Metabolomic Phenotyping of Gliomas: What Can We Get with Simplified Protocol for Intact Tissue Analysis?

SIMPLE SUMMARY: The diagnostic protocol for gliomas is based on histological examination and the determination of genetic biomarkers. However, examining molecular biomarkers in cancer tissue is usually labor-intensive and time-consuming when a homogenization step is involved. Therefore, this diagnostic approach has not been fully explored to date. The present study seeks to validate the applicability of solid-phase microextraction (SPME), or chemical biopsy, as a new approach for fast and simple sampling and sample-preparation in the surgery room prior to the application of metabolomic analysis to identify biomarkers. To this end, the metabolomic profiles of brain tumors were compared with genetic biomarkers and the results of histological analysis in order to identify changes of molecular metabolites of statistical significance. The findings of this study indicate that the proposed approach provides complementary information to current diagnostic methods and has the potential to be a valuable on-site analytical tool in future applications. ABSTRACT: Glioblastoma multiforme is one of the most malignant neoplasms among humans in their third and fourth decades of life, which is evidenced by short patient survival times and rapid tumor-cell proliferation after radiation and chemotherapy. At present, the diagnosis of gliomas and decisions related to therapeutic strategies are based on genetic testing and histological analysis of the tumor, with molecular biomarkers still being sought to complement the diagnostic panel. This work aims to enable the metabolomic characterization of cancer tissue and the discovery of potential biomarkers via high-resolution mass spectrometry coupled to liquid chromatography and a solvent-free sampling protocol that uses a microprobe to extract metabolites directly from intact tumors. The metabolomic analyses were performed independently from genetic and histological testing and at a later time. Despite the small cohort analyzed in this study, the results indicated that the proposed method is able to identify metabolites associated with different malignancy grades of glioma, as well as IDH and 1p19q codeletion mutations. A comparison of the constellation of identified metabolites and the results of standard tests indicated the validity of using the characterization of one comprehensive tumor phenotype as a reflection of all diagnostically meaningful information. Due to its simplicity, the proposed analytical approach was verified as being compatible with a surgical environment and applicable for large-scale studies.