Coupling constrained-based flux sampling and clustering to tackle cancer metabolic heterogeneity

Characterizing the heterogeneity of cancer metabolism requires the knowledge of metabolic fluxes in different tumor types. These fluxes cannot be directly determined, especially at a sub-cellular level. Still, they can be obtained numerically through constraint-based steady-state models after integrating other high-throughput -omics data, such as transcriptomics. In this work, we proposed to study cancer metabolism through data analysis and machine learning methodologies. To this aim, we considered transcriptomics profiles for a large set of cancer cells. Using a core metabolic network as a scaffold, we generated many feasible flux distributions for each cancer cell. Then, we used cluster analysis to analyze these data. This preliminary analysis revealed three well-separated clusters having different metabolic behaviors.