the rational design of high-order therapeutic combinations.

  • List of abbreviations

    AKT
    protein kinase B
    ER
    estrogen receptor
    HER2
    human epidermal growth factor receptor 2
    MEK
    mitogen-activated protein kinase kinase
    MEKi
    MEK inhibitor
    PI3K
    phosphatidylinositol
    4,5
    biphosphate 3-kinase
    RTK
    receptor tyrosine kinase
    RTKi
    RTK inhibitor
  • Copyright 
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    Posted August 14, 2017.
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    A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer
    Jorge G.T. Zañudo, Réka Albert
    bioRxiv 176214; doi: https://doi.org/10.1101/176214
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    A network modeling approach to elucidate drug resistance mechanisms and predict combinatorial drug treatments in breast cancer
    Jorge G.T. Zañudo, Réka Albert
    bioRxiv 176214; doi: https://doi.org/10.1101/176214

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