# In Vivo Base Editing for Precision Oncology Models

> **NIH NIH R01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2020 · $623,053

## Abstract

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...

## Key facts

- **NIH application ID:** 9893848
- **Project number:** 5R01CA229773-02
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** LUKAS Edward DOW
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $623,053
- **Award type:** 5
- **Project period:** 2019-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9893848

## Citation

> US National Institutes of Health, RePORTER application 9893848, In Vivo Base Editing for Precision Oncology Models (5R01CA229773-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9893848. Licensed CC0.

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