Systematic characterization of cancer variants using single-cell functional genomics

项目总结/摘要 癌症是一种遗传性疾病，肿瘤中的一组突变会影响其行为和对癌症的反应。 治疗大型测序计划已经产生了不同癌症中出现的基因变异目录。 然而，在解释其影响方面仍然存在重大挑战。首先，即使变异影响相同的 基因，它们的分子表型可能不同。其次，许多变体足够常见， 已经确定，但仍然足够罕见，没有针对性的研究已经确定了它们的特征。最后，癌症 一般产生于多个突变之间的合作，所以一个变异体在一个上下文细胞中的功能 类型，遗传背景或环境-可能只是部分地告知它的行为在另一个。数量之多 可能的变异和背景的争论，采取系统的方法表型。在这里，我们介绍 BEAT-seq（碱基编辑等位基因转录组测序），一种灵活、可扩展和强大的方法，用于 通过CRISPR介导的碱基编辑工程化癌症相关变体并测量所产生的效果 通过单细胞RNA测序对细胞表型的影响。鲁棒性来自我们开发的传感器 该测定可以平行量化许多sgRNA的碱基编辑效率，使我们能够鉴定那些 可靠地引入癌症变异。然后，我们利用改进的Perturb-seq协议，使我们能够 以合并的形式引入变体文库，并同时捕获两种sgRNA，编码 程序编辑和单细胞转录组，携带它们的表型后果。目标1： 通过产生靶向常见癌症变体的经验证的sgRNA，我们分析了 这些变异在不同的上皮细胞类型-胰腺和肺-以及不同的遗传学类型中的分布， 背景研究语境的作用。最后，我们探索BEAT-seq是否可以通过 构建一个针对约500种未知意义的体细胞和种系变体的库，这些变体是通过以下方法鉴定的 MSK-IMPACT测序这些任务的分析复杂性逐渐增加。在目标2中，我们建立了严格的 用于解释单细胞功能基因组学实验的统计管道。我们证明了 来自传感器测定的正交表征使得贝叶斯方法能够识别编辑的和 未经编辑的细胞，解决了影响许多单细胞屏幕的核心挑战。然后我们开发一个数据 标准化程序，用于以相对术语表示扰动的影响， made制作across横过context上下文.最后，在目的3中，我们进行了体内BEAT-seq实验，分析了携带 通过原位移植引入小鼠胰腺的几十种p53变体。这项工作使 在以前不可行的规模上并行表征癌症变体。我们的结果将提供 深入了解变体如何在不同背景下影响肿瘤表型，阐明未知基因变体的作用， 重要性，并提供了一套黄金标准的工具，用于进行和分析单细胞的基础编辑 实验

PROJECT SUMMARY/ABSTRACT Cancer is a genetic disease, and the set of mutations in a tumor affects both its behavior and its response to therapies. Large sequencing initiatives have produced catalogs of gene variants arising in different cancers. Substantial challenges remain, however, in interpreting their effects. First, even when variants affect the same gene, their molecular phenotypes may be distinct. Second, many variants are common enough that they have been identified, but still sufficiently rare that no targeted studies have characterized them. Finally, cancer in general arises from cooperation among multiple mutations, so the function of a variant in one context—cell type, genetic background, or environment—may only partly inform its behavior in another. The sheer number of possible variants and contexts argues for taking a systematic approach to phenotyping. Here, we present BEAT-seq (Base Editing Allele Transcriptome sequencing), a flexible, scalable, and robust approach for engineering cancer-associated variants by CRISPR-mediated base editing and measuring the resulting effects on cellular phenotype by single-cell RNA sequencing. Robustness follows from our development of a sensor assay that can quantify the base editing efficiency of many sgRNAs in parallel, enabling us to identify those that reliably introduce cancer variants. We then exploit an improved Perturb-seq protocol, enabling us to introduce libraries of variants in pooled format and simultaneously capture both the sgRNAs, encoding the programmed edits, and single-cell transcriptomes, carrying their phenotypic consequences. In Aim 1, we credential BEAT-seq by generating validated sgRNAs targeting common cancer variants. We profile the effects of these variants across different epithelial cell types—pancreatic and lung—and across different genetic backgrounds to study the role of context. Finally, we explore whether BEAT-seq can assign function by constructing a library targeting ~500 somatic and germline variants of unknown significance identified through MSK-IMPACT sequencing. These tasks grow gradually in analytical complexity. In Aim 2, we establish rigorous statistical pipelines for the interpretation of single-cell functional genomics experiments. We show that the orthogonal characterization from the sensor assay enables a Bayesian approach to identify edited and unedited cells, addressing a central challenge that affects many single-cell screens. We then develop a data normalization procedure for representing perturbations’ effects in relative terms, enabling comparisons to be made across contexts. Finally, in Aim 3 we conduct in vivo BEAT-seq experiments profiling cells carrying dozens of p53 variants introduced by orthotopic transplantation into mouse pancreases. This work enables parallelized characterization of cancer variants on a scale not previously feasible. Our results will provide insight into how variants affect tumor phenotype in different contexts, illuminate the role of variants of unknown significance, and provide a gold standard set of tools for conducting and analyzing single-cell base editing experiments.