Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer

Jorge G T Zañudo; Steven N Steinway; Réka Albert

doi:10.1016/j.coisb.2018.02.002

Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer

Curr Opin Syst Biol. 2018 Jun:9:1-10. doi: 10.1016/j.coisb.2018.02.002.

Authors

Jorge G T Zañudo^{1

2}, Steven N Steinway³, Réka Albert^{1

4}

Affiliations

¹ Department of Physics, The Pennsylvania State University, University Park, PA 16802, USA.
² Department of Medical Oncology, Dana-Farber Cancer Institute and Broad Institute of Harvard and MIT, Boston MA, USA.
³ Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
⁴ Department of Biology, The Pennsylvania State University, University Park, PA 16802, USA.

Abstract

Targeted drugs disrupting proteins that are dysregulated in cancer have emerged as promising treatments because of their specificity to cancer cell aberrations and thus their improved side effect profile. However, their success remains limited, largely due to existing or emergent therapy resistance. We suggest that this is due to limited understanding of the entire relevant cellular landscape. A class of mathematical models called discrete dynamic network models can be used to understand the integrated effect of an individual tumor's aberrations. We review the recent literature on discrete dynamic models of cancer and highlight their predicted therapeutic strategies. We believe dynamic network modeling can be used to drive treatment decision-making in a personalized manner to direct improved treatments in cancer.

Keywords: Boolean model; Cancer phenotypes; Combinatorial therapy; Discrete dynamical system; Drug resistance; Dynamic model; Network model; Personalized therapy; Signal transduction networks; Targeted cancer therapy.

Publication types

Review