Assessment of Overall Survival in Glioma Patients as Predicted by Metabolomic Criteria.

We assess the efficacy of the metabolomic profile from glioma biopsies in providing estimates of postsurgical Overall Survival in glioma patients. Tumor biopsies from 46 patients bearing gliomas, obtained neurosurgically in the period 1992-1998, were analyzed by high resolution H magnetic resonance spectroscopy (HR- H MRS), following retrospectively individual postsurgical Overall Survival up to 720 weeks. The Overall Survival profile could be resolved in three groups; Short (shorter than 52 weeks, = 19), Intermediate (between 53 and 364 weeks, = 19) or Long (longer than 365 weeks, = 8), respectively. Classical histopathological analysis assigned WHO grades II-IV to every biopsy but notably, some patients with low grade glioma depicted unexpectedly Short Overall Survival, while some patients with high grade glioma, presented unpredictably Long Overall Survival. To explore the reasons underlying these different responses, we analyzed HR-H MRS spectra from acid extracts of the same biopsies, to characterize the metabolite patterns associated to OS predictions. Poor prognosis was found in biopsies with higher contents of alanine, acetate, glutamate, total choline, phosphorylcholine, and glycine, while more favorable prognosis was achieved in biopsies with larger contents of total creatine, glycerol-phosphorylcholine, and myo-inositol. We then implemented a multivariate analysis to identify hierarchically the influence of metabolomic biomarkers on OS predictions, using a Classification Regression Tree (CRT) approach. The CRT based in metabolomic biomarkers grew up to three branches and split into eight nodes, predicting correctly the outcome of 94.7% of the patients in the Short Overall Survival group, 78.9% of the patients in the Intermediate Overall Survival group, and 75% of the patients in the Long Overall Survival group, respectively. Present results indicate that metabolic profiling by HR-H MRS improves the Overall Survival predictions derived exclusively from classical histopathological gradings, thus favoring more precise therapeutic decisions.