Sequential Application of Anti-Cancer Drugs Enhances Cell Death by Re-wiring Apoptotic Signaling Networks

By Michael J. Lee, A. S. Ye, A. Gardino, A. Heijink, Peter K. Sorger, G. MacBeath, Michael B. Yaffe. Cell, 2012

673 citations recorded by the source corpus.

SUMMARY Crosstalk and complexity within signaling pathways, and their perturbation by oncogenes, limits component-by-component approaches to understanding human disease. Network analysis of how normal and oncogenic signaling can be re-wired by drugs may provide opportunities to target tumors with high specificity and efficacy. Using targeted inhibition of oncogenic signaling pathways combined with DNA damaging chemotherapy, we report that time-staggered EGFR inhibition, but not simultaneous co-administration, dramatically sensitizes a subset of triple-negative breast cancer cells to genotoxic drugs. Systems-level analysis—using high-density time-dependent measurements of signaling networks, gene expression profiles, and cell phenotypic responses in combination with mathematical modeling— revealed an approach for altering the intrinsic state of the cell through dynamic re-wiring of oncogenic signaling pathways. This process converts these cells to a less tumorigenic state that is more susceptible to DNA damage-induced cell death by re-activation of an extrinsic apoptotic pathway whose function is suppressed in the oncogene-addicted state.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/7c548579fa3c3b33a5d69fe050e848e1d6541e0f

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Citations in the corpus

Systems Biology of Cancer Metastasis (2019, background): “…Cell line variants: Selection of clones from metastatic sources showing latency (Marlow and Dontu, 2015) Drug Resistance i. Multivariate statistical analysis and regimen optimization: Systemic variation of drug regimen, combination etc to study a response (Korkut et al., 2015; Lee et al., 2012) ii.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “…network- or machine learningbased clustering (Hopkins et al., 2006; Hopkins, 2008; Chen et al., 2009e; Xiong et al., 2010; Yang et al., 2010; Hu et al., 2011; Takigawa et al., 2011; Yabuuchi et al., 2011; Cheng et al., 2012a, 2012b; Lee et al., 2012c; Nacher & Schwartz, 2012; Yu et al., 2012).”