TMET-53. Characterizing the metabolic landscape of high-grade glioma with respect to transcriptional subtype and imaging region

Abstract High-grade glioma (HGG) continues to have a dismal prognosis owing in part to its heterogeneous and invasive nature both within and between patients. The most aggressive form of high-grade glioma, called glioblastoma, portends a median survival of around 15 months. Like many cancers, high-grade glioma rewires metabolism towards its own benefit, shifting away from the typical reliance of brain tissue on glucose towards other sources such as fatty acids and acetate. Using a cohort of image-localized high-grade glioma biopsies collected from diverse imageable regions with associated transcriptomics (58 patients, 202 biopsies), we are able to assess the spatial transcriptomic landscape of HGG. We have previously shown that the transcriptome of HGG can be characterized into a continuous transition between states, and we have shown that the cellular ecosystem follows a similar pattern, using a combination of trajectory inference and cellular deconvolution. These patterns are strongly associated with the non-enhancing and contrast-enhancing regions of the tumors, which are typically considered for treatment planning and progression assessment. Here, we applied differential expression of hallmark pathways to determine how metabolism factors into this paradigm. As expected, we find overall higher levels of metabolism-associated pathway expression in the contrast enhancing regions compared to the non-enhancing regions. Interestingly, we found metabolic expression differs between the ecologies associated with transcriptional subtypes of high-grade glioma. For example, fatty acid (p=0.003), xenobiotic (p<0.0001) and heme (p<0.0001) metabolism pathways were significantly upregulated for mesenchymal tumor samples, while significantly downregulated for proliferative tumor samples (p=0.02, p<0.0001, p<0.0001, respectively). As we continue to further understand the complex spatial and temporally heterogeneous landscape of these aggressive tumors, it is important to consider the metabolites on which they depend, to better inform prognosis and develop new treatments.