Identifying Oncogenetic Vulnerabilities With Inferred Synthetic Lethal Networks
Thomas Matthew, PhD Student, Biomolecular Engineering & Bioinformatics
Thursday, June 15, 2017 – 1:00pm
Location – Physical Science Building, Room 305
Host – Professor Josh Stuart
Abstract: Translation of cancer genomic data into cancer therapies and companion diagnostics remains a primary challenge in personalized medicine. Much of this challenge is due to the difficulty of identifying genetic dependencies, or essential genes, that lead to clinically actionable drug targets. Targeting many of the known essential gene products is not always selectively efficacious because these targets may be common to both malignant and benign cells. I propose the application of deep learning methods over new genetic interaction networks to predict gene essentiality and drug sensitivity. These methods will be developed in cell line data from CCLE both as an end-use tool to refine cell based assays, and as a proxy to tumor biology. Wet-lab validation of select hits, using either small molecules or RNAi knockdown, is proposed in order to demonstrate translatability of new essential genes to relevant viability phenotypes in human cell lines. The proposed work could provide a tool to accelerate both target discovery and biomarker discovery, to develop drugs suitable for a specific cancer, and identify and stratify patients who may benefit from these treatments.