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07 Identifying and prognosticating malignant brain tumors non-invasively using unique metabolomic signatures derived from patient serum and urine samples

  • D Yusuf, AD Singh, R Shaykhutdinov, J Wen, P Forsyth, HJ Vogel, JG Cairncross, AM Weljie and JC Easaw...

Abstract

BACKGROUND: Metabolomics technology has the potential to revolutionize how we screen, diagnose, and treat cancer, as well as improve upon existing cancer molecular tests that may not sufficiently capture the complexity of most malignancies. In this study, we explore the clinical potential of metabolomics analysis in the diagnosis and risk-stratification of brain tumors. METHODS: To test the hypothesis that brain tumor type and survival could be predicted with metabolomics, we analyzed the pre-operative serum and urine samples of patients with glioblastoma (GBM), oligoastrocytoma (OA2), meningioma (M1) and compared them to healthy controls. (HC). Sera from immune-deficient NOD-SCID mice xenografted with human GBM brain tumor initiating cells were also studied. RESULTS: Metabolomics analysis of patient samples was able to accurately differentiate GBM, OA2, M1 and HC (p = 2.3 x 10-26). Subsequently, a prediction model developed and validated internally was able to diagnose GBM with a sensitivity of 86.7% and specificity of 93.8%, and distinguish whether a GBM patient possess O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation (p = 7.4 x 10-10). Within the MGMT methylated group, the model was able to predict longevity (p = 3.25 x 10-4). The model was also able to predict survival irrespective of MGMT methylation status (p = 2.9 x 10-6). CONCLUSIONS: In this study, we demonstrate that metabolomic analysis of patient biofluids can identify brain tumors, distinguish brain tumor subtypes, and independently predict MGMT status as well as longevity among GBM patients. Metabolomics analysis may facilitate non-invasive diagnosis of aggressive brain tumours.

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      07 Identifying and prognosticating malignant brain tumors non-invasively using unique metabolomic signatures derived from patient serum and urine samples
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