P13.11 Metabolomics and molecular profiling in glioma patients: an interactomic approach

Abstract BACKGROUND A wider view of the interaction between different omic-domains is needed to identify potential biomarkers of low- and high-grade gliomas. Using an interactomic approach, we analyzed the correlation between radiological data, IDH mutation, gene expression profiling and metabolic signature in glioma samples. MATERIAL AND METHODS Tumor biopsies from 25 patients with clinical diagnosis of glioma were surgically collected during 2017–2019 at the senior author’s institution. Metabolomic data was obtained by high resolution 31P and 1H magnetic resonance spectroscopy (MRS, 19 metabolites quantified with LCModel). Gene expression profiling was performed using real-time qPCR of 19 genes related to energy metabolism. IDH1/2 common mutation (IDH1R132H/IDH2R172H) was verified by immunohistochemistry and amplicon sanger sequencing. All data was integrated using the R package mixOmics, and we built correlation network plot graphs and correlation maps to identify the most significant interactions, that were analyzed thereafter. RESULTS Mean age was 48±10 years and 72% were men. The most frequent clinical presentation was intracranial hypertension and focal deficit. Imaging revealed 88% of single lobar tumors, 96% of contrast enhancement, 52% located near eloquent areas, 48% with augmented perfusion (mean values of 300±130%) and 60% showed restricted diffusion. WHO 2016 diagnosis were glioblastoma IDH mutated (IDHmut, 16%), IDH wildtype (IDHw, 56%); anaplastic astrocytoma IDHmut (4%), IDHw (16%); diffuse astrocytoma IDHmut (4%), SEGA (4%). The genetic and metabolic profiles were normalized per sample using the total sum of all the studied variables per case. This step made the interactomic approach possible. We found no differences between the metabolic or genetic profiles of glioma grade III and IV samples. However, there was a statistical significance or near-threshold correlation between some metabolic patterns and IDH-mutation, where Alanine (4.7±1.3% IDHw vs 2.5±0.7 IDHmut, p=0.046), Glycine (2.7±0.5% vs. 1.6±0.4%, p=0.095), Glycerophosphorylcholine (3.9±0.4% vs. 6.4±0.9%, p=0.013) and Myo-inositol (4.9±1.0% vs 11.9±2.1%, p=0.004) were the most important biomarkers. Overexpression of Lactate Dehydrogenase subunit B (LDHB, 19±3% vs. 31±6%, p=0.039) and Aconitase 1 (ACO1, 0.5±0.1% vs 1.2±0.3%, p=0.08) had also a significant or near-threshold relationship with IDH-mutation. These correlations were shown as hot spots in the correlation graphs and maps.