Abstract 3721: Improved fitting of HRMAS NMR spectra for ex vivo metabolomic analysis of glioma tissue

Abstract

Abstract Gliomas are aggressive brain tumors with high rates of treatment resistance and low survival rates. High-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy can quantify metabolite concentrations in glioma tissue, but most analysis techniques require manual peak selection and integration that prevents accurate and reliable quantitation of overlapping peaks and large macromolecule baselines. Our goal was to compare the effectiveness of operator-independent LCModel analysis of glioma spectra acquired from free induction decay (FID) and Carr-Purcell-Meiboom-Gill (CPMG) pulse sequences. HRMAS NMR spectra were acquired using FID and CPMG sequences from 14 histologically-confirmed glioma tissue samples (WHO grade II (n=5), III (n=5), and IV (n=4)) collected during surgical resection from human brain tumor patients. Metabolite concentration ratios and Cramer-Rao lower bounds (CRLBs) were estimated using LCModel. Metabolite CRLBs were compared using a paired two sample t-test. Differences in concentrations as a function of WHO grade were determined with ANOVA and Tukey-Kramer post-hoc tests. Significance was determined by p<.05. Metabolite CRLBs for lactate and myo-inositol were significantly lower for CPMG compared to FID spectra (p<.05). Most metabolites could be quantified with LCModel from spectra acquired with CPMG where many metabolites acquired with the FID sequence were not detected. For example, lactate was quantifiable from 3 of the spectra acquired with FID compared to 12 spectra with CPMG. The use of the CPMG sequence reduces quantification errors by eliminating confounding baseline signals. LCModel facilitates improved separation of overlapping resonances compared to manual peak integration, supporting the use of operator-independent methods for metabolic spectral analysis. Comparison of metabolite concentrations as a function of WHO grade revealed significant differences in lactate and glutamine plus glutamate normalized to creatine (p<.05). 2-hydroxyglutarate (2-HG) was also detected in 9 out of 13 isocitrate dehydrogenase (IDH)-mutated samples using both sequences. IDH-mutated tissue should produce 2-HG; however, these results are promising as 2-HG can be difficult to quantify in 1D NMR due to spectral overlap. In conclusion, LCModel can reliably quantify HRMAS spectra acquired with the CPMG sequence but is less reliable with the FID sequence. Increases in lactate and glutamine plus glutamate concentrations as a function of tumor grade were consistent with previous results using HRMAS for glioma metabolic analysis, and 2-HG was detected in 1D HRMAS spectra acquired with both sequences. We expect that improved spectral fitting will contribute to future NMR-based metabolomics studies in glioma. Note: This abstract was not presented at the meeting. Citation Format: Selin Ekici, Ren Geryak, Stewart G. Neill, Hui-Kuo Shu, Candace C. Fleischer. Improved fitting of HRMAS NMR spectra for ex vivo metabolomic analysis of glioma tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3721.