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dc.contributor.authorRix, U.-
dc.contributor.authorColinge, J.-
dc.contributor.authorBlatt, K.-
dc.contributor.authorGridling, M.-
dc.contributor.authorRemsing Rix, L.-
dc.contributor.authorParapatics, K.-
dc.contributor.authorCerny-Reiterer, S.-
dc.contributor.authorBurkard, T.-
dc.contributor.authorJäger, U.-
dc.contributor.authorMelo, J.-
dc.contributor.authorBennett, K.-
dc.contributor.authorValent, P.-
dc.contributor.authorSuperti-Furga, G.-
dc.identifier.citationPLoS One, 2013; 8(10):e77155-1-e77155-14-
dc.description.abstractPhiladelphia chromosome-positive acute lymphoblastic leukemia (Ph+ ALL) is in part driven by the tyrosine kinase bcr-abl, but imatinib does not produce long-term remission. Therefore, second-generation ABL inhibitors are currently in clinical investigation. Considering different target specificities and the pronounced genetic heterogeneity of Ph+ ALL, which contributes to the aggressiveness of the disease, drug candidates should be evaluated with regard to their effects on the entire Ph+ ALL-specific signaling network. Here, we applied an integrated experimental and computational approach that allowed us to estimate the differential impact of the bcr-abl inhibitors nilotinib, dasatinib, Bosutinib and Bafetinib. First, we determined drug-protein interactions in Ph+ ALL cell lines by chemical proteomics. We then mapped those interactions along with known genetic lesions onto public protein-protein interactions. Computation of global scores through correlation of target affinity, network topology, and distance to disease-relevant nodes assigned the highest impact to dasatinib, which was subsequently confirmed by proliferation assays. In future, combination of patient-specific genomic information with detailed drug target knowledge and network-based computational analysis should allow for an accurate and individualized prediction of therapy.-
dc.description.statementofresponsibilityUwe Rix, a, Jacques Colinge, Katharina Blatt, Manuela Gridling, Lily L. Remsing Rix, a, Katja Parapatics, Sabine Cerny-Reiterer, Thomas R. Burkard, Ulrich Jäger, Junia V. Melo, Keiryn L. Bennett, Peter Valent, Giulio Superti-Furga-
dc.publisherPublic Library of Science-
dc.rights© 2013 Rix et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.-
dc.subjectMolecular Targeted Therapy-
dc.titleA target-disease network model of second-generation BCR-ABL inhibitor action in Ph+ ALL-
dc.typeJournal article-
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