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 可能的肿瘤抑制基因。然而，人类癌症通常是通过连续的 获得多个基因组改变和简单地识别反复发生的基因组改变不是 必然揭示了它们对癌症生长的功能重要性。转基因小鼠模型 独一无二地能够将明确的基因改变引入正常的成年细胞，从而导致 肿瘤的起始和生长完全在其自然的活体环境中。然而，它们的实用范围是 受限于它们既不容易扩展，也不够量化的事实。要扩大范围和 体内肿瘤建模的精确度，我们以前集成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定量基因组学 接近了。肿瘤条码结合CRISPR/Cas9介导的基因失活和高通量 条形码测序(Tuba-seq)能够并行研究个体中的多种肿瘤基因型别 并允许对成对的肿瘤抑制基因改变进行大规模分析。在目标1中，我们将使用我们的 多重和定量的Tuba-Seq方法来量化灭活许多未定性的影响 可能的肿瘤抑制基因对体内和跨时间肿瘤生长的影响。这一分析将拓宽我们的 了解肿瘤发生的驱动力并揭示其潜在的临床意义 基因组的改变。在目标2中，我们将揭示肿瘤抑制基因之间的上位性遗传交互作用 通过肿瘤抑制基因改变的成对组合产生新生肿瘤。我们将生成 首次对基因组改变的组合效应进行了广泛的功能理解 自体肿瘤模型。我们将揭示这些基因和途径的上位性相互作用， 阐明了肿瘤发生的新方面，并潜在地突出了治疗的脆弱性。在目标3中，我们 将发现不同基因类型的癌细胞中的分子程序。要深入了解 单个基因组改变的分子产出与成对改变的影响有关，我们还将 以协同肿瘤抑制因子的联合失活为特征的肿瘤。这将提供一种分子 了解新的肿瘤抑制因子的作用并揭示驱动因素的分子逻辑的框架 人类癌症的基因组改变模式。我们的初步数据，新的基因系统，以及强大的 协作团队使我们在进行这些研究方面处于独特的地位。这项提议的结果将是 重要，因为这些创新、多学科和高度量化的方法将加速我们的 了解癌症生长的决定因素，并将开始系统的基因去卷积 在肺癌体内生长过程中的作用。