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Precision oncology is largely based on the notion that identification and targeting of oncogenic drivers will lead to improved clinical outcomes. However, the promise of precision oncology awaits to be fulfilled for many cancers, including Glioblastoma (GBM), where identification of oncogenic drivers has yet to improve survival rates. Here, we have attempted to systematically identify GBM vulnerabilities by performing genome-wide CRISRP-Cas9 lethality screens in patient-derived GBM stem-like cells (GSCs). In validation studies, we comprehensively retested GSC-specific hits in multiple GSC isolates, which were also genomically profiled (e.g. RNA-seq, exome-seq, CNV), and further integrated these data with CRISPR-Cas9 lethality screens from over 500 human cell lines from the Broad Institute’s CRISPR Avana dataset. As a result, we have begun making GBM dependency predictions and functional associations for top scoring hits, including: tumor developmental subtype; loss of functional redundancy with other genes/proteins; cancer-specific subnetworks of genes involved in mitochondrial protein turnover and membrane trafficking; and genes of unknown function essential for subset of GBMs. A few examples of these categories include the following scenarios. We find ADAR (Adenosine Deaminase RNA Specific) gene dependency is associated with the mesenchymal GBM subtype. The EFR3Agene, which has roles in maintaining active pools of phosphatidylinositol 4-kinase, appears required when the expression of its paralog EFR3Bis low or absent in tumor cells. The F-box protein-encoding gene FBXO42appears non-essential to most human cells lines and neural stem cells, but when knocked out in sensitive GSCs causes mitotic arrest, mitotic catastrophe, and cell death. While still a work in progress, we hope to use these results as a foundation for exploring and illuminating patient-specific molecular vulnerabilities for brain tumors. The results also underscore the need for integration of functional genetic approaches, where gene activities are inhibited, into precision oncology paradigms.

COMP-04. MODELING PRECISION ONCOLOGY FOR GLIOBLASTOMA THROUGH INTEGRATION OF DESCRIPTIVE, FUNCTIONAL, AND NETWORK-BASED GENOMICS