# In vivo prime editing for precision cancer mouse models

> **NIH NIH R01** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2024 · $594,327

## Abstract

Project Summary/Abstract
Cancer genomic studies have identified a large number of mutations, including point mutations,
insertions/deletions (indels), and structural variants in coding and regulatory regions. For many
uncharacterized mutations, how they contribute to tumor growth and resistance to therapy remains murky. In
vivo functional validation is a pre-requisite for identifying suitable interventions and biomarkers. Rapid and
flexible genome editing platforms are therefore needed to build and validate precision mouse models with
translational value.
Recent advances in CRISPR-based genome editing allow us to directly model cancer mutations in adult
mouse tissues, bypassing the need for germline mouse models. Current CRISPR-based approaches however
are either inefficient (e.g., homology directed repair) or cannot be used to model all types of mutations (e.g.,
base editing). A more flexible genome editing platform is needed to speed up the generation of somatic cancer
mouse models.
We have begun to optimize prime editing as a way to precisely model a wide variety of cancer mutations in
mice. Prime editor (PE)—Cas9 nickase fused to reverse transcriptase—utilizes an extended guide RNA (called
a pegRNA) that doubles as a template for reverse transcriptase to copy information into the genomic target.
We have recently developed an optimized PE that can install cancer mutations in mouse liver. We also
engineered a dual-PE approach to precisely introduce large deletions (up to ~10 kb) with short insertions. This
work provides a foundation upon which to further optimize the delivery of PE to mice and to expand its
application to cancer mouse models.
The goal of this project is to develop and optimize new PE tools to speed up the generation of both
constitutive and inducible somatic mouse models of liver and lung cancer. Aim 1 will define rules for optimal
pegRNA design while validating 100 cancer-associated mutations, establish platforms for somatic cancer
modeling in vivo, define pre-clinical features of PE models, and develop delivery vectors for multiplexed prime
editing in mouse lung. Aim 2 will develop prime editing tools for inducible cancer models. Dual-PE approaches
will be used to make inducible alleles and floxed alleles in mouse liver. We will also generate efficient PE
models to recapitulate large genomic deletions in human cancer. This project will result in new mouse models
that validate driver mutations in liver and lung cancer and will provide a flexible platform to enhance the
translational utility of mouse models of cancer.

## Key facts

- **NIH application ID:** 10909321
- **Project number:** 5R01CA275945-02
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** Wen Xue
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $594,327
- **Award type:** 5
- **Project period:** 2023-08-17 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10909321

## Citation

> US National Institutes of Health, RePORTER application 10909321, In vivo prime editing for precision cancer mouse models (5R01CA275945-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10909321. Licensed CC0.

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