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Integrating network reconstruction with mechanistic modeling to predict cancer therapies
Date Issued
2016-11-22
Date Available
2019-04-01T10:14:05Z
Abstract
Signal transduction networks (STNs) are often rewired in cancerous cells. Effective cancer treatment requires identifying and repairing these harmful alterations. We developed a computational framework which can identify these aberrations and predict potential targets for intervention. It reconstructs network models of STNs from noisy and incomplete perturbation response data, and then uses the reconstructed networks to develop mechanistic models of STNs for predicting potential treatments. As a proof of principle, we analysed a perturbation dataset targeting Epidermal Growth Factor Receptor (EGFR) and Insulin like 2 Growth Factor 1 Receptor (IGF1R) pathways in a panel of colorectal cancer (CRC) cells, revealing cell line specific STN rewiring. Specifically, we found that the feedback inhibition of IRS1 by p70S6K is associated with resistance to EGF receptor (EGFR) inhibition, and disrupting this feedback may restore sensitivity to EGFR inhibitors in CRC cells. These findings were experimentally validated in vitro and in zebrafish (Danio rerio) xenografts.
Sponsorship
European Commission - Seventh Framework Programme (FP7)
Science Foundation Ireland
Type of Material
Journal Article
Publisher
American Association for the Advancement of Science
Journal
Science Signaling
Volume
9
Issue
455
Copyright (Published Version)
2016 the American Association for the Advancement of Science
Language
English
Status of Item
Peer reviewed
This item is made available under a Creative Commons License
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Integrating network reconstruction with mechanistic modelling to predict potential cancer therapy.pdf
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1.73 MB
Format
Owning collection
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