Note to users. If you're seeing this message, it means that your browser cannot find this page's style/presentation instructions -- or possibly that you are using a browser that does not support current Web standards. Find out more about why this message is appearing, and what you can do to make your experience of our site the best it can be.
Sci. Signal., 29 March 2011
Vol. 4, Issue 166, p. pe16
[DOI: 10.1126/scisignal.2001948]
PERSPECTIVES
Resistance to MEK Inhibitors: Should We Co-Target Upstream?
Poulikos I. Poulikakos1 and
David B. Solit2,3*
1 Program in Molecular Pharmacology and Chemistry, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA. 2 Human Oncology and Pathogenesis Program, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA. 3 Department of Medicine, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
Abstract:
Aberrant activation of the ERK pathway is common in human tumors. This pathway consists of a three-tiered kinase module [comprising the kinases RAF, mitogen-activated protein kinase (MAPK) kinase (MEK), and extracellular signal–regulated kinase (ERK)] that functions as a negative feedback amplifier to confer robustness and stabilization of pathway output. Because this pathway is frequently dysregulated in human cancers, intense efforts are under way to develop selective inhibitors of the ERK pathway as anticancer drugs. Although promising results have been reported in early trials for inhibitors of RAF or MEK, resistance invariably occurs. Amplification of the upstream oncogenic driver of ERK signaling has been identified as a mechanism for MEK inhibitor resistance in cells with mutant BRAF or KRAS. Increased abundance of the oncogenic driver (either KRAS or BRAF in the appropriate cellular context) in response to prolonged drug treatment results in increased flux through the ERK pathway and restoration of ERK activity above the threshold required for cell growth. For patients with BRAF mutant tumors, the results suggest that the addition of a RAF inhibitor to a MEK inhibitor may delay or overcome drug resistance. The data thus provide a mechanistic basis for ongoing trials testing concurrent treatment with RAF and MEK inhibitors.
Annette S. Little, Kathryn Balmanno, Matthew J. Sale, Scott Newman, Jonathan R. Dry, Mark Hampson, Paul A. W. Edwards, Paul D. Smith, and Simon J. Cook (29 March 2011) Sci. Signal.4 (166), ra17.
[DOI: 10.1126/scisignal.2001752] |Editor's Summary »|Abstract »|Full Text »|PDF »|Supplementary Materials »
EDITORIAL GUIDES
Elizabeth M. Adler and Nancy R. Gough (29 March 2011) Sci. Signal.4 (166), eg3.
[DOI: 10.1126/scisignal.2002014] |Abstract »|Full Text »|PDF »
RESEARCH ARTICLES
Oliver E. Sturm, Richard Orton, Joan Grindlay, Marc Birtwistle, Vladislav Vyshemirsky, David Gilbert, Muffy Calder, Andrew Pitt, Boris Kholodenko, and Walter Kolch (21 December 2010) Sci. Signal.3 (153), ra90.
[DOI: 10.1126/scisignal.2001212] |Editor's Summary »|Abstract »|Full Text »|PDF »|Supplementary Materials »
Jocelyn Kaiser (25 March 2011) Science331 (6024), 1542.
[DOI: 10.1126/science.331.6024.1542] |Summary »|Full Text »|PDF »
THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES:
Development of Therapeutic Combinations Targeting Major Cancer Signaling Pathways.
T. A. Yap, A. Omlin, and J. S. de Bono (2013)
J. Clin. Oncol.
31, 1592-1605
|Abstract »|Full Text »|PDF »
Fendiline Inhibits K-Ras Plasma Membrane Localization and Blocks K-Ras Signal Transmission.
D. van der Hoeven, K.-j. Cho, X. Ma, S. Chigurupati, R. G. Parton, and J. F. Hancock (2013)
Mol. Cell. Biol.
33, 237-251
|Abstract »|Full Text »|PDF »
Network-based drug discovery by integrating systems biology and computational technologies.
E. L. Leung, Z.-W. Cao, Z.-H. Jiang, H. Zhou, and L. Liu (2012)
Brief Bioinform
|Abstract »|Full Text »|PDF »
Bridging the Gap between Preclinical and Clinical Studies Using Pharmacokinetic-Pharmacodynamic Modeling: An Analysis of GDC-0973, a MEK Inhibitor.
H. Wong, L. Vernillet, A. Peterson, J. A. Ware, L. Lee, J.-F. Martini, P. Yu, C. Li, G. D. Rosario, E. F. Choo, et al. (2012)
Clin. Cancer Res.
18, 3090-3099
|Abstract »|Full Text »|PDF »
Computational Approaches for Analyzing Information Flow in Biological Networks.