Abstract 3721: Improved fitting of HRMAS NMR spectra forex vivometabolomic analysis of glioma tissue

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 Metabolite CRLBs for lactate and myo-inositol were significantly lower for CPMG compared to FID spectra (p Comparison of metabolite concentrations as a function of WHO grade revealed significant differences in lactate and glutamine plus glutamate normalized to creatine (p 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.