AUTHOR=Gandía-González María L. , Cerdán Sebastián , Barrios Laura , López-Larrubia Pilar , Feijoó Pablo G. , Palpan Jr. Alexis , Roda José M. , Solivera Juan 

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

JOURNAL=Frontiers in Oncology

VOLUME=Volume 9 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2019.00328

DOI=10.3389/fonc.2019.00328

ISSN=2234-943X

ABSTRACT=Objective: We assess the efficacy of the metabolomic profile of the tumor in providing estimates of postsurgical Overall Survival in glioma patients. 

Methods: Tumor biopsies from 46 patients bearing gliomas, obtained neurosurgically in the period 1992-1998, were analyzed by high resolution 1H magnetic resonance spectroscopy (HR- 1H MRS) and histopathology, following retrospectively individual postsurgical Overall Survival up to 720 weeks.

Results: The Overall Survival profile could be resolved in three groups; Short (shorter than 52 weeks, n=19), Intermediate (between 53 and 364 weeks, n=19) or Long (longer than 365 weeks, n=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 this response, we analyzed HR- 1H MRS spectra from acid extracts of the same biopsies, to identify 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 implemented then a multivariate analysis approach to identify hierarchically the influence of metabolomic biomarkers on OS predictions, using a Classification Regression Tree. Classification Regression Tree based in metabolomic criteria grew up to 3 branches and split into 8 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. 

Conclusion: Present results indicate that metabolic profiling by HR-1H MRS provides reliable Overall Survival predictions.