Abstract The cost of drug development has skyrocketed to an estimated $2.6B for every FDA approved drug primarily due to failures from lack of efficacy or safety. This suggests that our current preclinical validation process has been insufficient in predicting therapeutic potential and toxicity in humans. While genetically engineered mice have become the gold standard for dissecting cancer mechanisms and evaluating novel drug targets in vivo, the rat has historically been the major model species in many biomedical fields, notably toxicology and carcinogenicity testing; and for many scientists, the rat still remains the preferred rodent due to their larger size for surgical manipulation, repeat blood sampling, and their cognitive and physiological characteristics that more closely resemble humans than their mouse counterparts. Moreover, many hormone-dependent tumors cannot be engineered in mouse models and are better modeled in rats. Here, we take advantage of our two-step engineering approach and exploit the efficiency of CRISPR-based targeting to develop RNAi rat models that enable inducible and reversible gene silencing to simulate therapeutic regimes. We demonstrate that our approach allows us to rapidly generate RNAi rat models and mimic the function of the targeted small molecule inhibitors, such as BET inhibitors targeting Brd4. These results demonstrate that our high-throughput system currently used to generate RNAi mice is also applicable to the rat system and, by extension, other mammalian models. Inducible RNAi rat models will undoubtedly be powerful tools that can be used to model human cancers, to mimic the action of putative drugs, and to assess the potential of therapeutic targeting strategies in vivo prior to the costly drug development, ultimately guiding the development of safer and more effective drugs. Citation Format: Chia-Lin Wang, Yu-ting Yang, Ana Vasileva, Allison Maurice, Lukas Dow, Johannes Zuber, Scott Lowe, Prem K. Premsrirut. RNAi rat models for drug discovery [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6119.
Abstract G protein-coupled receptors (GPCRs) are the largest class of membrane-bound receptors and transmit critical signals from the extracellular to the intracellular spaces. Transcriptomic data of resected breast tumors shows that low mRNA expression of the orphan GPCR GPR52 correlates with reduced overall survival in breast cancer patients, leading to the hypothesis that loss of GPR52 supports breast cancer progression. CRISPR-Cas9 was used to knockout GPR52 in human triple-negative breast cancer (TNBC) cell lines MDA-MB-468 and MDA-MB-231, and in the non-cancerous breast epithelial cell line, MCF10A. Loss of GPR52 was found to be associated with increased cell-cell interaction in 2D cultures, altered 3D spheroid morphology, and increased propensity to organize and invade collectively in Matrigel. Furthermore, GPR52 loss was associated with features of EMT in MDA-MB-468 cells. To determine the in vivo impact of GPR52 loss, MDA-MB-468 cells were injected into zebrafish and loss of GPR52 was associated with a greater total cancer area compared to control cells. RNA-sequencing and proteomic analyses of GPR52-null breast cancer cells reveal an increased cAMP signaling signature. Consistently, we found that treatment of wild-type (WT) cells with forskolin, which stimulates production of cAMP, induces some phenotypic changes associated with GPR52 loss, and inhibition of cAMP production rescued some of the GPR52 KO phenotypes. Overall, our results reveal GPR52 loss as a potential mechanism by which breast cancer progression may occur and support the investigation of GPR52 agonism as a therapeutic option in breast cancer. Statement of Significance We show that loss of the orphan G protein-coupled receptor GPR52 in human breast cell lines leads to increased cell clustering, hybrid/partial EMT, and increased tumor burden in zebrafish.
RNA interference (RNAi) enables sequence-specific, experimentally induced silencing of virtually any gene by tapping into innate regulatory mechanisms that are conserved among most eukaryotes. The principles that enable transgenic RNAi in cell lines can also be used to create transgenic animals, which express short-hairpin RNAs (shRNAs) in a regulated or tissue-specific fashion. However, RNAi in transgenic animals is somewhat more challenging than RNAi in cultured cells. The activities of promoters that are commonly used for shRNA expression in cell culture can vary enormously in different tissues, and founder lines also typically vary in transgene expression due to the effects of their single integration sites. There are many ways to produce mice carrying shRNA transgenes and the method described here uses recombinase-mediated cassette exchange (RMCE). RMCE permits insertion of the shRNA transgene into a well-characterized locus that gives reproducible and predictable expression in each founder and enhances the probability of potent expression in many cell types. This procedure is more involved and complex than simple pronuclear injection, but if even a few shRNA mice are envisioned, for example, to probe the functions of several genes, the effort of setting up the processes outlined below are well worthwhile. Note that when creating a transgenic mouse, one should take care to use the most potent shRNA possible. As a rule of thumb, the sequence chosen should provide >90% knockdown when introduced into cultured cells at single copy (e.g., on retroviral infection at a multiplicity of ≤0.3).
ABSTRACT Loss-of-function (LoF) screenings have the potential to reveal novel cancer-specific vulnerabilities, prioritize drug treatments, and inform precision medicine therapeutics. These screenings were traditionally done using shRNAs, but with the recent emergence of CRISPR technology there has been a shift in methodology. However, recent analyses have found large inconsistencies between CRISPR and shRNA essentiality results. Here, we examined the DepMap project, the largest cancer LoF effort undertaken to date, and find a lack of correlation between CRISPR and shRNA LoF results; we further characterized differences between genes found to be essential by either platform. We then introduce ECLIPSE, a machine learning approach, which combines genomic, cell line, and experimental design features to predict essential genes and platform specific essential genes in specific cancer cell lines. We applied ECLIPSE to known drug targets and found that our approach strongly differentiated drugs approved for cancer versus those that have not, and can thus be leveraged to identify potential cancer repurposing opportunities. Overall, ECLIPSE allows for a more comprehensive analysis of gene essentiality and drug development; which neither platform can achieve alone.
