TMOD-13. IDENTIFYING DRIVERS IN THE CONVERGING SYNTENIC REGIONS OF SPONTANEOUS CANINE AND PEDIATRIC HIGH-GRADE GLIOMA USING IMAGING BASED CRISPR-CAS9 ARRAY SCREEN. (12th November 2021)

• Record Type: Journal Article
• Authors: Amin, Samirkumar; Kang, Wonyeong; Gujar, Amit; Maher, Leigh; Courtois, Elise; Robson, Paul; Dickinson, Peter; Packer, Rebecca; Lee, Charles; Verhaak, Roel
• Is Part Of: Neuro-oncology. Volume 23: Supplement 6(2021)
• Page Start: vi218
• Page End: vi218
• Publication Date: 2021-11-12
• Subjects: Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noab196.874
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: Gliomas occur in companion dogs at rates comparable to humans, with short-snouted breeds such as boxers being more susceptible than others. The natural progression of cancer in the immuno-competent host allows companion dogs diagnosed with sporadic glioma as an optimal model for preclinical testing of therapeutic approaches with human relevance, including immunotherapies. We have recently performed comprehensive genomic and epigenetic characterization of glioma in dogs to their human counterparts and found strong convergent evolution – shared somatic mutations and aneuploidies - among syntenic regions, including those of known pediatric glioma drivers, e.g., PDGFRA, MYC, PIK3CA . Here, using arrayed CRISPR-Cas9 imaging based phenotypic screen, we will probe potential oncogenic drivers and tumor suppressor genes within syntenic aneuploidies and thus outline functional versus non-functional heterogeneity of cancer aneuploidy. Specifically, we are conducting arrayed knockout screen (one gene per well) of 400+ genes within syntenic aneuploidies across canine (n=2) and pediatric (n=2) high-grade glioma cell lines. We will first capture images by high-speed confocal imaging system at three time points post-transduction of single guide RNAs (2 per gene) targeting each of 400+ genes in their separate wells. Then, using high-throughput image analysis and semi-supervised machine learning methods, we will measure well-based phenotypic features (viability, growth, and morphology) from these images. Genes will be ranked per cross-validated predicted probability in yielding either proliferating or slow-growing cell type based on learned phenotypic features using image data of knockout cells from and across wells. The top ranked genes will then be linked to oncogenes and tumor suppressors based on pathway and ontology analysis. We expect that we will see convergence of the most impactful molecular abnormalities (based on their knockout phenotypes) on mechanisms or candidate signaling pathways for the development of new drugs and repurposing of existing drugs for kids and dogs with high-grade glioma.