A Thermodynamic-Based Interpretation of Protein Expression Heterogeneity in Different Glioblastoma Multiforme Tumors Identifies Tumor-Specific Unbalanced Processes

We describe a thermodynamic-motivated, information theoretic analysis of proteomic data collected from a series of 8 glioblastoma multiforme (GBM) tumors. GBMs are considered here as prototypes of heterogeneous cancers. That heterogeneity is viewed here as manifesting in different unbalanced biologi...

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Bibliographic Details
Main Authors: Kravchenko-Balasha, Nataly (Author), Johnson, Hannah (Author), Heath, James R. (Author), Levine, R. D. (Author), White, Forest M (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Biological Engineering (Contributor)
Format: Article
Language:English
Published: American Chemical Society (ACS), 2017-08-03T13:52:39Z.
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Online Access:Get fulltext
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100 1 0 |a Kravchenko-Balasha, Nataly  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Biological Engineering  |e contributor 
100 1 0 |a White, Forest M  |e contributor 
700 1 0 |a Johnson, Hannah  |e author 
700 1 0 |a Heath, James R.  |e author 
700 1 0 |a Levine, R. D.  |e author 
700 1 0 |a White, Forest M  |e author 
245 0 0 |a A Thermodynamic-Based Interpretation of Protein Expression Heterogeneity in Different Glioblastoma Multiforme Tumors Identifies Tumor-Specific Unbalanced Processes 
260 |b American Chemical Society (ACS),   |c 2017-08-03T13:52:39Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/110914 
520 |a We describe a thermodynamic-motivated, information theoretic analysis of proteomic data collected from a series of 8 glioblastoma multiforme (GBM) tumors. GBMs are considered here as prototypes of heterogeneous cancers. That heterogeneity is viewed here as manifesting in different unbalanced biological processes that are associated with thermodynamic-like constraints. The analysis yields a molecular description of a stable steady state that is common across all tumors. It also resolves molecular descriptions of unbalanced processes that are shared by several tumors, such as hyperactivated phosphoprotein signaling networks. Further, it resolves unbalanced processes that provide unique classifiers of tumor subgroups. The results of the theoretical interpretation are compared against those of statistical multivariate methods and are shown to provide a superior level of resolution for identifying unbalanced processes in GBM tumors. The identification of specific constraints for each GBM tumor suggests tumor-specific combination therapies that may reverse this imbalance. 
546 |a en_US 
655 7 |a Article 
773 |t Journal of Physical Chemistry B