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 基因组测序已经将人类癌症中的体细胞改变编目，并确定了许多 推定的肿瘤抑制基因。然而，人类癌症通常通过连续的 获得多个基因组改变和简单地鉴定复发性基因组改变并不 必然揭示了它们对癌症生长的功能重要性。遗传工程小鼠模型 独特地能够将定义的遗传改变引入正常的成体细胞，这导致 肿瘤的起始和生长完全在其天然的体内环境中。然而，其效用的广度是 受限于它们既不容易扩展也不充分定量的事实。扩大范围， 为了提高体内癌症建模的精确性，我们先前整合了常规的基因工程小鼠， 模型，基于CRISPR/Cas9的体细胞基因组工程，以及定量基因组学与数学 接近。肿瘤条形码与CRISPR/Cas9介导的基因失活和高通量耦合 条形码测序（Tuba-seq）使得能够在个体中并行研究多种肿瘤基因型。 小鼠，并允许大规模分析成对的肿瘤抑制基因的改变。在目标1中，我们将使用我们的 多重和定量Tuba-seq方法，以量化灭活许多未表征的 推定的肿瘤抑制基因对体内和跨时间肿瘤生长的影响。这一分析将扩大我们的 了解肿瘤发生的驱动力，并揭示这些潜在的临床意义 基因组改变在目标2中，我们将揭示肿瘤抑制基因之间的上位性遗传相互作用 通过产生具有成对组合的肿瘤抑制基因改变的新生肿瘤。我们将生成 第一次大规模的功能性理解的组合效应的基因组改变在一个 原位癌模型。我们将揭示这些基因和途径的上位相互作用， 阐明了肿瘤发生的新方面，并潜在地突出了治疗的弱点。在目标3中，我们 将揭示不同基因型癌细胞的分子程序。为了深入了解 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 用协同肿瘤抑制因子的联合失活来表征肿瘤。这将提供一个分子 框架，以了解新的肿瘤抑制剂的作用，并揭示驱动肿瘤发生的分子逻辑。 人类癌症的基因组改变模式我们的初步数据，新的遗传系统， 协作团队使我们处于进行这些研究的独特地位。这项提议的结果将是 重要的是，这些创新的、多学科的和高度定量的方法将加速我们的研究。 了解癌症生长的决定因素，并将开始系统地解卷积基因 在体内肺癌生长过程中发挥作用。