(PQ4) Quantitative and multiplexed analysis of gene function in cancer in vivo

(PQ4)体内癌症基因功能的定量和多重分析

• 批准号: 10238887
• 负责人: Dmitri Petrov
• 金额: $ 46.2万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10238887
• 关键词: Address; Adult; Bar Codes; Biological Models; CRISPR/Cas technology; Cancer Cell Growth; Cancer Model; Cell Line; Cells; Clinical; Consumption; DNA Sequence Alteration; Data; Detection; Development; Evolution; Gene Expression; Gene Expression Profiling; Gene Silencing; Generations; Genes; Genetic; Genetic Determinism; Genetically Engineered Mouse; Genome engineering; Genomics; Genotype; Goals; Growth; Human; Individual; Investigation; Lentivirus Vector; Lung Neoplasms; Malignant Neoplasms; Malignant neoplasm of lung; Maps; Mediating; Methods; Modeling; Mouse Strains; Mus; Mutation; Neoplasms; Pathogenesis; Pathway interactions; Pharmaceutical Preparations; Population; Positioning Attribute; Recurrence; Research Personnel; Resistance; Resolution; Resources; Statistical Methods; Structure; System; Time; Tumor Suppressor Genes; Tumor Suppressor Proteins; Validation; analytical method; base; cancer care; cancer cell; cancer genetics; combinatorial; cost effective; driving force; gene function; genetic analysis; genome editing; genome sequencing; in vivo; innovation; mRNA Expression; mathematical methods; mouse model; novel; programs; response; single-cell RNA sequencing; tumor; tumor barcoding and sequencing; tumor growth; tumor initiation; tumorigenesis; vector

PROJECT SUMMARY Genome sequencing has catalogued the somatic alterations in human cancers and identified many putative driver genes. However, human cancers generally evolve through the sequential acquisition of multiple genomic alterations and simply identifying recurrent genomic alterations does not necessarily reveal their functional importance to cancer growth. Genetically engineered mouse models have become a mainstay for the analysis of gene function in cancer in vivo, however the breadth of their utility is limited by the fact that they are neither readily scalable nor sufficiently quantitative. To increase the scope and precision of in vivo cancer modeling, we previously integrated conventional genetically-engineered mouse models, CRISPR/Cas9-based somatic genome engineering, and quantitative genomics with mathematical approaches. We developed methods to inactivate multiple genes in parallel in mouse models of lung cancer using pools of barcoded sgRNA- containing lentiviral vectors. This tumor barcoding with sequencing (Tuba-seq) approach uncovers the size of each tumor, enables the parallel investigation of multiple tumor genotypes in individual mice, and allows the generation of large-scale maps of gene function within autochthonous cancer models. Our preliminary data and novel genetic systems, as well as our dedicated and collaborative team of investigators with expertise in cancer genetics, mouse models, genome-editing, clinical cancer care, and quantitative modeling make us uniquely positioned to conduct these studies. In this proposal, we will extend Tuba-seq to quantify the effect of combinatorial genetic alterations through the development and validation of a platform for the rapid and quantitative analysis of interactions between genetic alterations on tumor growth in vivo. To enable multiplexed and quantitative analysis of the impact of temporally controlled genomic alterations on cancer cell growth in vivo, we will also develop a system for inducible genome editing in established lung tumors. Finally, we will develop novel in vivo approaches to comprehensively and broadly uncover the gene expression programs in cancer cells of different genotypes in parallel. Through multiplexed in vivo genetic alterations, the effect of putative cancer drivers can be uncovered at an unprecedented scale and resolution. The results of this proposal will be significant because innovative methods for the cost-effective, quantitative, and multiplexed analysis of the genetic determinants of cancer pathogenesis will illuminate novel aspects of tumorigenesis and accelerate our ability to understand cancer evolution, drug responses, and therapy resistance.

项目总结 基因组测序已经对人类癌症中的体细胞变化进行了分类，并识别了许多 推定的驾驶员基因。然而，人类癌症通常是通过连续获得多个 基因组改变和简单地识别重复的基因组改变并不一定揭示它们的 对癌症生长具有重要作用。转基因小鼠模型已经成为 分析体内癌症中的基因功能，然而，它们的实用范围受到以下事实的限制 既不容易扩展，也不够量化。提高体内肿瘤的范围和精确度 建模，我们之前集成了传统的基因工程小鼠模型，基于CRISPR/Cas9 体细胞基因组工程，以及数学方法的定量基因组学。我们开发了 利用条形码sgRNA池在小鼠肺癌模型中并行灭活多个基因的方法- 含有慢病毒载体的。这种肿瘤条形码测序(Tuba-Seq)方法揭示了 每一种肿瘤，能够在单个小鼠中并行研究多种肿瘤基因类型，并允许 在本土癌症模型中生成大规模的基因功能图。我们的初步数据和 新的基因系统，以及我们具有癌症专业知识的敬业和合作的研究团队 遗传学、小鼠模型、基因组编辑、临床癌症护理和定量建模使我们独一无二 有能力进行这些研究。在这项提案中，我们将扩展Tuba-seq以量化 通过开发和验证组合遗传改变的平台进行快速和 基因改变与体内肿瘤生长相互作用的定量分析。启用多路传输 以及定量分析时间受控的基因组变化对体内癌细胞生长的影响， 我们还将开发一种在已建立的肺癌中进行可诱导基因组编辑的系统。最后，我们将发展 全面而广泛地揭示癌细胞基因表达程序的体内新方法 不同的基因类型是平行的。通过体内多重基因改变，推测癌症的影响 驱动因素可以以前所未有的规模和分辨率被发现。这项提议的结果将是重大的 因为具有成本效益的、定量的和多重的基因分析的创新方法 癌症发病机制的决定因素将阐明肿瘤发生的新方面，并加速我们 了解癌症的演变、药物反应和治疗耐药性。