PATH-11. TRANSLATING GENOMIC DATA OF GLIOBLASTOMA INTO CLINICAL PRACTICE: A CASE STUDY. (5th November 2018) - Rose Jue, Toni; Yin, Julia; Siddell, Anna; Lu, Victor; Rapkins, Robert; Chung, Sylvia; Whittaker, Shane; Hovey, Elizabeth; Fairhall, Jacob; McDonald, Kerrie
Abstract: Glioblastoma (GBM), a malignant brain tumour that occur in adults and children, represents a major challenge for treatment. Tumor heterogeneity has been shown to contribute to this problem. The aim of this study was to overcome this issue by exploring an individualized treatment approach by selecting treatment options using whole genome sequencing, drug-screening panel and a network analysis. We present a case of a 51-year old female long-term GBM survivor with an unmethylated MGMT promoter gene who survived more than three years. Whole genome sequencing (WGS) revealed an ultra-mutated genotype in both primary and recurrent tumour samples with 421 substitutions per megabase. In depth analysis of the WGS revealed an average of 30 cancer driver genes were mutated with a 91% similarity in both primary and recurrent tumors. A drug screening panel and network analysis helped identify actionable targets. The drug screening panel included 165 compounds, of these we identified YM155, an experimental survivin inhibitor as a potential treatment. On the other hand, the network analysis revealed over 130 interconnected pathways affected by mutations in the driver genes. Pathways of interest were selected based on an FDR (false discovery rate) of 0.05 or less. These pathways included PTEN/PI3K/AKT pathway, DNA repair pathway, cell cycle pathway and various signaling pathways. EGFR was found to be predominant in 37% of the affected pathways. Hence, an EGFR inhibitor was recommended for treatment. Genome-guided treatment selection to individualize treatment for GBM patients was demonstrated to be possible in clinic. It remains a promising avenue for further translational research, with larger databases and integrated platforms to increase the efficiency of analyzing and interpreting the individual genomic data of GBM.