Core 1: Functional Genomics and Proteomics

CCMI v2.0 核心1：功能基因组学和蛋白质组学 核心负责人：Nevan Krogan;合作研究者：Prashant Mali 总结 蛋白质-蛋白质相互作用（PPI）的系统表征和癌症的遗传相互作用网络 驱动因子对于识别患者分层和靶向癌症的功能复合物至关重要 治疗确定PPI和蛋白质复合物化学计量和拓扑结构的蛋白质组学方法可以 为动态癌症格局提供战略性见解，并帮助指导量身定制的遗传筛查， 新表征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， CRISPR基因筛选（基因组医学研究所（IGM）基因组学中心，UCSD）和蛋白质组学 技术（Thermo Fisher Scientific Proteomics Facility for Disease Target Discovery，UCSF，Quantitative 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（Aim 2;在 支持项目1）和遗传（目标1;支持项目2）相互作用数据，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 表征癌症驱动网络，在三种不同的癌症（乳腺癌， 头颈癌、肺癌），并确定引入点突变后果。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FLAG亲和标签引入所选的内源基因座， 基因，使用质谱和cryo-EM研究内源性蛋白质复合物的化学计量和结构 (in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).