Ex vivo HR-MAS NMR, in vivo MRS-MRI and Multiivariate analysis to Highlight Biomarkers in Gliomas

Gliomas account for about 40% of total primitive brain tumors, and discrimination between high and low glioma grade remains a vital diagnostic decision, determining the most effective treatment and having an important impact on patient management and outcome. The in vivo MRS is considered a tool able to help the diagnosis based on MRI in the evaluation of several human pathologies, including cancer. In vivo MRS provides the spectra of living tissues, directly correlated to their chemical composition, but it can be used when the molecular markers of tissues are well established by means of a detailed biochemical picture. This last can be derived from the spectroscopic analysis of ex vivo biopsy samples using High Resolution Magic Angle Spinning (HR-MAS) NMR technique. The ex vivo HR-MAS NMR spectra provide more details about metabolites (aminoacids, carbohydrates, osmolites, organic acids, mobile lipids) than in vivo MRS and permits to produce a metabolic picture of the tissues. Accurate biochemical assignment of metabolites will improve our interpretation of HR-MAS data and the translation of NMR tumor biomarkers to in vivo studies. 1D and 2D HR-MAS NMR experiments were used to determine metabolites of brain tumor (astrocytoma grade II, grade III gliomas, glioblastomas). We developed this project on gliomas with the aim to gain a better insight into the discrimination among different grades and subtypes using ex vivo HR-MAS NMR, in vivo MRS, MRI, clinical data and statistical analysis. We report experiments performed on 15 specimen already collected from different grade glioma. Different amount of some small metabolites such as alanine, lactate, glutamine, glutamate, myo-inositol and glycine in two different ex vivo high grade glioma samples. The ex vivo spectra obtained on samples from different locations, line-broadened in order to be compared with the in vivo MR spectrum, obtained from the same selected voxel. A number of metabolites have been identified as potential biomarkers of tumor type; now we need to combine all the in vivo, ex vivo, histological and clinical data to obtain a unique tumor fingerprints. Results gathered from this study should lead to the development of tools that can facilitate the distinction of tumor types and grade that cannot be readily distinguished by histopathology or by routine neuroimaging.