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

基本信息

批准号：
10599180
负责人：
SCOTT W. LOWE
金额：
$ 43.19万
依托单位：
SLOAN-KETTERING INST CAN RESEARCH
依托单位国家：
美国
项目类别：
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/10599180
关键词：
Address Affect Alleles Attention Bayesian Method Behavior Biological Biological Assay CRISPR screen Calibration Catalogs Cell Line Cell Transplantation Cells Clustered Regularly Interspaced Short Palindromic Repeats Credentialing Data Development Disease Engineering Environment Epithelial Cells Evolution Exhibits Experimental Designs Gene Expression Genes Genetic Genetic Diseases Germ-Line Mutation Goals 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 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 parallelization rare variant response sensor single-cell RNA sequencing targeted sequencing tool transcriptome transcriptome sequencing treatment response tumor tumor microenvironment variant detection variant of unknown significance

项目摘要

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。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，首先，即使变异影响相同，在解释它们的影响方面仍然存在巨大挑战。基因，它们的分子表型可能不同，其次，许多变异足够常见，以至于它们具有。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。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。在这里，我们提出了可能的变异和背景，主张采取系统的方法进行表型分析。 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 测序对细胞表型进行分析，我们开发了一种传感器。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然后我们利用改进的 Perturb-seq 协议，使我们能够以合并格式引入变体文库并同时捕获两个 sgRNA，编码在目标 1 中，我们研究了程序化编辑和单细胞转录组，以及它们的表型结果。我们通过生成针对常见癌症变异的经过验证的 sgRNA 来验证 BEAT-seq 的效果。这些变异跨越不同的上皮细胞类型（胰腺和肺）以及不同的遗传最后，我们探讨 BEAT-seq 是否可以通过分配功能。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 测序的分析复杂性逐渐增加，我们建立了严格的目标。我们展示了用于解释单细胞功能基因组学实验的统计管道。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，然后我们开发了数据。用相对术语表示扰动影响的标准化程序，使比较能够最后，在目标 3 中，我们进行了体内 BEAT-seq 实验，对携带的细胞进行分析。这项工作通过原位移植将数十种 p53 变体引入小鼠胰腺中。我们的结果将提供以前不可能的规模的癌症变异的并行表征。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。