Integrating network reconstruction with mechanistic modeling to predict cancer therapies

DC FieldValueLanguage
dc.contributor.authorHalasz, Melinda-
dc.contributor.authorKholodenko, Boris N.-
dc.contributor.authorKolch, Walter-
dc.contributor.authorSantra, Tapesh-
dc.date.accessioned2019-04-01T10:14:05Z-
dc.date.available2019-04-01T10:14:05Z-
dc.date.copyright2016 the American Association for the Advancement of Scienceen_US
dc.date.issued2016-11-22-
dc.identifier.citationScience Signalingen_US
dc.identifier.urihttp://hdl.handle.net/10197/9755-
dc.description.abstractSignal 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.en_US
dc.description.sponsorshipEuropean Commission - Seventh Framework Programme (FP7)en_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.language.isoenen_US
dc.publisherAmerican Association for the Advancement of Scienceen_US
dc.rightsThis is the author's version of the work. It is posted here by permission of the AAAS for personal use, not for redistribution. The definitive version was published in Science Signalling {9, (2016)}, doi:10.1126/scisignal.aae0535en_US
dc.subjectNetwork inferenceen_US
dc.subjectCancer therapiesen_US
dc.subjectMechanistic modelingen_US
dc.subjectSignal transduction pathwaysen_US
dc.subjectModular response analysisen_US
dc.subjectBayesian inferenceen_US
dc.subjectDrug resistanceen_US
dc.subjectSignal transduction networksen_US
dc.titleIntegrating network reconstruction with mechanistic modeling to predict cancer therapiesen_US
dc.typeJournal Articleen_US
dc.statusPeer revieweden_US
dc.identifier.volume9en_US
dc.identifier.issue455en_US
dc.identifier.doi10.1126/scisignal.aae0535-
dc.neeo.contributorHalasz|Melinda|aut|-
dc.neeo.contributorKholodenko|Boris N.|aut|-
dc.neeo.contributorKolch|Walter|aut|-
dc.neeo.contributorSantra|Tapesh|aut|-
dc.date.updated2017-12-05-
dc.identifier.grantid06/CE/B1129-
dc.identifier.grantid259348-2-
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:Conway Institute Research Collection
SBI Research Collection
Medicine Research Collection
Show simple item record

SCOPUSTM   
Citations 20

18
Last Week
1
Last month
checked on Jun 17, 2019

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.