Identification of Metabolomic Markers in Frozen or Formalin-Fixed and Paraffin-Embedded Samples of Diffuse Glioma from Adults

Author:

Chardin David12ORCID,Jing Lun1ORCID,Chazal-Ngo-Mai Mélanie3,Guigonis Jean-Marie1,Rigau Valérie4,Goze Catherine5,Duffau Hugues6ORCID,Virolle Thierry7ORCID,Pourcher Thierry1ORCID,Burel-Vandenbos Fanny38ORCID

Affiliation:

1. Laboratory Transporter in Imaging and Radiotherapy in Oncology (TIRO), Direction de la Recherche Fondamentale (DRF), Institut des Sciences du Vivant Frederic Joliot, Commissariat a l’Energie Atomique et aux Energies Alternatives (CEA), Université Cote d’Azur (UCA), 06000 Nice, France

2. Service de Médecine Nucléaire, Centre Antoine Lacassagne, Université Cote d’Azur, 06000 Nice, France

3. Department of Pathology, University Hospital of Nice, 06000 Nice, France

4. Department of Pathology and Oncobiology, Institute for Neurosciences of Montpellier, INSERM U1051, University Hospital of Montpellier, 34000 Montpellier, France

5. Laboratory of Solid Tumors Biology, Institute for Neurosciences of Montpellier, INSERM U1051, University Hospital of Montpellier, 34000 Montpellier, France

6. Neurosurgery Department, Institute for Neurosciences of Montpellier, INSERM U1051, University Hospital of Montpellier, 34000 Montpellier, France

7. Team INSERM “Cancer Stem Cell Plasticity and Functional Intra-Tumor Heterogeneity”, Institut de Biologie Valrose, Université Côte D’Azur, CNRS, INSERM, 06000 Nice, France

8. Laboratory “Cancer Stem Cell Plasticity and Functional Intra-Tumor Heterogeneity”, UMR CNRS 7277-UMR INSERM 1091, Institute of Biology Valrose, University Côte d’Azur, 06000 Nice, France

Abstract

The aim of this study was to identify metabolomic signatures associated with the gliomagenesis pathway (IDH-mutant or IDH-wt) and tumor grade of diffuse gliomas (DGs) according to the 2021 WHO classification on frozen samples and to evaluate the diagnostic performances of these signatures in tumor samples that are formalin-fixed and paraffin-embedded (FFPE). An untargeted metabolomic study was performed using liquid chromatography/mass spectrometry on a cohort of 213 DG samples. Logistic regression with LASSO penalization was used on the frozen samples to build classification models in order to identify IDH-mutant vs. IDH-wildtype DG and high-grade vs low-grade DG samples. 2-Hydroxyglutarate (2HG) was a metabolite of interest to predict IDH mutational status and aminoadipic acid (AAA) and guanidinoacetic acid (GAA) were significantly associated with grade. The diagnostic performances of the models were 82.6% AUC, 70.6% sensitivity and 80.4% specificity for 2HG to predict IDH status and 84.7% AUC, 78.1% sensitivity and 73.4% specificity for AAA and GAA to predict grade from FFPE samples. Thus, this study showed that AAA and GAA are two novel metabolites of interest in DG and that metabolomic data can be useful in the classification of DG, both in frozen and FFPE samples.

Publisher

MDPI AG

Subject

Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis

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