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Combination of a Proteomics Approach and Reengineering of Meso Scale Network Models for Prediction of Mode-of-Action for Tyrosine Kinase Inhibitors

Author

Listed:
  • Stefan Balabanov
  • Thomas Wilhelm
  • Simone Venz
  • Gunhild Keller
  • Christian Scharf
  • Heike Pospisil
  • Melanie Braig
  • Christine Barett
  • Carsten Bokemeyer
  • Reinhard Walther
  • Tim H Brümmendorf
  • Andreas Schuppert

Abstract

In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs.

Suggested Citation

  • Stefan Balabanov & Thomas Wilhelm & Simone Venz & Gunhild Keller & Christian Scharf & Heike Pospisil & Melanie Braig & Christine Barett & Carsten Bokemeyer & Reinhard Walther & Tim H Brümmendorf & And, 2013. "Combination of a Proteomics Approach and Reengineering of Meso Scale Network Models for Prediction of Mode-of-Action for Tyrosine Kinase Inhibitors," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-14, January.
  • Handle: RePEc:plo:pone00:0053668
    DOI: 10.1371/journal.pone.0053668
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    References listed on IDEAS

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    1. Judith S. Sebolt-Leopold & Jessie M. English, 2006. "Mechanisms of drug inhibition of signalling molecules," Nature, Nature, vol. 441(7092), pages 457-462, May.
    2. Alexander Mitsos & Ioannis N Melas & Paraskeuas Siminelakis & Aikaterini D Chairakaki & Julio Saez-Rodriguez & Leonidas G Alexopoulos, 2009. "Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-11, December.
    3. Ramesh Ummanni & Frederike Mundt & Heike Pospisil & Simone Venz & Christian Scharf & Christine Barett & Maria Fälth & Jens Köllermann & Reinhard Walther & Thorsten Schlomm & Guido Sauter & Carsten Bok, 2011. "Identification of Clinically Relevant Protein Targets in Prostate Cancer with 2D-DIGE Coupled Mass Spectrometry and Systems Biology Network Platform," PLOS ONE, Public Library of Science, vol. 6(2), pages 1-14, February.
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