(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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要：人类癌症的进化是由多个分子和细胞事件驱动的复杂过程。癌细胞经常携带多种畸变，这些畸变可能以添加，平行，对立，上位或协同的方式起作用。那些相互作用有助于肿瘤发生，进展，转移，耐药性或其他威胁生命的特征。尽管这些相互作用可以通过对肿瘤序列数据的分析来薄弱地推断出，体内阐明遗传相互作用快速建立癌症发展的强大图表和加速治疗发展所必需的。然而，目前，很少有有效的工具可以精确地对体内癌症进行多基因操纵，从而限制了我们的范围准确剖析这些相互作用。我们努力利用单生RNA引导的核酶（RGN）基因组编辑，平行筛查和人类癌的体内建模。最近，我们生成了一个平台系统地直接在体内询问数百个基因座。要克服当前的多基因编辑和实现对多行肿瘤建模的同时性和顺序性的更准确控制，我们利用CPF1，A 可以简单地用CRRNA编辑其目标的RGN独立于tracrrna，从而同时编辑多个具有单个crRNA阵列的基因。我们开发了基于CPF1的初步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，我们将表演 CCA的验证和优化癌症co驱动器的体内双敲击表型。我们将建立它的同时编辑的技术严格，效率和特异性以及开发一组计算管道为了准确调用统计学上显着的基因对。我们将采用这种方法来研究在耶鲁癌症中心和医院的肺癌患者中发现的肿瘤抑制剂，并确定转移至重要器官。在第二个目标中，我们将对CPF1-FLIP系统进行验证和优化癌症靶标的顺序诱变。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和大型多样本转移数据集的公共研究中确定的集合聚集在耶鲁癌症患者身上。然后，我们将将这种方法作为公正的耗竭屏幕来识别目标这对于在特定的致癌背景中生存至关重要。我们将开发新颖的多功能转基因小鼠菌株以及用于直接建模小鼠多基因肿瘤发生的病毒载体。我们将将这些工具结合在一起直接在天然器官中直接在健康细胞中启用健康细胞中的高通量遗传相互作用筛查以识别因果关系驱动肿瘤发生的突变对。我们预计开发和建立这些工具将改变人类癌症的多基因肿瘤建模和临床前研究，直接解决NCI挑衅性问题4。这些强大的工具包将使科学家同时或顺序地靶向任何基因对或组合，评估其体内相互作用在肿瘤进展，转移，合成致死性，药物中的表型结果癌症进化的敏感性或其他过程。