Localized metabolomic gradients in patient-derived xenograft models of glioblastoma

Glioblastoma (GBM) is increasingly recognized as a disease involving dysfunctional cellular metabolism.GBMs are known to be complex, heterogeneous systems containing multiple distinct cell populationssupplied by an aberrant network of blood vessels. A better understanding of glioblastoma metabolism,its variation with respect to the tumor microenvironment and resulting regional changes in chemicalcomposition is required and may shed light on observed heterogeneous drug distribution which cannotbe fully described by limited or uneven disruption of the blood brain barrier. In this work we use massspectrometry imaging (MSI) to map metabolites and lipids in patient-derived xenograft models ofglioblastoma. A data analysis workflow revealed that distinctive spectral signatures were detected fromdifferent regions of the intracranial tumor model. A series of long chain acylcarnitines were identifiedand detected with increased intensity at the tumor edge. A 3D MSI dataset demonstrated that thesemolecules were observed throughout the entire tumor/normal interface and not confined to a singleplane. mRNA sequencing demonstrated that Hallmark genes related to fatty acid metabolism were morehighly expressed in samples with higher acyl-carnitine content. These data suggest that cells in the coreand the edge of the tumor undergo different fatty acid metabolism, resulting in different chemicalenvironments within the tumor which may influence drug distribution through changes in tissue drugaffinity or transport, and constitute an important consideration for therapeutic strategies in thetreatment of GBM.