Functional Analysis of Oncogenic Networks in Primary Organoids

描述(申请人提供)：癌症产生于肿瘤抑制基因和癌基因的离散组合中的多种突变和基因组异常的获得和协同作用，称为“驱动因素”。大型癌症基因组测序研究，如TCGA，现已投入使用，癌症目标发现和发展网络(CTDD)寻求“弥合产生的海量数据……与将这些数据用于人类癌症治疗发展的能力之间的差距”。CTDD计划的第二个目标是，在五年内，“整个CTDD网络有望识别和描述大约25种或更多(如果可能)癌症类型的靶点。”对于申请者来说，“拥有或建立利用多种癌症类型的数据集进行深入分析和实验方法的能力。广泛的”覆盖范围“是这一倡议的范例。”在这里，丰富的TCGA数据将直接与使用以高通量格式排列的不同组织的原始鼠3D器官培养对候选癌症驱动模块进行强大的体外功能验证相结合。在目标1中，Hanlee Ji和Sylvia Plevtis小组将使用监督贝叶斯分析和主调节器的非监督模块网络分析的互补方法，从来自多种实体肿瘤类型的TCGA数据集中识别共同分离的突变模块。在目标2中，这些按临床意义分层的优先突变模块将在Calvin Kuo小组开发的可广泛应用、可多路复用的体外3D初级有机系统中进行直接功能验证，该系统符合组合基因工程的要求。与比尔·哈恩合作，这将利用HIGH 通过慢病毒导入cDNA或shRNA来系统地询问扩增和缺失内的基因，在发生这些拷贝数变化的TCGA突变背景下进行上下文模拟。此外，来自不同组织的共分离突变模块将在类器官培养中进行系统删除，以定义最小的模块组成，我们将进行过程开发，以扩大可以建模类器官的组织范围。总体而言，这些研究描述了生物信息学和体外建模方法，这些方法可强大地移植到各种器官系统，用于对各种TCGA数据集的功能询问，对RFA-CA-12-006和CTDD具有高度响应，并伴随着对癌症生物学、诊断和治疗的影响。

DESCRIPTION (provided by applicant): Cancer arises from the acquisition and concerted action of multiple mutations and genomic aberrations in discrete combinations of tumor suppressors and oncogenes, known as "drivers". Large cancer genome-scale sequencing studies such as TCGA, are now operative and the Cancer Target Discovery and Development (CTDD) Network seeks "to bridge the gap between the enormous volumes of data generated... and the ability to use these data for the development of human cancer therapeutics". A secondary goal for the CTDD Initiative is that in five years, "the entire CTDD Network is expected to identify and characterize targets for approximately 25 or more (if possible) cancer types." and for applicants "to have or build the capacity for in depth analyses and experimental approaches utilizing datasets for many cancer types. A broad "coverage" is the paradigm for this initiative." Here, the wealth of TCGA data will be directly coupled to robust in vitro functinal validation of candidate cancer driver modules using primary mouse 3D organoid cultures of diverse tissues arrayed in high-throughput format. In Aim 1, the Hanlee Ji and Sylvia Plevritis groups will identify co-segregating mutational modules from TCGA datasets from multiple solid tumor types, using complementary methods of supervised Bayesian analysis and Unsupervised Module Network Analysis for Master Regulators. In Aim 2, these prioritized mutational modules, stratified for clinical significance, will undergo direct functional validation in a broadly applicble, multiplexable, in vitro 3D primary organoid system developed by the Calvin Kuo group, which is amenable to combinatorial gene engineering. In collaboration with Bill Hahn, this will utilize high throughput lentiviral introduction of cDNA or shRNA to systematically interrogate the genes within amplicons and deletions, contextually modeled in the TCGA mutational background in which these copy number variations occur. Additionally, co-segregating mutational modules from diverse tissues will undergo systematic deletion in organoid cultures to define minimal module composition, and we will pursue process development to extend the range of tissues from which organoids can be modeled. Overall, these studies describe bioinformatic and in vitro modeling approaches that are robustly portable across a variety of organ systems for functional interrogation of diverse TCGA datasets, as highly responsive to RFA-CA-12-006 and the CTDD, and with attendant implications for cancer biology, diagnosis and therapy.