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：
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
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/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

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.

项目摘要/摘要癌症是一种遗传疾病，肿瘤中的一组突变会影响其行为及其对疗法。大型测序倡议产生了在不同的取消中产生的基因变体的目录。但是，在解释其影响方面仍然存在重大挑战。首先，即使变体影响相同基因，它们的分子表型可能是不同的。其次，许多变体很常见被鉴定出来，但仍然足够罕见，没有针对性的研究表征它们。最后，癌症一般是由多个突变之间的合作引起的，因此变体在一种情况下的功能 - 细胞类型，遗传背景或环境 - 可能只部分地告知其行为在另一种行为。纯粹的数字可能的变体和上下文主张采用系统的方法进行表型。在这里，我们在这里 Beat-Seq（基础编辑等位基因转录组测序），一种灵活，可扩展和可靠的方法通过CRISPR介导的基础编辑和测量产生的效果的工程癌症相关的变体通过单细胞RNA测序在细胞表型上。鲁棒性从我们开发传感器的发展遵循可以并行量化许多SGRNA的基本编辑效率的测定，从而使我们能够识别这些效率这可靠地引入了癌症变体。然后，我们利用改进的扰动式协议，使我们能够以汇总格式介绍变体的库，并简单地捕获SGRNA，编码编程和单细胞转录组带有其表型后果。在AIM 1中，我们通过产生靶向常见癌症变异的验证的SGRNA，证书Beat-Seq。我们介绍效果在不同上皮细胞类型的这些变体（胰腺和肺）以及不同遗传的这些变体中研究背景的作用的背景。最后，我们探索Beat-Seq是否可以通过构建一个针对约500个未知意义的体细胞和种系变体的库 MSK IMPACT测序。这些任务在分析复杂性中逐渐增长。在AIM 2中，我们确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中的正交表征使贝叶斯方法可以识别编辑和未经编辑的细胞，应对影响许多单细胞屏幕的中心挑战。然后我们开发数据以相对术语表示扰动效应的归一化过程，使比较成为跨环境制造。最后，在AIM 3中，我们在体内Beat-Seq实验中进行携带的细胞分析细胞原位移植引入小鼠胰腺中引入的数十个p53变体。这项工作可以实现以前不可行的量表对癌症变体的平行表征。我们的结果将提供了解变体如何影响不同情况下肿瘤表型，阐明未知变异的作用意义，并提供一组金标准工具，用于进行和分析单细胞基础编辑实验。