Functional Analysis of Oncogenic Networks in Primary Organoids

描述（由申请人提供）：癌症源于肿瘤抑制因子和癌基因（称为“驱动因子”）离散组合中的多个突变和基因组畸变的获得和协同作用。大型癌症基因组规模测序研究，如TCGA，现在正在运作，癌症靶点发现和开发（CTDD）网络寻求“弥合产生的大量数据之间的差距.以及利用这些数据开发人类癌症治疗方法的能力”。CTDD倡议的第二个目标是，在五年内，“整个CTDD网络预计将确定和表征大约25种或更多（如果可能的话）癌症类型的目标。“以及申请人“拥有或建立利用许多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“覆盖面”是这一举措的范例。“在这里，丰富的TCGA数据将直接与候选癌症驱动模块的强大体外功能验证相结合，这些模块使用以高通量格式排列的不同组织的原代小鼠3D类器官培养物。在目标1中，Hanlee Ji和Sylvia Pleveling小组将使用监督贝叶斯分析和主监管机构无监督模块网络分析的互补方法，从多种实体瘤类型的TCGA数据集中识别共分离突变模块。在目标2中，这些优先的突变模块，分层的临床意义，将在一个广泛适用的，多路复用的，在体外3D主要类器官系统由卡尔文郭组，这是服从组合基因工程开发的直接功能验证。在与比尔哈恩合作，这将利用高 通过cDNA或shRNA的慢病毒导入系统地询问扩增子和缺失内的基因，在发生这些拷贝数变异的TCGA突变背景中上下文建模。此外，来自不同组织的共分离突变模块将在类器官培养物中进行系统性缺失，以定义最小模块组成，我们将进行工艺开发，以扩大类器官可以建模的组织范围。总体而言，这些研究描述了生物信息学和体外建模方法，这些方法在各种器官系统中具有强大的便携性，可用于对不同TCGA数据集进行功能性询问，对RFA-CA-12-006和CTDD具有高度响应性，并对癌症生物学、诊断和治疗产生影响。

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.