The metabolomic signature of malignant glioma reflects accelerated anabolic metabolism

Abstract

Although considerable progress has been made toward understanding glioblastoma biology through large-scale genetic and protein expression analyses, little is known about the underlying metabolic alterations promoting their aggressive phenotype. We conducted global metabolomic profiling on patient-derived glioma specimens and identified specific metabolic programs differentiating low- and high-grade tumors, with the metabolic signature of glioblastoma reflecting accelerated anabolic metabolism. When coupled with transcriptional profiles, we identified the metabolic phenotype of the mesenchymal subtype to consist of accumulation of the glycolytic intermediate phosphoenolpyruvate and decreased pyruvate kinase activity. Unbiased hierarchical clustering of metabolomic profiles identified three subclasses, which we term energetic, anabolic, and phospholipid catabolism with prognostic relevance. These studies represent the first global metabolomic profiling of glioma, offering a previously undescribed window into their metabolic heterogeneity, and provide the requisite framework for strategies designed to target metabolism in this rapidly fatal malignancy.

Figure 1

Global metabolomic profiling in glioma identifies grade specific metabolic changes. A, a combination of high-throughput LC- and GC–based MS was conducted on a total of 69 fresh-frozen glioma specimens (grades and histologies are provided in Supplementary Table S1). From a metabolomic library consisting of more than 2,000 purified standards, a total of 308 named biochemicals were detected, and the metabolic pathways that the identified metabolites reside in are presented. B, following log transformation and imputation with minimum observed values for each compound, Welch 2-sample t tests were used to identify biochemicals that differed significantly between histologic grades. Summaries of the numbers of biochemicals that achieve statistical significance (P ≤ 0.05) are provided. C, a summary of the metabolic pathways differentiating grade 4 and 2 tumors, with red and green indicating a statistically significant increase and decrease in identified metabolic pathways, respectively. Gly, glycine; Ser, serine; Thr, threonine; Ala, alanine; Asp, aspartate; Lys, lysine; Phe, phenylalanine, Trp, tryptophan; Val, valine; Leu, leucine; Ile, isoleucine; Cys, cysteine; GSH, glutathione; FA, fatty acids.

Figure 2

RF analysis identifies key metabolites differentiating glioma grade. RF analysis was conducted to determine the capacity of global metabolic profiles to classify samples between tumor grades and to identify biochemicals important to the classification; the top 30 biochemicals for the RF classification scheme are provided.

Figure 3

The metabolic signature of glioblastoma reflects accelerated anabolic metabolism. Schematic of metabolites involved in carbohydrate metabolism, comparing grade 4 with grade 2 glioma. Metabolites in red reflect an increase accumulation in grade 4 tumors; green, decrease; black, no change; and gray, not identified in this analysis. Ratios were generated (in parenthesis) by normalizing the individual metabolite concentration to the median concentration of the respective metabolite obtained from all samples.

Figure 4

The mesenchymal subtype of malignant glioma is characterized by PEP accumulation and altered PKM2 expression and activity. Malignant glioma tumors (n = 51) were classified as mesenchymal (mes; n = 20), proneural (PN; n = 22), and proliferative (PR; n = 9) based on their transcriptional profiles (Supplementary Fig. S3). A, box-plots were generated from the PEP ratios of tumor clustering to individual subtypes. Band C, of the initial 51 malignant glioma samples used for global metabolomic and transcriptional profiling, 23 samples had sufficient tissue remaining to evaluate for differential PK activity and PKM2 expression between subtypes (mes, n = 8; PN, n = 15). PK activity was determined using a calorimetric-based assay and PKM2 expression was determined by Western blot analysis, with expression quantified relative to β-actin.

Figure 5

Global metabolism reveals metabolic signatures in malignant glioma. A, unsupervised, hierarchical clustering was conducted on global metabolomic profiles generated in malignant glioma (n = 51). B, glioma grade and transcriptional subtypes of tumors clustering to the identified metabolomic subtypes. C, Kaplan–Meier estimates for overall survival based on metabolomic subtype.