Project 1: Systematic Physical and Spatial Mapping of Cancer Driver Networks

CCMI v2.0 项目1：癌症司机网络的系统物理和空间绘图 项目负责人：内万·克罗根和艾玛·伦德伯格；联合调查员：艾伦·阿什沃斯，让-菲利普·科普 詹妮弗·格兰迪斯、西尔维奥·古特金德、娜塔莉亚·朱拉和劳拉·范特维尔 摘要 肿瘤表现出复杂的突变特征，在遗传学研究中表现为一种随机的突变模式。 然而，正是它们在癌症途径上的非随机结合和汇聚导致了转化。 特定的通路，如PI3K或P53轴，在大多数癌症中反复突变，但除了 这样的泛癌突变途径，每个肿瘤都含有20到1000多个罕见的额外突变 在患者群体中随处可见。肿瘤的异质性、起源组织和进展程度各有不同 Case是一组独特的改变的通路，并阻碍了靶向癌症疗法的发展。 将基因突变映射到先前已识别的细胞通路上可以为临床提供见解 人物刻画。为了有效地利用治疗策略的路径网络，在项目1中，我们将确定 并描述癌症的驱动途径。 为此，我们将把物理和空间蛋白质相互作用与大规模基因组数据相结合，并应用 一套通过低温电子显微镜(Cryo-EM)进行体外成像的蛋白质组技术 以正交(癌症特异性)或横向(跨癌症)的方式系统地绘制蛋白质网络图。 具体地说，我们将系统地确定PI3K通路和P53的关键调控网络 乳腺癌(BRCA)、头颈部癌(HNSCC)和肺鳞癌(LUSC)，并补充我们以前的 通过识别LUSC中的驱动程序网络来处理HNSCC和BRCA。以蛋白质组学方法为指导 通过复杂的成像和关键络合物的高分辨率结构分析并进行功能验证， 项目1将深入了解这些癌症的潜在分子生物学，并揭开基因 与治疗相关的脆弱性。在目标1中，我们将绘制30个蛋白质-蛋白质相互作用(PPI)图 跨越HNSCC的PI3K途径的蛋白质(以及其中6个蛋白质的12个突变体)和P53的10个突变体， BRCA和LUSC。我们还将定义30个最频繁改变的蛋白质(和 9种蛋白质中的20个相关突变体)，补充了我们之前在HNSCC和BRCA方面的工作。 使用抗体的人类蛋白质图谱资源，目标2将重点放在空间的宏观地图上 目标1中定义的关键致癌驱动因素及其相互作用的亚细胞组织。目标3将利用最近的 低温电子显微镜在表征包括PI3K途径在内的关键复合体结构方面的进展。最后， 来自先前目标的预测将在目标4中在细胞系、原代细胞和小鼠模型中进行测试，并与 临床数据。

CCMI v2.0 Project 1: Systematic physical and spatial mapping of driver networks in cancer Project Leads: Nevan Krogan and Emma Lundberg; Co-Investigators: Alan Ashworth, Jean-Philippe Coppe, Jennifer Grandis, Silvio Gutkind, Natalia Jura and Laura van ’t Veer SUMMARY Tumors display complex mutational profiles that appear as a random pattern of mutations in genetic studies. However, it is their non-random combination and convergence on cancer pathways that lead to transformation. Specific pathways such as the PI3K or p53 axis are recurrently mutated in a majority of cancers but besides such pan-cancer mutated pathways, each tumor harbors 20 to over 1,000 additional mutations that are rarely seen across the patient population. Tumor heterogeneity, tissue of origin, and degree of progression give each case a unique subset of altered pathways and has hampered the development of targeted cancer therapies. Mapping genetic mutations onto previously identified cellular pathways can provide insights for clinical characterization. To efficiently leverage pathway networks for therapeutic strategies, in Project 1 we will identify and characterize cancer driver pathways. To this end, we will combine physical and spatial protein interactions with large scale genomic data and apply a suite of proteomic technologies with in vitro imaging through cryo-electron microscopy (cryo-EM) to systematically map protein networks in an orthogonal (cancer specific) or transversal (across cancers) manner. Specifically, we will systematically identify the network of key regulators of the PI3K pathway and p53 across breast (BRCA), head and neck (HNSCC) and lung squamous cancers (LUSC), and complement our previous work on HNSCC and BRCA by identifying driver networks in LUSC. Guided by proteomic approaches coupled with sophisticated imaging and high-resolution structural analysis of key complexes with functional validation, Project 1 will gain insights into the underlying molecular biology of these cancers and unravel genetic vulnerabilities of therapeutic relevance. In Aim 1, we will map the protein-protein interactions (PPIs) of 30 proteins (and 12 mutants in 6 of those proteins) of the PI3K pathway and 10 mutants of p53 across HNSCC, BRCA and LUSC. We will also define the physical interactions of the 30 most recurrently altered proteins (and 20 associated mutants in 9 of the proteins) in LUSC, complementing our previous work on HNSCC and BRCA. Using the Human Protein Atlas resource of antibodies, Aim 2 will focus on macroscopic mapping of the spatial subcellular organization of key oncogenic drivers and their interactors defined in Aim 1. Aim 3 will exploit recent advances in cryo-EM to structurally characterize key complexes, including those in the PI3K pathway. Finally, predictions from the previous aims will be tested in Aim 4 in cell lines, primary cells and mouse models and with clinical data.