Core 1: Functional Genomics and Proteomics

CCMI v2.0 核心1：功能基因组学和蛋白质组学核心 核心负责人：内万·克罗根；联合调查员：普拉尚特·马里 摘要 肿瘤的蛋白质-蛋白质相互作用(PPI)和遗传相互作用网络的系统表征 驱动因素对于识别患者分层和靶向癌症的功能复合体至关重要 治疗。确定PPI和蛋白质复合体化学计量和拓扑的蛋白质组学方法可以 提供对动态癌症前景的战略性洞察，并帮助指导定制的基因筛查以评估 新确定的PPI的功能相关性。功能基因组学和蛋白质组学核心(核心1) 将提供完成CCMI总体目标所必需的基础设施和技术专长。 在两个核心设施的支持下，功能基因组学和蛋白质组学核心将提供其专业知识 在CRISPR遗传屏幕(基因组医学研究所基因组学中心，UCSD)和蛋白质组中 技术(Thermo Fisher科学蛋白质组学疾病靶点发现设施，加州大学旧金山分校，定量 生物科学研究所(QBI)，Gladstone研究所)，用于产生高质量的蛋白质-蛋白质(目标2；in 支持项目1)和遗传(目标1；支持项目2)互动数据，将使用 项目3和核心2中的系统生物学方法。我们的目标是提供功能和结构 癌症驱动网络的特征，在三种不同的癌症(乳腺癌， 头颈部、肺癌)，并确定引入点突变的后果。最后，我们将使用 用于确定内生网络/动态结构(EN/DS)的独特平台(目标3)，将使用 基于CRISPR的遗传学将点突变和标志亲和力标签引入选定的内源基因座 内源蛋白质复合体化学计量学和结构基因的研究 (支持项目1)。

CCMI v2.0 Core 1: Functional Genomics and Proteomics Core Core Lead: Nevan Krogan; Co-Investigator: Prashant Mali SUMMARY Systematic characterization of protein-protein interactions (PPIs) and genetic interaction networks of cancer drivers is critical for the identification of functional complexes for patient stratification and targeted cancer therapies. Proteomic approaches that determine PPIs and protein complex stoichiometry and topology can provide strategic insight into the dynamic cancer landscape and help guide tailored genetic screens to assess functional relevance of newly characterized PPIs. The Functional Genomics and Proteomics Core (Core 1) will provide the infrastructure and technical expertise essential to the completion of the overall CCMI objectives. With support of two Core facilities, the Functional Genomics and Proteomics Core will provide its expertise in CRISPR genetic screens (Institute for Genomic Medicine (IGM) Genomics Center, UCSD) and proteomic technologies (Thermo Fisher Scientific Proteomics Facility for Disease Target Discovery, UCSF, Quantitative Biosciences Institute (QBI), Gladstone Institute) for the generation of high-quality protein-protein (Aim 2; in support of Project 1) and genetic (Aim 1; in support of Project 2) interaction data, which will be integrated using systems biology approaches in Project 3 and Core 2. Our goal is to provide functional and structural characterization of cancer driver networks, model their functional interactions in three different cancers (breast, head and neck, lung cancer), and determine consequences of introducing point mutations. Finally, we will use a unique platform for Endogenous Network/Dynamic Structure (EN/DS) determination (Aim 3) that will use CRISPR-based genetics to introduce point mutations and FLAG-affinity tags into the endogenous loci of selected genes to study endogenous protein complex stoichiometry and structure using mass spectrometry and cryo-EM (in support of Project 1).