Abstract 3478: Integrative metabolomic and genomic analysis of glioblastoma
Authors: Dayalan Antony , Kesarwani Pravin , Kant Shiva , Chinnaiyan Prakash 
Published: August 2018

Abstract
Normal 0 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin;} Despite considerable progress in understanding the molecular alterations in Glioblastoma (GBM), the diverse metabolic programs driving their aggressive phenotype remains unclear. We performed global metabolomic profiling in patient-derived GBM (n=80) and low-grade glioma (LGG; n=28). Hierarchical clustering of profiles identified clear metabolic programs differentiating LGG from GBM. GBM had an accumulation of metabolites that appeared mutually exclusive compared to LGG, a majority of which was involved in lipid and peptide metabolism. We next examined metabolic heterogeneity within GBM. Hierarchical clustering identified unique metabolic subtypes in GBM. The first subtype, which represented &lt;10% of the tumors analyzed, was defined by an accumulation of lysolipids, a second subtype, by alterations in amino acid, nucleotide, and lipid metabolism. The third subtype, which comprised the majority of tumors, had a unique accumulation of dipeptides in addition to a heterogeneous accumulation of the metabolites of the first two subtypes. To understand the molecular underpinnings of the metabolic heterogeneity in GBM, integrative analysis using gene expression profiles of matched tumors was performed. Preliminary analyses determined that metabolic heterogeneity in GBM is associated with known molecular subtypes, demonstrating mutual exclusivity of the proneural and mesenchymal metabolic profiles, consistent with their molecular signatures. MGMT methylated and IDH mutated tumors were evenly distributed within the metabolic subtypes, indicating that conserved metabolic programs associated with phenotypic changes are required for gliomagenesis. As aberrant fatty acid metabolism, amino acid metabolism, and accumulation of dipeptides represented core metabolic pathways differentiating LGG from GBM and appeared to contribute towards metabolic heterogeneity, we studied these pathways in further detail. Integrated cross-platform analyses uncovered a tightly orchestrated and highly redundant transcriptional program designed to drive the observed metabolic phenotype, also observed in preclinical models. Collectively, integrated metabolomic and genomic analyses helps in both, understanding biologic processes associated with gliomagenesis and identification of novel therapeutic targets.
Citation Format: Antony Dayalan, Pravin Kesarwani, Shiva Kant, Prakash Chinnaiyan. Integrative metabolomic and genomic analysis of glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3478.