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Sci. Signal., 13 August 2013
Vol. 6, Issue 288, p. ra68
Modeling Optimal Therapeutic Strategies
Drug resistance is a common cause of therapy failure in cancer, and identifying optimal therapeutic strategies is difficult because of complex feedback, crosstalk, and redundancy in cellular signaling networks. Using cellular data, Kirouac et al. constructed an in silico model of signaling circuits activated by the ErbB family of receptors in cells with a genomic amplification of ERBB2. Predicted in silico and validated in cultured ERBB2-amplified cells, ErbB3 was activated in response to kinase-targeted therapeutics, such as the ErbB2 inhibitor lapatinib, and ErbB3 activity promoted drug resistance in breast cancer cells. Adding an ErbB3 inhibitor (MM-111) either to lapatinib and trastuzumab treatment or to inhibitors of the kinases AKT and MEK effectively reduced tumor growth in mice bearing ErbB2-overexpressing xenografts. The findings indicate that combination therapies inhibiting ErbB3 are an improved therapeutic option for HER2-positive breast cancer patients.
Citation: D. C. Kirouac, J. Y. Du, J. Lahdenranta, R. Overland, D. Yarar, V. Paragas, E. Pace, C. F. McDonagh, U. B. Nielsen, M. D. Onsum, Computational Modeling of ERBB2-Amplified Breast Cancer Identifies Combined ErbB2/3 Blockade as Superior to the Combination of MEK and AKT Inhibitors. Sci. Signal.6, ra68 (2013).
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[DOI: 10.1126/scisignal.2005207] |Abstract »|Full Text »|Podcast »
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[DOI: 10.1126/scisignal.2000352] |Editor's Summary »|Abstract »|Full Text »|PDF »|Supplementary Materials »
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[DOI: 10.1126/stke.3812007eg2] |Abstract »|Full Text »|PDF »