Abstract PHA05: Strategies to improve engineering cancer-associated SNVs with base editing

Abstract

Abstract Base editing (BE) is a powerful tool for engineering single-nucleotide variants (SNVs) and has been used to create targeted mutations in cell lines, organoids, and animal models. Recent development of new BE enzymes has provided an extensive toolkit for genome modification; however, unlike Cas9, which shows remarkable efficacy in creating homozygous disruptive mutations following DNA double-strand breaks (DSBs), BE is relatively inefficient and identifying and isolating edited cells for analysis has proved challenging. To enable simple identification and enrichment of base editing events, we developed a “Gene On” (GO) reporter system that indicates precise cytosine or adenine base editing in situ with high sensitivity and specificity. We validate GO using an activatable GFP and use it to measure the kinetics, efficiency, PAM specificity, and fidelity of a range of new BE variants. Further, GO is flexible and can be easily adapted to induce expression of numerous genetically encoded markers, antibiotic resistance genes, or enzymes such as Cre recombinase. With these tools, GO can be exploited to functionally link BE events at endogenous genomic loci to cellular enzymatic activities in human and mouse cell lines and organoids. We further demonstrate the application of GO in detecting in vivo BE activity using a newly generated mouse model with inducible and reversible base editor expression that will enable generation of novel cancer models in vivo. GO provides a powerful approach to increase the practicality and feasibility of implementing CRISPR BE in biomedical research. This abstract is also being presented as Poster A45. Citation Format: Alyna Katti, Miguel Foronda, Jill Zimmerman, Bianca Diaz, Maria Paz Zafra, Sukanya Goswami, Elena Piskounova, Lukas E. Dow. Strategies to improve engineering cancer-associated SNVs with base editing [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr PHA05.