Despite rapid advances in elucidating the molecular basis of human diseases, an ostensibly more difficult post-genomic challenge is the functional annotation of disease-specific signaling pathways and integration of this information into the development of novel drugs. RNA interference (RNAi) now makes it possible to use large-scale functional genomic strategies for target identification. Unfortunately, while RNAi has opened many potential avenues for improving the drug discovery process, these avenues remain only potential opportunities until we develop robust RNAi screening technologies, as well as experimental and bioinformatics tools for data validation and integration of this information into operational cell-based models. To address these issues, in Phase I, we developed second generation functionally validated (FV) human druggable genome lentiviral 15K shRNA libraries, and we have demonstrated their utility for deciphering cell- signaling pathways. The ultimate goal of the Phase II studies is to develop and establish a cost-effective novel functional genomics platform to facilitate the discovery of therapeutic molecular targets en masse. Specifically, we propose to scale-up development of and commercialize a comprehensive set of human and mouse genome- wide FV shRNA libraries. These libraries will have improved performance and be designed for cost-effective pooled-format screening and identification of effectors by high-throughput (HT) sequencing. As supporting tools, we will develop protocols, reagents and software tools for in vitro and in vivo screening hit validation and therapeutic target prioritization. To test the performance of our functional genomics platform, we propose to use our novel RNAi resource to delineate the processes that underlie tumorigenesis in breast epithelial cells. We will perform synthetic lethality screens in a unique panel of isogenic human mammary epithelial cell (HMEC) lines that comprise the most relevant breast cancer genetic alterations. Furthermore, we will validate the results of our in vitro screens in xenograft models using both fully transformed HMECs and common breast cancer cell lines. Our findings will then be combined with data collected from scientific publications and presented in a publicly available knowledge base, with the ultimate goal of developing models of signaling pathways that specifically control the proliferation and survival of breast cancer cells. These developed RNAi screening, validation and software tools will be commercialized as products and custom services to provide the research community with highly modular, cost-effective approaches for studies aimed at understanding and integrating dynamic changes in signal transduction networks and ultimately delineating disease-specific phenotypes. As a result, we foresee that these toolsets will significantly improve the efficiency, economy and ease of elucidating and modeling disease-specific signal transduction networks and provide basic researchers with preferred, cost-effective alternatives to existing commercially available reagents and software. The proposed RNAi screening and bioinformatics strategies harbor considerable potential to systematically identify new anti-cancer targets for therapeutic intervention and to facilitate the development of highly specific drugs, biomarkers and novel therapeutic concepts.
The ultimate goal of the Phase II project is to develop and make commercially available a novel orthogonal functional genomics platform to facilitate discovery and validation of therapeutic molecular targets en masse. As a first step, we propose to develop and make commercially available a set of second generation of functionally-validated genome-wide human and mouse 65K pooled shRNA lentiviral libraries with improved performance and optimized design for cost-effective genetic screens. As a confirmation tool, we will develop protocols for high-throughput in-vitro and ex-vivo validation of drug target candidates identified in the screen with pooled shRNA sublibraries. From a bioinformatics viewpoint, we will make software tools for integration of RNAi screening data with transcriptome profiling and molecular network information mined from scientific literature. The proposed functional genomics platform will be applied and validated for the discovery of novel cancer therapeutic targets in a unique collection of isogenic human mammary epithelial cell (HMEC) lines, comprising the most common breast cancer genetic alterations. As a result of these studies we will reconstruct synthetic lethality pathways and make publicly available breast cancer knowledge database. These developed RNAi screening, validation and software tools will be commercialized as products and custom services to provide the research community with highly modular, cost-effective approaches for studies aimed at understanding and integrating dynamic changes in signal transduction networks and ultimately delineating disease-specific phenotypes. As a result, we foresee that these toolsets will significantly improve the efficiency, economy and ease of elucidating and modeling disease-specific signal transduction networks and provide basic researchers with preferred, cost-effective alternatives to existing commercially available reagents and software. The proposed RNAi screening and bioinformatics strategies harbor considerable potential to systematically identify new anti-cancer targets for therapeutic intervention and to facilitate the development of highly specific drugs, biomarkers and novel therapeutic concepts.
| Guo, Jing; Liu, Hui; Zheng, Jie (2016) SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets. Nucleic Acids Res 44:D1011-7 |
| Boettcher, Michael; Lawson, Andrew; Ladenburger, Viola et al. (2014) High throughput synthetic lethality screen reveals a tumorigenic role of adenylate cyclase in fumarate hydratase-deficient cancer cells. BMC Genomics 15:158 |
| Wolf, Jonas; Dewi, Dyah Laksmi; Fredebohm, Johannes et al. (2013) A mammosphere formation RNAi screen reveals that ATG4A promotes a breast cancer stem-like phenotype. Breast Cancer Res 15:R109 |
