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Inferring Genetic Co-dependencies to Identify New Vulnerabilities in Cancer
July 2 @ 3:00 pm - 5:00 pm PDT
Thomas J. Matthew
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 co-dependencies that lead to clinically actionable drug targets. Targeting many of the known essential gene products are not always selectively efficacious because these targets may be common to both malignant and benign cells. However, essential genes that are associated with particular genomic alterations in cancer cells, like those from synthetic lethality, can potentially provide a source of tumor-specific drug targets.
To help aid novel drug discovery, I developed a computational approach called CLOvE, a multi-omic approach that identifies co-dependencies in pairs of genes. These co-dependencies are inferred from context-dependent changes in expression, where CLOvE assigns high scores to those genes with the greatest compensatory change in expression. These scores may suggest synthetic lethal interactions, which may uncover clinically actionable essential genes. These methods were developed in CCLE cell lines and validated with RNAi and CRISPR viability data. CLOvE identifies meaningful expression changes, assigns high scores to known essentials, reveals known synthetic lethal connections, and implicates many possible new connections. This approach 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.