NIMG-69. INTERROGATION OF GLIOBLASTOMA FATTY-ACID SYNTHESIS AND METABOLISM WITH DEUTERIUM METABOLIC IMAGING

Abstract Glioblastoma, an invariably lethal brain tumor, undergoes marked metabolic alterations during tumorigenesis and is extraordinarily difficult to treat. Detecting recurrence after treatment and understanding metabolic alterations within the tumor microenvironment are extremely challenging. An exciting and emerging imaging methodology, deuterium metabolic imaging (DMI), detects metabolic adaptations via non-radioactively labeled deuterated substrates. This study developed approaches to image lipid synthesis with deuterium oxide (D2O) and metabolism with D31-palmitate (D31-Pal) in glioblastoma. A fatty-acid phantom was generated for characterization of lipid DMI signal at 12 T. DMI was performed in mice orthotopically implanted with GL261 glioblastoma utilizing either orally administered D31-Pal or D2O and was correlated with pharmacokinetic metabolic measurements from mouse serum. Mice were either orally gavaged with an emulsion of D31-Pal prior to imaging or provided 16% D2O for ad libitum consumption following tumor implantation. Although D2O administration resulted in robust enrichment of lipid signal in GBM, D31-Pal uptake and metabolism were not significantly detected within intracranial tumors. Interestingly, D31-Pal uptake and metabolism were detected in normal tissues, indicating preferential utilization of fatty acids in non-neoplastic tissues. These findings suggest de novo lipogenesis within GBM, rather than uptake of administered long chain fatty acids, is the preferential pathway for cellular lipids. DMI can assist in interrogating GBM fatty-acid synthesis and metabolism, as well as furthering our understanding of lipid distribution within the body by providing a non-invasive scaffold for dynamic interrogation of fatty-acid distribution in an oncologic model. Developing a non-invasive method for characterization of tumor fatty-acid synthesis and metabolism may improve our understanding of tumor metabolism and could dynamically guide selection of patient-tailored metabolic therapies in the future.