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Sci Rep. 2015 Feb 3;5:8190. doi: 10.1038/srep08190.

Drug target optimization in chronic myeloid leukemia using innovative computational platform.

Author information

1
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge CB3 0WA, UK.
2
1] Microsoft Research, Cambridge CB1 2FB, UK [2] MRC Cancer Unit, University of Cambridge, Cambridge, CB2 0XZ, UK.
3
Microsoft Research, Cambridge CB1 2FB, UK.
4
1] Microsoft Research, Cambridge CB1 2FB, UK [2] Department of Computer Science, University College London, London, WC1E 6BT, UK.
5
Department of Computer Science, University of Leicester, Leicester, LE1 7RH, UK.
6
Department of Computer Science, Rice University, Huston 77005-1892, Texas.
7
Department of Medicine, University Hospital of Aachen, Aachen D-52074, Germany.
8
1] Cambridge Institute for Medical Research, University of Cambridge, Cambridge CB2 0XY, UK [2] Wellcome Trust and MRC Cambridge Stem Cell Institute, Cambridge CB2 0XY, UK.
9
1] Microsoft Research, Cambridge CB1 2FB, UK [2] Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK.

Abstract

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3.

PMID:
25644994
PMCID:
PMC4650822
DOI:
10.1038/srep08190
[Indexed for MEDLINE]
Free PMC Article

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