Comparative Transcriptomics to Identify Targeted Therapy Candidates in High Grade Glioma. (16th November 2020)

• Record Type: Journal Article
• Authors: Hundley, Kelsey; Vaske, Olena; Lyle, Geoff; Kephart, Ellen; Learned, Katrina; Beale, Holly; Wardell, Christopher; De Loose, Annick; Day, John D; Rodriguez, Analiz
• Is Part Of: Neurosurgery. Volume 67(2010)Supplement 1
• Publication Date: 2020-11-16
• Subjects: Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1093/neuros/nyaa447_871
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract: INTRODUCTION: Genomic characterization is often used for the identification of therapeutic targets in tumors. Recently, comparative transcriptomics has begun to be utilized for this purpose. In this pilot, we compare the transcriptome of a patient with recurrent high grade glioma (HGG) to our cohort to identify potential therapies. METHODS: We reviewed transcriptomic profiles from patients who had resection of HGG over the past year. Briefly, tumor RNA was extracted from embedded tumor tissue sections with tumor cellularity higher than 20%. One hundred nanograms of RNA per tumor sample was fragmented with heat in the presence of magnesium to an average size of 200 base pairs. The RNA libraries were prepared and samples were hybridized with the IDT xGEN Exome Research Panel. The RNA libraries were sequenced to obtain approximately 65 million reads on an Illumina HiSeq 4000 System utilizing patterned flow cell technology. From RNA sequencing variant calling, the best practices published by the Broad Institute were followed. The RNA profile of a 24 male with Li-Fraumeni syndrome and recurrent HGG with leptomeningeal spread underwent comparative transcriptomics to identify targets. A Bayesian statistical framework for gene expression outlier detection was used. These comparisons were used to identify genes and pathways that are significantly overexpressed. RESULTS: Our HGG cohort consisted of 44 adult patients. Our HGG cohort was evenly distributed among the 4 HGG Verhaak subtypes. Our patient of interest had druggable outlier expression in HDAC1, STAT1 and STAT2 in comparison to the cohort indicating vorinostat and ruxolitinib as potential therapies, respectively. The patient did not have outlier expression in notch transcriptional targets or immune checkpoint biomarkers. CONCLUSION: Comparative Transcriptomics can identify therapeutic targets in a patient with recurrent HGG. Our patient's identified druggable targets will be utilized to treat leptomeningeal recurrence.