A new strategy for brain tumour metabolomic analysis

Abstract

Introduction: Nowadays, diagnosis of brain tumours is mainly carried out via neuroimaging techniques. The most widespread methods for routine analysis include computer tomography and magnetic resonance imaging. While such methods are useful to localise tumours, they are unable to offer a conclusive diagnosis of the tumour type. A final diagnosis can only be made via a histological examination of tissue after tumour resection, or, in cases where the location of the tumour is not amenable to resection, after a biopsy of the tumour is carried out. Untargeted metabolite analysis is a relatively new approach to diagnostics, capable of establishing wide characterisation of endogenous metabolites of a given system, a method that can be applied to improve identification of tumour types via biomarker discovery. In this regard, sample collection and preparation can be said to be the most important step in metabolomic studies.Material and methods: In the current study, a solid phase microextraction (SPME) protocol for metabolo-mics, which has been successfully applied towards metabolite analysis in various biological materials in the last few years, was optimised for brain tumour tissue metabolomic analysis. In the current study, the described approach was applied to human brain tumours. Aiming to incur minimal tissue damage, the probes used for sampling were of diameter ca. 0.2 mm. Aiming to optimise the method towards enhanced recovery of the extracted metabolites, various desorption solvents were tested in an optimisation study. The final protocol was used for analysis of a pilot cohort of patients with glioma and meningioma tumours. Results: The results showed that a protocol where chemical biopsy was performed directly from resected tumour with 7-mm-long coating SPME probe and desorption was done using 0.3 mL of a mixture of acetonitrile and water 80:20 v/v was superior to other tested protocols. The optimised method allowed for successful differentiation between the two types of brain tumours studied: meningioma and glioma. Despite the relatively small cohort group involved in the study, several compounds were tentatively identified as statistically significant metabolites responsible for this differentiation.Conclusions: The presented preliminary data demonstrate a potential of the proposed method as a low invasive diagnostic tool for on-site analysis.