Using Genomics and Bioinformatics to Match Cancers with Their Therapeutic Medicines

We are applying a pipeline platform, VeroTest1, in order to functionally identify new drug targets in cancers. Our platform starts with powerful genome-scale gene essentiality screens, and then, using an automated set of bioinformatics programs, first matches these "gene dependencies" to "essential pathways", and then matches these pathways to optimal targeted drugs. Our Successful Drug Prediction Algorithm (SDPA) maximizes this process by enriching for cancer-specific gene dependencies and then testing combinations of programs that optimize our ability to identify drugs with the strongest testable killing power for any and all cancer cell types. Our unbiased screening pipeline taps into massive, growing datasets describing the sensitivities of hundreds of human cancers to >100,000 FDA-approved drugs, >99% of which were developed for diseases other than cancer. Thus, VeroTest1 can functionally identify the best drug targets for all cancer conditions, even those that are drug-resistant or undruggable, in a rapid, accurate and cost-efficient manner.

 

VeroTest2, currently under development, will apply the power of VeroTest1 to patient-specific surgical or biopsy samples in order to produce a fully personalized VeroTest oncology pipeline that can functionally identify drug susceptibilities.