Nicolas Coquery1, 2, Vasile Stupar, 23, Rgine Farion, 23, Sverine Maunoir-Regimbal4, Emmanuel Luc Barbier, 25, Chantal Rmy1, 2, Florence Fauvelle4
1U836, INSERM, Grenoble, France; 2Universit Joseph Fourier, grenoble, France; 3Grenoble MRI Facility IRMaGe, Grenoble, France; 4IRBA-CRSSA, La Tronche, France; 5INSERM U836, Grenoble, France
Magnetic Resonance-based spectroscopy (MRS) is a powerful method to investigate the metabolic consequences of cancer disease. In vivo MRS provides information regarding tumor growth and response to treatment. These information can be refined with ex vivo High Resolution Magic Angle Spinning (HRMAS) MRS. With these two approaches a huge amount of information can thus be gathered that might render the analysis difficult in clinic. We propose here to use statistical tools such as PLS-DA to discriminate tumoral tissue from normal tissue. PLS-DA analysis is also able to show a clear separation between three glioma models in rat and to highlight the metabolites that contribute to this separation despite inter-individual variability.