Date on Master's Thesis/Doctoral Dissertation
JB Speed School of Engineering
glioma; glioblastoma multiforme; tumor heterogeneity; machine learning; MGMT; cancer metabolomics
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.
Baxter, Mary E., "Metabolomic differentiation of tumor core and edge in glioma." (2023). Electronic Theses and Dissertations. Paper 4185.
Retrieved from https://ir.library.louisville.edu/etd/4185