BSCI-23 ANEUPLOIDY PROFILING IN GLIOBLASTOMA IDENTIFIES MECHANISMS OF DISEASE PROGRESSION AND TREATMENT VULNERABILITIES

Glioblastoma (GBM) is the most common and malignant adult brain tumor. Despite years of research, few advancements have been made in its management. One challenging area of glioblastoma research is patient stratification in clinical trials based on genomic features. Although several regions of aneuploidy have been known to drive disease progression in GBM, the degree of aneuploidy across the genome varies widely and the significance of regions of aneuploidy has not been assessed. Using whole genome sequencing profiles for matched tumor and non-tumor samples, we were able to accurately determine the degree of aneuploidy and loss of heterozygosity for a set of primary GBM tumors. Next, using machine learning techniques, distinct patterns of aneuploidy and loss of heterozygosity emerged among a set of GBM tumors, allowing us to define distinct aneuploidy subgroups. Interestingly, these aneuploidy subgroups showed distinctly different rates of patient survival, suggesting that regions of aneuploidy may be driving disease progression. Differing rates of various GBM genomic subtypes including IDH mutation, EGFR mutation, MGMT methylation, and tumor subtypes was also seen among the aneuploidy subgroups. We were able to derive a gene expression signature for each of these aneuploidy subgroups and revealed distinct pathways that were driving tumor growth. Furthermore, using a perturbagen-response dataset we were able to predict compounds to distinctly target each subgroup. Collectively, this suggests that aneuploidy profiling provides important clues to varying mechanisms of disease progression and is a promising approach for targeted therapy in a patient-specific manner.