METB-05. IDENTIFICATION OF A PROGNOSTIC SEXUAL DIMORPHISM IN GLIOMA GLYCOLYSIS

Abstract

Sex differences are present in tumor incidence and mortality and independent of age or race. In many cancers, males not only have a higher incidence but also exhibit poorer response to therapy. In a recent study, both lower grade gliomas and glioblastomas were said to exhibit a "weak sex effect" based upon global trancriptome-level data obtained from The Cancer Genome Atlas (TCGA). Given known sex differences in metabolism and the established positive correlation between fluorodeoxyglucose (FDG) uptake and glioma grade and survival, we hypothesized that male gliomas would have enhanced glycolytic activity relative to females and therefore exhibit a "glycolytic" transcriptome. We re-analyzed low grade glioma data from TCGA, targeted to transcripts encoding hexose transport, glycolysis, and lactate export. We identified the presence of a robust sexual dimorphism in glycolysis where males with high glycolytic transcript expression (54% of all males) exhibited significantly poorer overall and progression free survival than females followed by males with low glycolytic transcript expression. Moreover, we discovered specific genomic associations associated with the male "high glycolytic" group. Specifically, the presence of IDH1 and TP53 mutations conferred significantly better survival within the male "high glycolytic" group and these same genetic alterations were not associated with differences in either the male "low glycolytic" or female groups. Moreover, deep deletions of CDKN2A, CDKN2B and MTAP were significantly enriched in the male "high glycolytic" group relative to the male "low glycolytic" group that conferred significantly worse survival. The transcriptome studies were validated with metabolome-level data of human glioma specimens demonstrating enhanced glycolytic metabolite levels in males relative to females. Together, our findings represent a previously uncharacterized phenomenon that may pave the way toward a "sex-based workflow" for stratification employing genomics, metabolomics and metabolic PET imaging.