In Vivo Base Editing for Precision Oncology Models

项目总结 基因突变是癌细胞生长和治疗耐药的主要驱动因素。事实上，一个主要目标是 个性化医学是识别单个肿瘤中特定的基因变化，其概念是定义 这些变化将指导更有效和更有针对性的治疗。虽然这种精确的肿瘤学方法表明 临床前景，正在进行的肿瘤测序工作继续确定潜在的新疾病驱动因素和新的 突变。这些未知的突变等位基因是如何导致疾病的，通常并不明显，需要 功能检查。基因工程小鼠模型(GEMM)提供了一个理想的工具来研究 基因变化对肿瘤生物学的影响，然而现有的方法不够快或足够精确来 重现人类癌症中所见的基因改变谱。我们和其他人使用了基于CRISPR的 基因组编辑，以加快复杂的、基因定义的动物模型的生成。然而，虽然CRISPR 系统又快又简单，基本的工具并不精确，因为它们会导致插入和删除 基因具有功能，但不能模仿人类癌症中最常见的单核苷酸变体。 为了建立体内系统来概括特定的人类癌症相关突变，我们的项目利用了新的 CRISPR工具将Cas9与胞苷脱氨酶偶联并在定义的位置实现直接DNA突变 基因组区域。‘碱基编辑’(BE)技术比现有的同源技术提供了更高的效率和灵活性 定向修复(HDR)方法，消除了交付外源DNA模板的需要。我们有 系统优化BE酶的表达和活性，提高基因组效率 并建立了生物信息学和实验管道，以预测和验证BE工具 重建已知的和新的癌症突变。 在目标1中，从广泛优化的BE酶构建，我们将产生一系列敲入转基因小鼠 为了最大化可使用BE突变的可能基因组区域的数量，并验证BE的活性 这些老鼠使用了一种新的基于荧光的报告系统。此外，使用一种新的传感器分析，我们将识别 所有人类和小鼠的sgRNAs都可以针对复发的癌症相关突变位点。共同努力，这项工作 将确定数千个独立sgRNA的BE效率，并建立第一个体内体细胞基础 编辑平台。在目标2中，我们将使用我们在体内的BE工具来产生新的胰腺和 结直肠癌，并检查每种疾病中不同癌症相关突变的后果。这 这项工作不仅将提供对关键致癌突变的新理解，还将提供对 体内的效用将在多种癌症环境中发挥作用。 通过提供一种简单而有效的途径来捕捉人类疾病等位基因的多样性，我们相信这一新的 精密编辑平台有可能从根本上改变我们设计和实现鼠标的方式 用于转化性研究的癌症模型。

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.