Examining the Effects of Molecular Aberrations on Metabolic Tumor Environment

Principal Investigator: Alexander Lin

Authors: Natasha Najam, Sam Jiang, Hujiun Liao, Patrick Wen, David Reardon, Raymond Huang, Alexander Lin
Lay Abstract

Cancer is a disease in which abnormal cells divide uncontrollably and destroy surrounding tissue. A tumor can be formed when this aggressive dividing of cells occurs in the bone, tissue, or organ. In this study, we focused on Gliomas, which are aggressive tumors that reside in the brain’s white matter. Our research looked at the chemicals in the brain to see if there was a pattern between the different types of gliomas. These chemicals have the potential to become identifying molecules or biomarkers of certain gliomas. We used Magnetic Resonance Spectroscopy (MRS) to collect chemical and genetic data and the Epic database to collect clinical data. Our study found robust differences in the pattern of chemicals present around the tumors. The strategy of focusing on the associating chemicals found in the tumor microenvironment can help accurately categorize and identify gliomas. This specification can play a key role in the treatment process.

Scientific Abstract

Background: Molecular markers have been shown to be useful in identifying and classifying uniform subgroups of gliomas based on genetic data. Isocitrate Dehydrogenase (IDH) mutation, 1p/19q co-deletion, and the Methyl-Guanine MethylTransferase (MGMT) promoter mutation are important genetic markers associated with better prognosis and overall survival. Magnetic Resonance Spectroscopy (MRS) is a non-invasive analytical technique that identifies and quantifies chemical metabolites in vivo. We hypothesize that the different genetic markers will result in metabolite changes in the tumor microenvironment.

Methods: In this study, we examined 515 MRS scans and combined clinical and molecular data, in order to identify the histological and genetic construct of the tumor. Then, through exploratory statistical analysis via the Mann Whitney test, we extracted statistically significant metabolites for the aforementioned molecular aberrations.

Results: In IDH mutations we found increased concentrations of 2HG and decreased concentration of glutamate, scyllo-inositol, and lipids. Through 1p/19q analysis, we identified decreased concentrations of glutamate and lipids. Finally, in MGMT mutation analysis, we identified decreased concentrations of myoinositol, glutamate, and creatine. The latter of which is a novel finding

Conclusion: The findings can categorize and identify histological biomarkers that can play an important role in the process of personalized cancer treatment.

Clinical Implications
Chemical metabolites vary among tumors, depending on the genetic and chemical make-up of the tumor. These variations can help us more accurately identify tumors and thus, tailor treatment strategies to tumor-specific biology.

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