The wealth of data on the genomics of cancer provides a great opportunity to develop more effective targeted therapies. However, many commonly mutated cancer genes resist efforts to target with drugs, genetic heterogeneity of tumors confounds choice or efficacy of drugs, and development of resistance to commonly used therapies is common, leaving few alternatives. New approaches are needed to address these challenges. Exploiting cellular vulnerabilities generated as a result of mutations in commonly mutated genes, e.g. synthetic lethality, is a promising approach, as illustrated by the recent approval of the PARP inhibitor olaparib in ovarian cancer. We have developed and optimized a synthetic lethal discovery platform that entails high throughput screening to identify novel targets in patient-derived cancer cell cultures and isogenic cell systems. Integration of functional screen results with both patient specific (N of 1) and population-based genomic data is used to prioritize targets useful to the greatest number of patients and in the most appropriate genomic and molecular contexts. Prioritized targets undergo exhaustive confirmation and orthogonal validation in physiologically-relevant settings including genomically characterized patient-derived cell cultures, organoids and patient derived xenograft (PDX) models. Synthetic lethal genes identified with our platform are conserved across species, have been confirmed as candidate drug targets across multiple human cancer types and have led to an investigator initiated clinical trial, illustrating the translational utility of our platform. The outcome of this proposal will be novel validated targets and therapeutic strategies to several human cancer types including those resistant to standard of care agents and a deeper understanding of the biology of several major cancer genes.
Thanks to large scale DNA sequencing efforts, we now have a comprehensive view of the genomic landscape of most major human cancers. The next challenge is to translation this information into better therapies. To accomplish this we need to identify novel gene targets which can be inhibited by drugs or immunotherapy agents. We propose to utilize personalized cancer models for both discovery and validation of new targets. Integrating this information with genetic data will be used to prioritize targets useful to the greatest number of patients and in the most appropriate genomic and molecular contexts. The outcome of this project will not only identify novel gene targets for future drug development but it will generate insight into how major cancer genes function.
