Impact of Enhancing and Non-enhancing Niches in the Therapeutic Vulnerability of Glioblastoma

Introduction: Glioblastoma’s heterogeneity is a known driver of treatment resistance. Further, improved survival with resection of non-enhancing tumor regions (NET) has motivated this study to characterize the biology and functional impact of enhancing (ET) and NET in the therapeutic vulnerability of GBMs.

Methods: A total of 4 tumor regions per patient in 6 patients were sampled. From each, a glioma stem cell enriched as well as a bulk model were established. Whole-exome sequencing (WES) and RNAseq were used to query their genomic and transcriptomic profiling. Integrated analysis involving -omic and therapeutic profiles were pursued.

Results: Using WES the same PTEN alteration was found in all regions of 5 patients, whilst interestingly EGFR displayed different mutations and varying allele frequency in different regions within cases. Transcriptomic analysis (TCGA- and pathway-based classification) showed ETs to have mesenchymal, glycolytic/plurimetabolic and proliferative/progenitor enrichment at the expense of neuronal and proneuronal. Using an unbiased systems biology approach, WGCNA (Weighted Gene Co-expression Network Analysis) combined with Ingenuity Pathway Analysis to explore genes/pathway enrichment, we identified 5 gene clusters able to differentiate ET and NET tumors. Specifically, clusters enriched in ETs showed upregulation of ECM-related pathways and inflammation, whereas clusters in NETs had upregulation of RhoGDI signaling. Therapeutic vulnerability was tested in 2 different tumor models through 15 single agents targeting common GBM oncogenic pathways with high reproducibility (rs=0.6632, p< 0.001). Integrated analysis using HAllA (Hierarchical All-against-All association testing) showed NET tumors to present better responses to TMZ, in part due to upregulation of FBXW7. Multiple signatures were also found to predict response to drugs targeting canonical GBM pathways.

Conclusion : Our study is the first to systematically characterize the impact of NET/ET on drug vulnerability and to define novel enhancement-dependent gene clusters. Further, numerous signatures were found to explain differential drug response for multiple drugs.