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Targeted therapy in GIST: in silico modeling for prediction of resistance

Nature Reviews Clinical Oncology volume 8, pages 161–170 (2011)Cite this article

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Abstract

Elucidation of the genetic processes leading to neoplastic transformation has identified cancer-promoting molecular alterations that can be selectively targeted by rationally designed therapeutic agents. Protein kinases are druggable targets and have been studied intensively. New methodologies—including crystallography and three-dimensional modeling—have allowed the rational design of potent and selective kinase inhibitors that have already reached the clinical stage. However, despite the clinical success of kinase-targeted therapies, most patients that respond eventually relapse as a result of acquired resistance. Darwinian-type selection of secondary mutations seems to have a major role in this resistance. The emergence and/or expansion of tumor clones containing new mutations in the target kinase and that are drug-insensitive have been observed after chronic treatment. The resistance mechanisms to tyrosine kinase inhibitors, in particular secondary resistant mutations as a consequence of treatment, will be discussed in detail. In particular, this Review will focus on KIT and PDGFRA mutations, which are involved in the pathogenesis of gastrointestinal stromal tumors. Harnessing the selection of mutated variants developed to overcome these resistance mechanisms is an ongoing goal of current research and new strategies to overcome drug resistance is being envisaged.

Key Points

  • The pathogenetic role of constitutively activated receptor tyrosine kinases (RTKs) and drugs that specifically target this alteration in cancer has provided a new therapeutic opportunity

  • Despite encouraging early therapeutic results, the development of resistance can occur after a variable period of chronic treatment

  • The emergence of secondary mutations that affect the tyrosine kinase domain of RTKs reduce the drug binding affinity to the enzymatic pocket of the receptor; this resistance can be overcome by the development of drugs that bind efficiently the new mutated RTK forms

  • A new in silico approach is consistent with both biochemical and molecular data and patient clinical outcome and could support clinical decisions to increase the drug dose or administer a different drug

  • In silico molecular modeling can be used to predict the occurrence of all activating but drug-resistant secondary mutations and to develop a multi-drug targeted prevention strategy

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Figure 1: KIT core conformations.
Figure 2: KIT and PDGFRA mutations and correllation to protein structure.
Figure 3: Imaging, biochemical and molecular evidence and modeling of KIT.
Figure 4: Clinical prediction of treatment outcomes with molecular modeling.

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Acknowledgements

We wish to thank all the INT clinical staff who made this multidisciplinary investigation of GIST possible, in particular Dr. Paolo G. Casali and Dr. Alessandro Gronchi. A special thanks goes to Dr. Elena Fumagalli who answered to all our clinical questions. The authors are partially funded by the Associazione Italiana per la Ricerca sul Cancro (AIRC).

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Authors and Affiliations

  1. Scientific Directorate, Fondazione IRCCS Istituto Nazionale dei Tumori-Milano, via Venezian 1, 20133, Milano, Italy

    Marco A. Pierotti

  2. Laboratory of Experimental Molecular Pathology, Fondazione IRCCS Istituto Nazionale dei Tumori-Milano, via Venezian 1, 20133, Milano, Italy

    Elena Tamborini, Tiziana Negri & Silvana Pilotti

  3. Molecular Simulation Engineering Laboratory, DI3, University of Trieste, Piazzale Europa 1, 34127, Trieste, Italy

    Sabrina Pricl

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  1. Marco A. Pierotti
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M. A. Pierotti, E. Tamborini, T. Negri, S. Pricl and S. Pilotti contributed equally to the literature review, discussions of the content, writing the article and to review and/or editing of the manuscript before submission.

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Correspondence to Sabrina Pricl.

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Pierotti, M., Tamborini, E., Negri, T. et al. Targeted therapy in GIST: in silico modeling for prediction of resistance. Nat Rev Clin Oncol 8, 161–170 (2011). https://doi.org/10.1038/nrclinonc.2011.3

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