Bay Area Cancer Target Discovery and Development

PROJECT SUMMARY Our general strategy is to take advantage of novel tools and methodologies that we have developed during our first two CTD^2 funding periods– more specifically pioneering and applying CRISPR based technologies to aid the discovery and characterization of novel cancer targets and their modulators– using innovative high throughput technologies. Our end goal is to uncover optimal combinations of targets with the potential to eliminate all cancer cells, despite their clonal heterogeneity and environmental context. This requires us to better understand tumor biogenesis, namely the combinations of genes that drive oncogenesis, and tumor heterogeneity which complicates effective therapeutic treatment. In this proposal we build upon exciting systems allowing us to quantitate genotypic and phenotypic cell heterogeneity in cell culture and in vivo. The overall goal is to identify synthetic gene combinations necessary for clinical resistance and related to inter- and intra-tumor heterogeneity. We hypothesize that altered cell states such as inflammatory phenotypes and lineage plasticity fuels therapy tolerance and resistance. We apply single-cell approaches and cutting-edge lineage tracing tools to investigate the genesis of pathogenic cellular state changes and use genetic screening, computational and pharmacologic approaches, and clinically relevant in vitro and in vivo tumor models to identify mechanistically calibrated, specific therapeutic vulnerabilities. These approaches will be applied to two cancer, lung and breast adenocarcinoma. Tumor biogenesis and evolution is a challenging area of research, largely due to the complexity of cell types and behaviors and the combinations of genes that drive cancer types and subtypes is poorly understood. We have developed next generation GEMMs to interrogate gene combinations that promote cancer. In this aim, mouse models will be generated that contain combinations of genetic perturbations of the top 30 TCGA recurrent mutations. These studies will associate the combination of perturbagens with specific cell states, despite their clonal heterogeneity and cell state and lay a solid foundation for identifying which combinations of recurrent genes respond to which therapy, thus helping to stratify patients. This part of the research program focuses on lung cancer as it synergizes with other components of the proposal. We apply an evolved lineage tracing technology with single cell RNA-seq readout that lets us follow tumor evolution with unprecedented resolution. These studies will help us understand how tumor plasticity enables cancers to evade therapeutic challenges. And importantly, how the loss of tumor suppressor genes or gene combinations, alters the preferred evolutionary paths a single transformed cell takes to reach aggressive and metastatic states.