Integrating network reconstruction with mechanistic modeling to predict cancer therapies

Title: Integrating network reconstruction with mechanistic modeling to predict cancer therapies
Authors: Halasz, Melinda
Kholodenko, Boris N.
Kolch, Walter
Santra, Tapesh
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Date: 22-Nov-2016
Online since: 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.
Funding Details: 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
Keywords: Network inferenceCancer therapiesMechanistic modelingSignal transduction pathwaysModular response analysisBayesian inferenceDrug resistanceSignal transduction networks
DOI: 10.1126/scisignal.aae0535
Language: en
Status of Item: Peer reviewed
Appears in Collections:Conway Institute Research Collection
SBI Research Collection
Medicine Research Collection

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