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 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.