(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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要基因组测序已分类人类癌症的体细胞改变，并确定了许多推定的驱动基因。但是，人类癌的癌症通常通过顺序获取多个基因组改变并简单地识别复发性基因组改变并不一定揭示其对癌症生长的功能重要性。基因工程的鼠标模型已成为对体内癌症基因功能的分析，但是它们的效用的广度受到了以下事实的限制既不容易扩展也不足够定量。增加体内癌的范围和精度建模，我们以前整合了常规遗传工程的鼠标模型，基于CRISPR/CAS9 具有数学方法的体细胞基因组工程和定量基因组学。我们开发了使用条形码sgrna-的肺癌小鼠模型中灭活多个基因的方法包含慢病毒载体。这种肿瘤条形码与测序（Tuba-Seq）方法可发现的大小每种肿瘤，都可以平行研究单个小鼠中多种肿瘤基因型，并允许在自chon癌模型中生成了基因功能的大规模图。我们的初步数据和新颖的遗传系统，以及我们具有癌症专业知识的研究人员的专门合作团队遗传学，小鼠模型，基因组编辑，临床癌症护理和定量建模使我们独特定位进行这些研究。在此提案中，我们将扩展tuba-seq，以量化组合遗传改变通过开发和验证平台的快速和验证遗传改变体内肿瘤生长之间的相互作用的定量分析。启用多路复用以及对时间控制基因组改变对体内癌细胞生长的影响的定量分析，我们还将开发一个在已建立的肺肿瘤中进行诱导基因组编辑的系统。最后，我们将发展新颖的体内方法，用于全面和广泛地发现癌细胞中的基因表达程序并联不同的基因型。通过多重体内遗传改变，推定癌症的作用可以以前所未有的规模和解决方案来发现驾驶员。该提案的结果将是重要的因为遗传的具有成本效益，定量和多重分析的创新方法癌症发病机理的决定因素将阐明肿瘤发生的新方面，并加速我们的能力了解癌症的进化，药物反应和耐药性。