(PQ4) Novel tools for in vivo study of genetic interactions in cancer progression

(PQ4) 用于体内研究癌症进展中遗传相互作用的新工具

• 批准号: 9982276
• 负责人: Sidi Chen
• 金额: $ 57.66万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9982276
• 关键词: Address; Alleles; Atlas of Cancer Mortality in the United States; Biological; Biological Models; Biomedical Engineering; Brain; Cancer Hospital; Cancer Model; Cancer Patient; Cell Line; Cells; Companions; Complex; Data; Data Set; Development; Disease Progression; Drug resistance; Engineering; Event; Evolution; Gene Chips; Genes; Genetic; Genetic study; Genetically Engineered Mouse; Genomics; Growth; Guide RNA; Heterogeneity; Human; Joints; Knock-out; Life; Liver; Lung; Malignant Neoplasms; Malignant neoplasm of lung; Mammalian Cell; Methodology; Modeling; Molecular; Mouse Strains; Mus; Mutagenesis; Mutate; Mutation; Nature; Neoplasm Metastasis; Nonmetastatic; Oncogenes; Oncogenic; Organ; Outcome; Pathologic; Patients; Phenotype; Physiological; Play; Primary Neoplasm; Process; Regulatory Element; Relapse; Role; Sampling; Science; Scientist; Specificity; System; TP53 gene; Testing; The Cancer Genome Atlas; Transgenic Mice; Tumor Suppressor Proteins; Validation; Viral Vector; Yale Cancer Center; base; cancer cell; causal variant; clinically relevant; cohort; computational pipelines; drug sensitivity; effective therapy; endonuclease; flexibility; genetic manipulation; genome editing; genome-wide; high throughput screening; human disease; human model; in vivo; in vivo Model; insight; mouse model; neoplastic cell; novel; preclinical study; screening; therapeutic development; tool; transplant model; tumor; tumor progression; tumorigenesis; vector

PROJECT SUMMARY: The evolution of human cancer is a complex process driven by multiple molecular and cellular events. Cancer cells often harbor numerous aberrations that can act in additive, parallel, antagonistic, epistatic or synergistic fashion. Those interactions contribute to tumorigenesis, progression, metastasis, drug resistance or other life-threatening features. While these interactions can be weakly inferred from analysis of tumor sequence data, elucidating genetic interactions in vivo is essential for rapidly building a robust map of cancer development and to accelerate therapeutic developments. However, there are currently few effective tools for precise multigenic manipulation of cancer in vivo, limiting our scope for accurately dissecting these interactions. We endeavored to harness single-effector RNA-guided endonucleases (RGNs) for genome editing, parallel screening and in vivo modeling of human cancer. Recently, we generated a platform to systematically interrogate several hundred loci directly in vivo. To overcome current limitations in multigene editing and achieve more accurate control of simultaneity and sequentiality of multi-allelic tumor modeling, we utilized Cpf1, an RGN that can edit its target simply with crRNAs independent of tracrRNA thus allowing simultaneous editing of multiple genes with a single crRNA array. We developed a preliminary Cpf1-based crRNA array screening (CCAS) system in mammalian cells, and applied it in mouse models of progression and metastasis. In our first aim, we will perform validation and optimization of CCAS for in vivo double-knockout phenotyping of cancer co-drivers. We will establish its technical rigor, efficiency and specificity for simultaneous editing, as well as developing a set of computational pipelines for accurate calling of statistically significant gene pairs. We will apply this approach to study the genetic interactions of tumor suppressors found in lung cancer patients at Yale Cancer Center and Hospital, and identify potential co-drivers of metastasis to vital organs. In the second aim, we will carry out validation and optimization of a Cpf1-Flip system for sequential mutagenesis of cancer targets. We will demonstrate its broader applicability by testing clinically relevant gene sets identified from public studies of the genomics of metastasis as well as a large multi-sample metastasis dataset gathered on Yale cancer patients. We will then apply this methodology as an unbiased depletion screen to identify targets that are essential for survival in specific oncogenic backgrounds. We will develop novel versatile transgenic mouse strains and companion viral vectors for direct modeling of multigenic tumorigenesis in mice. We will combine these tools to enable high-throughput genetic interaction screening in healthy cells directly in the native organ to identify causative mutation pairs that drive tumorigenesis. We anticipate that developing and establishing these tools will transform multigenic tumor modeling and pre-clinical studies of human cancer, directly addressing NCI Provocative Question 4. These powerful toolkits will enable scientists to target any gene pairs or combinations simultaneously or sequentially, assessing the phenotypic outcome of their in vivo interactions in tumor progression, metastasis, synthetic lethality, drug sensitivity or other processes in cancer evolution.

项目概要： 人类癌症的演变是一个由多种分子和细胞事件驱动的复杂过程。癌细胞通常 具有许多畸变，这些畸变可以以相加、平行、拮抗、上位或协同的方式起作用。那些 相互作用导致肿瘤发生、进展、转移、耐药性或其他危及生命的特征。而 这些相互作用可以从肿瘤序列数据的分析中微弱地推断出来，阐明体内的遗传相互作用， 这对于快速建立癌症发展的强大地图和加速治疗发展至关重要。然而，在这方面， 目前几乎没有有效的工具用于精确的体内多基因操作癌症，限制了我们的范围， 准确地剖析这些相互作用。我们致力于利用单效应RNA引导的核酸内切酶（RGN）， 基因组编辑、平行筛选和人类癌症的体内建模。最近，我们创建了一个平台， 直接在体内系统地询问几百个基因座。为了克服目前多基因编辑的局限性， 为了更准确地控制多等位基因肿瘤建模的准确性和顺序性，我们利用Cpf 1， RGN可以简单地用不依赖于tracrRNA的crRNA编辑其靶标，从而允许同时编辑多个 基因与单个crRNA阵列。我们开发了一个初步的基于Cpf 1的crRNA阵列筛选（CCAS）系统， 哺乳动物细胞，并将其应用于进展和转移的小鼠模型中。在我们的第一个目标中，我们将执行 CCAS用于癌症辅助驱动因子的体内双敲除表型的验证和优化。我们将建立自己的 同时编辑的技术严谨性，效率和特异性，以及开发一套计算管道 用于准确调用统计学上显著的基因对。我们将应用这种方法来研究基因的相互作用， 在耶鲁癌症中心和医院的肺癌患者中发现的肿瘤抑制因子，并确定潜在的共同驱动因素， 转移到重要器官。在第二个目标中，我们将进行Cpf 1-Flip系统的验证和优化， 癌症靶标的连续诱变。我们将通过测试临床相关基因来证明其更广泛的适用性， 从转移基因组学的公开研究中确定的数据集以及大型多样本转移数据集 耶鲁大学癌症患者的数据然后，我们将应用这种方法作为一个公正的耗尽屏幕，以确定目标 在特定的致癌背景下生存所必需的。我们将开发新的通用转基因小鼠品系 以及用于小鼠中多基因肿瘤发生的直接建模的伴随病毒载体。我们将联合收割机结合这些工具， 能够直接在天然器官中的健康细胞中进行高通量遗传相互作用筛选， 基因突变对导致肿瘤发生。我们预计，开发和建立这些工具将改变 多基因肿瘤建模和人类癌症的临床前研究，直接解决NCI挑衅性问题4。 这些强大的工具包将使科学家能够同时或顺序地靶向任何基因对或组合， 评估它们在肿瘤进展、转移、合成致死性、药物治疗、肿瘤治疗中的体内相互作用的表型结果， 敏感性或癌症演变中的其他过程。