Systematic characterization of cancer variants using single-cell functional genomics

项目摘要/摘要 癌症是一种遗传性疾病，肿瘤中的一组突变既影响其行为，也影响其对 治疗。大型测序计划已经制作了不同癌症中出现的基因变异的目录。 然而，在解释它们的影响方面仍然存在重大挑战。首先，即使变异会影响相同的 它们的分子表型可能是不同的。其次，许多变种足够常见，以至于它们有 已经确定，但仍然非常罕见，以至于没有针对性的研究来描述它们的特征。最后，癌症在 一般产生于多个突变之间的合作，因此一个变体在一个上下文细胞中的功能 类型、遗传背景或环境--可能只在一定程度上影响了它在另一个地方的行为。纯粹的数字 对可能的变种和背景的研究表明，应该采取系统的方法进行表型分析。在这里，我们向大家介绍 BEAT-SEQ(碱基编辑等位基因转录组测序)，这是一种灵活、可扩展和健壮的方法 通过CRISPR介导的碱基编辑来设计癌症相关变体并测量由此产生的效果 单细胞rna测序对细胞表型的影响。健壮性源于我们开发的传感器 一种可以量化并行的许多sgRNA的碱基编辑效率的方法，使我们能够识别 它可靠地引入了癌症变种。然后，我们利用改进的perturb-seq协议，使我们能够 以池格式引入变异库，并同时捕获两个sgRNAs，编码 程序化编辑和单细胞转录本，携带其表型结果。在目标1中，我们 通过生成针对常见癌症变体的有效sgRNA来进行凭证节拍-seq。我们描述了这些影响 这些变异跨越不同的上皮细胞类型-胰腺和肺-以及跨越不同的基因 背景研究语境的作用。最后，我们探讨了BTH-SEQ是否可以通过 构建针对~500个未知意义体细胞和生殖系变异体的文库 MSK-影响测序。这些任务逐渐增加了分析的复杂性。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实验的统计管道。我们证明了 传感器分析的正交特征使贝叶斯方法能够识别编辑过的和 未编辑的单元格，解决了影响许多单元格屏幕的中心挑战。然后我们开发一个数据 用相对术语表示扰动影响的归一化过程，使比较能够 在不同的背景下制作。最后，在目标3中，我们进行了活体节拍-序列实验，分析细胞携带 小鼠胰腺原位移植引入数十个P53变异体。这项工作使 在以前不可行的规模上对癌症变异进行并行表征。我们的结果将提供 洞察变异如何在不同的背景下影响肿瘤表型，阐明未知变异的作用 意义，并提供了一套用于执行和分析单元格基础编辑的黄金标准工具 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.