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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.

项目总结

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.