In Vivo Base Editing for Precision Oncology Models

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PROJECT SUMMARY Genetic mutation is the predominant driver of cancer cell growth and therapy resistance. In fact, a major goal of personalized medicine is to identify specific genetic changes in individual tumors with the notion that defining these changes will guide more effective and targeted treatment. While this precision oncology approach shows clinical promise, ongoing tumor sequencing efforts continue to identify potential new disease drivers and new mutations. How these uncharacterized mutant alleles contribute to disease is often not obvious, and requires functional examination. Genetically engineered mouse models (GEMMs) provide an ideal tool to investigate the consequences of genetic changes on tumor biology, yet existing approaches are not fast or precise enough to recreate the spectrum of genetic alterations seen in human cancer. We and others have used CRISPR-based genome editing to accelerate the generation of complex, genetically defined animal models. Yet, while CRISPR systems are fast and simple, the basic tools are imprecise in that they cause insertions and deletions that ablate gene function but cannot mimic the single nucleotide variants most often seen in human cancer. To build in vivo systems that recapitulate specific human cancer-associated mutations, our project exploits new CRISPR tools that couple Cas9 to cytidine deaminase enzymes and enable direct DNA mutagenesis at defined genomic regions. ‘Base editing’ (BE) technology offers far greater efficiency and flexibility than existing homology directed repair (HDR) approaches by eliminating the need to deliver exogenous DNA templates. We have systematically optimized the expression and activity of BE enzymes to increase the efficiency of genome modification and established a bioinformatic and experimental pipeline to predict and validate BE tools that recreate known and novel cancer mutations. In Aim 1, building from extensively optimized BE enzymes, we will generate a range of knock-in transgenic mice to maximize the number of possible genomic regions that can be mutated using BE, and validate the activity of these mice using a new fluorescence-based reporter system. Further, using a novel sensor assay, we will identify all human and mouse sgRNAs that can target recurrent cancer-associated mutation sites. Together, this work will define the BE efficiency of thousands of independent sgRNAs, and establish the first in vivo somatic base editing platforms. In Aim 2 we will use our in vivo BE tools to generate novel animal models of pancreatic and colorectal cancer, and examine the consequences of distinct cancer-associated mutations in each disease. This work will not only offer a new understanding of key oncogenic mutations, it will provide critical validation of the utility of in vivo BE in multiple cancer settings. By providing an easy and efficient path to capture the diversity of human disease alleles, we believe this new precision editing platform has the potential to fundamentally change the way we design and implement mouse cancer models for translational research.