Systematic characterization of cancer variants using single-cell functional genomics

使用单细胞功能基因组学对癌症变异进行系统表征

• 批准号: 10358184
• 负责人: SCOTT W. LOWE
• 金额: $ 44.07万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10358184
• 关键词: Address; Affect; Alleles; Attention; Bayesian Method; Behavior; Biological; Biological Assay; CRISPR screen; Catalogs; Cell Line; Cell Transplantation; Cells; Clustered Regularly Interspaced Short Palindromic Repeats; Data; Development; Disease; Engineering; Environment; Epithelial Cells; Evolution; Exhibits; Experimental Designs; Gene Expression; Genes; Genetic; Genetic Diseases; Goals; Gold; Immune system; In Vitro; KRAS2 gene; Learning; Libraries; Lung; Malignant Neoplasms; Measures; Mediating; Methods; Modeling; Morphologic artifacts; Mus; Mutation; Oncogenic; Pancreas; Phenotype; Play; Population; Procedures; Protocols documentation; Recurrence; Reproducibility; Role; Statistical Models; TP53 gene; Tail; Techniques; Technology; Tissues; Transplantation; Variant; Work; base; base editing; base editor; behavior change; cancer genetics; cell type; clinical phenotype; clinical sequencing; design; experimental study; fitness; flexibility; functional genomics; genetic variant; improved; in vivo; insight; molecular phenotype; mutant; rare variant; response; sensor; single cell analysis; single-cell RNA sequencing; targeted sequencing; tool; transcriptome; transcriptome sequencing; treatment response; tumor; tumor microenvironment; variant of unknown significance

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.

项目概要/摘要 癌症是一种遗传性疾病，肿瘤中的一系列突变会影响其行为及其对癌症的反应。 疗法。大型测序计划已经产生了不同癌症中出现的基因变异的目录。 然而，在解释其影响方面仍然存在巨大挑战。首先，即使变异影响相同 基因，它们的分子表型可能不同。其次，许多变体非常常见，因此它们具有 已被识别，但仍然非常罕见，没有针对性的研究来描述它们的特征。最后，癌症在 一般是由多个突变之间的合作产生的，因此一个变体在一个环境中的功能——细胞 类型、遗传背景或环境——可能只能部分影响其在另一种情况下的行为。绝对数量 对可能的变异和背景的研究主张采取系统的方法进行表型分析。在这里，我们介绍 BEAT-seq（碱基编辑等位基因转录组测序）是一种灵活、可扩展且稳健的方法 通过 CRISPR 介导的碱基编辑设计癌症相关变异并测量产生的效果 通过单细胞 RNA 测序研究细胞表型。我们开发的传感器具有鲁棒性 可以并行量化许多 sgRNA 的碱基编辑效率的测定，使我们能够识别那些 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，并同时捕获两个 sgRNA，编码 程序化编辑和单细胞转录组，携带着它们的表型结果。在目标 1 中，我们 通过生成针对常见癌症变异的经过验证的 sgRNA 来获得 BEAT-seq 证书。我们分析效果 这些变异跨越不同的上皮细胞类型（胰腺和肺）以及不同的遗传 背景来研究语境的作用。最后，我们探讨 BEAT-seq 是否可以通过以下方式分配功能： 构建一个针对约 500 个意义未知的体细胞和种系变异的文库 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 传感器测定的正交表征使贝叶斯方法能够识别编辑和 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 用相对术语表示扰动影响的标准化程序，使比较能够 跨上下文制作。最后，在目标 3 中，我们进行了体内 BEAT-seq 实验，分析携带 通过原位移植到小鼠胰腺中引入了数十种 p53 变体。这项工作使 以以前不可行的规模对癌症变异进行并行表征。我们的结果将提供 深入了解变异在不同情况下如何影响肿瘤表型，阐明未知变异的作用 重要性，并提供一套用于进行和分析单细胞碱基编辑的黄金标准工具 实验。