Unraveling mechanisms of tumor suppression in lung cancer

揭示肺癌肿瘤抑制机制

• 批准号: 10633103
• 负责人: Dmitri Petrov
• 金额: $ 43.12万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10633103
• 关键词: Acceleration; Adult; Bar Codes; Biological Models; Biology; CRISPR/Cas technology; Cancer Model; Candidate Disease Gene; Cell Line; Cells; Chromatin; Clinical; Coupled; DNA Sequence Alteration; Data; Data Set; Dedications; Disease; Gene Expression; Gene Silencing; Genes; Genetic; Genetically Engineered Mouse; Genome engineering; Genomics; Genotype; Goals; Growth; Human; Individual; Investigation; Laboratories; Logic; LoxP-flanked allele; Lung Adenocarcinoma; Malignant Neoplasms; Malignant neoplasm of lung; Mediating; Methods; Molecular; Molecular Analysis; Mus; Mutate; Mutation; Output; Pathway interactions; Pattern; Positioning Attribute; Recurrence; Research Personnel; System; TP53 gene; Therapeutic; Time; Tumor Suppression; Tumor Suppressor Genes; Tumor Suppressor Proteins; cancer cell; combinatorial; driving force; fitness; gene function; gene interaction; genome editing; genome sequencing; human cancer mouse model; in vivo; innovation; insight; laboratory experience; mathematical methods; mouse model; multidisciplinary; neoplastic cell; novel; programs; synergism; theories; transcriptome; tumor; tumor growth; tumor initiation; tumorigenesis

PROJECT SUMMARY Genome sequencing has catalogued the somatic alterations in human cancers and identified many putative tumor suppressor 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 uniquely enable the introduction of defined genetic alterations into normal adult cells, which results in the initiation and growth of tumors entirely within their natural in vivo setting. 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. Tumor barcoding coupled with CRISPR/Cas9-mediated gene inactivation and high-throughput barcode sequencing (Tuba-seq) enables the parallel investigation of multiple tumor genotypes in individual mice and allows the large-scale analysis of pairwise tumor suppressor alterations. In Aim 1, we will employ our multiplexed and quantitative Tuba-seq approach to quantify the impact of inactivating many uncharacterized putative tumor suppressor genes on tumor growth in vivo and across time. This analysis will broaden our understanding of the driving forces of tumorigenesis and uncover the potential clinical meaning of these genomic alterations. In Aim 2, we will uncover epistatic genetic interactions between tumor suppressor genes by generating de novo tumors with pairwise combination of tumor suppressor alterations. We will generate the first broad-scale functional understanding of the combinatorial effects of genomic alterations within an autochthonous cancer model. We will uncover the epistatic interactions of these genes and pathways, illuminating novel aspects of tumorigenesis, and potentially highlighting therapeutic vulnerabilities. In Aim 3, we will uncover the molecular programs in cancer cells of different genotypes. To gain insight into how the molecular outputs of single genomic alterations relate to the effects of pairwise alteration, we will also characterize tumors with combined inactivation of cooperative tumor suppressors. This will provide a molecular framework to understand the effects of novel tumor suppressors and uncover the molecular logic that drives the pattern of genomic alterations in human cancer. Our preliminary data, novel genetic systems, and strong collaborative team make us uniquely positioned to conduct these studies. The results of this proposal will be significant because these innovative, multidisciplinary, and highly quantitative approaches will accelerate our understanding of the determinants of cancer growth and will begin the systematic deconvolution of gene function during lung cancer growth in vivo.

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 推定的肿瘤抑制基因。但是，人类癌症通常通过顺序发展 获取多种基因组改变并简单地识别复发性基因组改变不会 必然揭示其对癌症生长的功能重要性。基因工程的鼠标模型 独特地使定义的遗传改变引入正常成年细胞，从而导致 肿瘤的启动和生长完全在其自然体内环境中。但是，他们实用程序的广度是 受到既不容易扩展也不足够定量的事实的限制。增加范围和 体内癌症建模的精度，我们先前整合了常规基因工程小鼠 模型，基于CRISPR/CAS9的体细胞基因组工程以及数学的定量基因组学 方法。肿瘤条形码以及CRISPR/CAS9介导的基因失活和高通量 条形码测序（TUBA-SEQ）可以平行研究单个肿瘤基因型 小鼠并允许对成对肿瘤抑制剂改变的大规模分析。在AIM 1中，我们将采用我们的 多路复用和定量的tuba-seq方法，以量化灭活许多未充满特征的影响 假定的肿瘤抑制基因在体内和整个时间内肿瘤生长。这种分析将扩大我们的 了解肿瘤发生的驱动力并发现这些临床的潜在临床含义 基因组改变。在AIM 2中，我们将发现肿瘤抑制基因之间的上皮遗传相互作用 通过产生与肿瘤抑制剂改变的成对组合的从头肿瘤。我们将生成 对基因组改变的组合作用的首次广泛的功能理解 自我癌症模型。我们将揭示这些基因和途径的上皮相互作用， 阐明肿瘤发生的新方面，并可能突出治疗脆弱性。在AIM 3中，我们 将发现不同基因型癌细胞中的分子程序。洞悉如何 单个基因组改变的分子输出与成对改变的影响有关，我们还将 表征肿瘤的合并抑制剂抑制剂的肿瘤。这将提供分子 了解新型肿瘤抑制剂的影响并揭示驱动的分子逻辑的框架 人类癌症基因组改变的模式。我们的初步数据，新的遗传系统和强大的 协作团队使我们在进行这些研究方面拥有独特的位置。该提议的结果将是 重要的是因为这些创新，多学科和高度定量的方法将加速我们 了解癌症生长的决定因素，并将开始系统的基因反卷积 体内肺癌生长期间的功能。