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

CCMI v2.0 项目1：癌症驱动网络的系统性物理和空间映射 项目负责人：Nevan Krogan和Emma Lundberg;共同研究者：Alan Ashworth，Jean-Philippe Coppe， 詹妮弗·格兰迪斯，西尔维奥·古特金德，娜塔莉亚朱拉和劳拉·货车 总结 肿瘤显示复杂的突变谱，在遗传研究中表现为随机突变模式。 然而，正是它们在癌症途径上的非随机组合和趋同导致了转变。 特定途径如PI 3 K或p53轴在大多数癌症中反复突变，但除此之外， 在这种泛癌突变途径中，每个肿瘤含有20到1,000多个额外的突变， 在患者人群中看到。肿瘤的异质性、起源组织和进展程度， 这是一个独特的改变途径的子集，阻碍了靶向癌症治疗的发展。 将基因突变映射到先前确定的细胞通路上可以为临床研究提供见解。 特征化为了有效地利用通路网络的治疗策略，在项目1中，我们将确定 并表征癌症驱动途径。 为此，我们将联合收割机物理和空间蛋白质相互作用与大规模基因组数据相结合， 通过冷冻电子显微镜（cryo-EM）进行体外成像的一套蛋白质组学技术， 以正交（癌症特异性）或横向（跨癌症）方式系统地绘制蛋白质网络。 具体来说，我们将系统地确定PI 3 K通路和p53的关键调节因子网络， 乳腺癌（BRCA），头颈部癌（HNSCC）和肺鳞状细胞癌（LUSC），并补充我们以前的研究 通过识别LUSC中的驱动程序网络来研究HNSCC和BRCA。以蛋白质组学方法为指导， 通过对关键复合物的复杂成像和高分辨率结构分析以及功能验证， 项目1将深入了解这些癌症的潜在分子生物学， 与治疗相关的弱点在目标1中，我们将绘制30种蛋白质-蛋白质相互作用（PPI） PI 3 K途径的蛋白质（以及这些蛋白质中的6种中的12种突变体）和跨HNSCC的p53的10种突变体， BRCA和LUSC。我们还将定义30种最经常改变的蛋白质的物理相互作用（以及 9种蛋白质中的20种相关突变体），补充了我们先前在HNSCC和BRCA上的工作。 利用抗体的人类蛋白质图谱资源，目标2将侧重于空间的宏观映射， Aim 1中定义的关键致癌驱动因子及其相互作用因子的亚细胞组织。目标3将利用最近的 冷冻EM的进展，以结构表征关键复合物，包括PI 3 K通路中的复合物。最后， 目标4将在细胞系、原代细胞和小鼠模型中测试先前目标的预测， 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.