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(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：
10469407
负责人：
Dmitri Petrov
金额：
$ 44.64万
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 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10469407
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经将人类癌症中的体细胞改变编目，并确定了许多推定的驱动基因。然而，人类癌症通常通过连续获得多个细胞因子而进化。基因组改变和简单地鉴定复发性基因组改变并不一定揭示其对癌症生长的重要性。基因工程小鼠模型已成为研究的主要工具。在体内癌症中基因功能的分析，然而，它们的效用的广度受到以下事实的限制：既不容易扩展也不充分定量。增加体内癌症的范围和精度建模，我们之前整合了传统的基因工程小鼠模型，基于CRISPR/Cas9的体细胞基因组工程和数学方法的定量基因组学。我们开发使用条形码化的sgRNA库在肺癌小鼠模型中平行扩增多个基因的方法- 含有慢病毒载体。这种肿瘤条形码测序（Tuba-seq）方法揭示了肿瘤细胞的大小。每个肿瘤，使得能够在单个小鼠中平行研究多种肿瘤基因型，并允许在本地癌症模型中生成大规模基因功能图。我们的初步数据和新的遗传系统，以及我们在癌症方面具有专业知识的专门和协作的研究人员团队遗传学、小鼠模型、基因组编辑、临床癌症护理和定量建模使我们成为独一无二的来进行这些研究。在本提案中，我们将扩展Tuba-seq以量化通过开发和验证一个平台，定量分析遗传改变对体内肿瘤生长的相互作用。要启用多路复用以及定量分析时间控制的基因组改变对体内癌细胞生长的影响，我们还将开发一种系统，用于在已建立的肺肿瘤中进行诱导性基因组编辑。最后，我们将开发全面和广泛揭示癌细胞中基因表达程序的体内新方法不同基因型的基因。通过多重体内遗传改变，可以以前所未有的规模和分辨率发现驱动程序。这一提议的结果将是重大的因为用于遗传学的成本效益，定量和多重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，并加快我们的能力，了解癌症演变、药物反应和治疗耐药性。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Phagocytosis increases an oxidative metabolic and immune suppressive signature in tumor macrophages.

DOI：
10.1084/jem.20221472
发表时间：
2023-06-05
期刊：
The Journal of experimental medicine
影响因子：
0
作者：
通讯作者：

Quantitative In Vivo Analyses Reveal a Complex Pharmacogenomic Landscape in Lung Adenocarcinoma.

DOI：
10.1158/0008-5472.can-21-0716
发表时间：
2021-09-01
期刊：
Cancer research
影响因子：
11.2
作者：
Li C;Lin WY;Rizvi H;Cai H;McFarland CD;Rogers ZN;Yousefi M;Winters IP;Rudin CM;Petrov DA;Winslow MM
通讯作者：
Winslow MM

A new system for multiplexed mosaic analysis of gene function in the mouse.