In vivo prime editing for precision cancer mouse models

项目摘要/摘要 癌症基因组研究已经确定了大量的突变，包括点突变， 插入/缺失(Indels)以及编码区和调控区的结构变体。对许多人来说 未知的突变，它们如何促进肿瘤生长和对治疗的耐药性仍不清楚。在……里面 活体功能验证是确定合适的干预措施和生物标志物的先决条件。快速且 因此，需要灵活的基因组编辑平台来构建和验证精确的小鼠模型 翻译价值。 基于CRISPR的基因组编辑的最新进展使我们能够直接模拟成人的癌症突变 小鼠组织，绕过对生殖系小鼠模型的需要。然而，当前基于CRISPR的方法 或者低效(例如，同源定向修复)或者不能用于对所有类型的突变进行建模(例如， 基本编辑)。需要一个更灵活的基因组编辑平台来加速体细胞癌的产生 老鼠模型。 我们已经开始优化素材编辑，将其作为一种精确建模各种癌症突变的方法 老鼠。主编(PE)-Cas9尼克酶与逆转录酶融合-利用扩展的引导RNA(称为 一种pegRNA)，兼作逆转录酶的模板，用于将信息复制到基因组靶标中。 我们最近开发了一种优化的PE，可以在小鼠肝脏安装癌症突变。我们也 设计了一种双PE方法，通过短插入精确引入大片段缺失(最高可达~10kb)。这 这项工作为进一步优化PE对小鼠的传递和扩大其 应用于小鼠肿瘤模型。 该项目的目标是开发和优化新的PE工具，以加快两者的生成 肝癌和肺癌的结构性和可诱导性体细胞小鼠模型。目标1将定义最优规则 在验证100个癌症相关突变的同时，pegRNA设计为体细胞癌建立了平台 体内建模，定义PE模型的临床前特征，并开发多路素材的传递载体 在小鼠肺中编辑。AIM 2将为可诱发癌症模型开发主要的编辑工具。双PE方法 将用于在小鼠肝脏中制造可诱导的等位基因和花序等位基因。我们还将生成高效的PE 重述人类癌症中大量基因组缺失的模型。这个项目将产生新的鼠标模型 验证肝癌和肺癌的驱动程序突变，并将提供一个灵活的平台来增强 癌症小鼠模型的翻译应用。

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.