Please use this identifier to cite or link to this item:
https://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 |
Editor: | Bendall, L. |
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. |
DOI: | 10.1371/journal.pone.0077155 |
Published version: | http://dx.doi.org/10.1371/journal.pone.0077155 |
Appears in Collections: | Aurora harvest 2 Medicine publications |
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hdl_93363.pdf | Published version | 1.43 MB | Adobe PDF | View/Open |
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