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

Year: 2018
Type: review
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.
DOI: 10.1016/j.coisb.2018.02.002
Institutions: Broad Institute, Pennsylvania State University, Harvard University, Dana-Farber Cancer Institute, Massachusetts Institute of Technology, Johns Hopkins University, Johns Hopkins Medicine
Language: English
Open Access status: hybrid
Estimated APC paid: $4,170
OpenAlex ID: W2790669965
FWCI: 0.883
Cites: 68
Cited by: 64
Related to: 10