Date on Master's Thesis/Doctoral Dissertation

4-2023

Document Type

Master's Thesis

Degree Name

M. Eng.

Department

Bioengineering

Degree Program

JB Speed School of Engineering

Committee Chair

Frieboes, Hermann

Committee Member

Altiparmak, Nihat

Committee Member

Chen, Joseph

Author's Keywords

glioma; glioblastoma multiforme; tumor heterogeneity; machine learning; MGMT; cancer metabolomics

Abstract

Glioma is one of the most aggressive forms of brain cancer. It has been shown that the microenvironments differ significantly between the core and edge regions of glioma tumors. This study obtained metabolomic profiles of glioma core and edge regions using paired glioma core and edge tissue samples from 27 human patients. Data was acquired by performing liquid-liquid metabolite extraction and 2DLC-MS/MS on the tissue samples. In addition, a boosted generalized linear machine learning model was employed to predict the metabolomic profiles associated with O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation.

A panel of 66 metabolites was found to be statistically significant between the core and edge regions. The machine learning model achieved AUROC values of 0.941 for the core and 0.960 for edge. This proof-of-concept study shows the metabolomic differences are reflected in MGMT promoter methylation status and demonstrates the potential for machine learning to aid as a prognostic and therapeutic tool.

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