UC Santa Cruz

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.