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Maximal entropy inference of oncogenicity from phosphorylation signaling

Graeber, T. G. and Heath, J. R. and Skaggs, B. J. and Phelps, M. E. and Remacle, F. and Levine, R. D. (2010) Maximal entropy inference of oncogenicity from phosphorylation signaling. Proceedings of the National Academy of Sciences of the United States of America, 107 (13). pp. 6112-6117. ISSN 0027-8424. PMCID PMC2851899.

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Point mutations in the phosphorylation domain of the Bcr-Abl fusion oncogene give rise to drug resistance in chronic myelogenous leukemia patients. These mutations alter kinase-mediated signaling function and phenotypic outcome. An information theoretic analysis of the correlation of phosphoproteomic profiling and transformation potency of the oncogene in different mutants is presented. The theory seeks to predict the leukemic transformation potency from the observed signaling by constructing a distribution of maximal entropy of site-specific phosphorylation events. The theory is developed with special reference to systems biology where high throughput measurements are typical. We seek sets of phosphorylation events most contributory to predicting the phenotype by determining the constraints on the signaling system. The relevance of a constraint is measured by how much it reduces the value of the entropy from its global maximum, where all events are equally likely. Application to experimental phospho-proteomics data for kinase inhibitor-resistant mutants shows that there is one dominant constraint and that other constraints are not relevant to a similar extent. This single constraint accounts for much of the correlation of phosphorylation events with the oncogenic potency and thereby usefully predicts the trends in the phenotypic output. An additional constraint possibly accounts for biological fine structure.

Item Type:Article
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Heath, J. R.0000-0001-5356-4385
Additional Information:© 2010 by the National Academy of Sciences. Contributed by Michael E. Phelps, January 29, 2010 (sent for review December 7, 2009). Published online before print March 11, 2010. We are grateful to Professors Amos Golan and Nathan Price who acted as the internal referees of this paper. Professor Michael Fisher kindly commented on the draft manuscript. Funding for the experimental research was provided by National Institutes of Health National Human Genome Research Institute Grant HG002807 (to T.G.G.) and National Cancer Institute Grant 5U54 CA119347 (to T.G.G., J.R.H., and M.E.P.; J.R.H., principal investigator). Author contributions: T.G.G., F.R., and R.D.L. designed research; T.G.G., J.R.H., B.J.S., M.E.P., F.R., and R.D.L. analyzed data; F.R. and R.D.L. performed research; and T.G.G., J.R.H., M.E.P., F.R., and R.D.L. wrote the paper.
Funding AgencyGrant Number
National Cancer Institute5U54 CA119347
Subject Keywords:high-throughput measurements; information theory; phospho proteomics; signal transduction networks; systems biology
Issue or Number:13
PubMed Central ID:PMC2851899
Record Number:CaltechAUTHORS:20100521-111915396
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:18388
Deposited By: Tony Diaz
Deposited On:07 Jun 2010 19:33
Last Modified:03 Oct 2019 01:42

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