Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/93363
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Type: Journal article
Title: A target-disease network model of second-generation BCR-ABL inhibitor action in Ph+ ALL
Author: Rix, U.
Colinge, J.
Blatt, K.
Gridling, M.
Remsing Rix, L.
Parapatics, K.
Cerny-Reiterer, S.
Burkard, T.
Jäger, U.
Melo, J.
Bennett, K.
Valent, P.
Superti-Furga, G.
Citation: PLoS One, 2013; 8(10):e77155-1-e77155-14
Publisher: Public Library of Science
Issue Date: 2013
ISSN: 1932-6203
1932-6203
Statement of
Responsibility: 
Uwe 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
Abstract: Philadelphia 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.
Keywords: Molecular Targeted Therapy
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.
RMID: 0030027411
DOI: 10.1371/journal.pone.0077155
Appears in Collections:Medicine publications

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