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

项目 1：癌症驱动网络的系统物理和空间测绘

基本信息

批准号：
10525588
负责人：
Emma Lundberg
金额：
$ 56.19万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-14 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10525588
关键词：
Affinity Antibodies Atlases Biological Markers Biology Breast CRISPR interference Cancer Biology Catalogs Cell Line Cell model Cells Chromosome Mapping Clinical Clinical Cancer Center Clinical Data Clinical Trials Clustered Regularly Interspaced Short Palindromic Repeats Complement Complex Coupled Coupling Cryoelectron Microscopy Custom DNA Sequence Alteration Data Development Drug Combinations Drug usage ERBB3 gene Exhibits Genes Genetic Predisposition to Disease Genetic study Head and Neck Squamous Cell Carcinoma Head and neck structure Human Image In Vitro Lead Lung Malignant Neoplasms Maps Mass Spectrum Analysis Modeling Molecular Molecular Biology Mutate Mutation Oncogenic Pathway interactions Pattern Pharmaceutical Preparations Precision therapeutics Prediction of Response to Therapy Protein-Protein Interaction Map Proteins Proteomics Recurrence Regulation Research Research Personnel Resolution Resources Sampling Small Interfering RNA Spatial Distribution Structure TP53 gene Technology Testing Therapeutic Tissues Validation Work base cancer cell cancer imaging cancer therapy cancer type candidate marker crosslink experimental study functional genomics genomic data high throughput screening in vivo in vivo imaging innovation insight metaplastic cell transformation mouse model mutant new technology novel patient derived xenograft model patient population protein complex protein protein interaction structural biology targeted cancer therapy therapeutic target treatment response tumor tumor heterogeneity tumor progression tumorigenesis

项目摘要

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.

CCMI v2.0 项目 1：癌症驱动网络的系统物理和空间绘图项目负责人：Nevan Krogan 和 Emma Lundberg；联合研究员：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·范特·维尔概括肿瘤表现出复杂的突变谱，在遗传学研究中表现为随机突变模式。然而，正是它们的非随机组合和癌症途径的趋同导致了转变。 PI3K 或 p53 轴等特定通路在大多数癌症中反复发生突变，但除此之外这种泛癌突变途径，每个肿瘤都含有 20 到 1,000 多个额外突变，而这些突变很少见在患者群体中观察到的肿瘤异质性、组织起源和进展程度给出了每一个。病例是改变途径的独特子集，并阻碍了靶向癌症疗法的发展。将基因突变映射到先前确定的细胞通路上可以为临床提供见解为了有效利用治疗策略的通路网络，在项目 1 中，我们将确定并描述癌症途径驱动因素的特征。为此，我们将物理和空间蛋白质相互作用与大规模基因组数据相结合，并应用一套通过冷冻电子显微镜 (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）方式一致地绘制蛋白质网络。具体来说，我们将系统地识别 PI3K 通路和 p53 的关键调控因子网络乳腺癌 (BRCA)、头颈癌 (HNSCC) 和肺鳞癌 (LUSC)，并补充了我们之前的研究在蛋白质组学方法的指导下，通过识别 LUSC 中的驱动网络来研究 HNSCC 和 BRCA。通过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并揭示遗传机制在目标 1 中，我们将绘制 30 个蛋白之间的相互作用 (PPI)。 PI3K 通路的蛋白质（以及其中 6 个蛋白质中的 12 个突变体）和 HNSCC 中 p53 的 10 个突变体，我们还将定义 30 种最经常改变的蛋白质（以及 LUSC 中 9 个蛋白质中的 20 个相关突变体，补充了我们之前在 HNSCC 和 BRCA 方面的工作。利用人类蛋白质图谱抗体资源，目标 2 将重点关注空间的宏观映射目标 1 中定义的关键致癌驱动因素及其相互作用因子的亚细胞组织。目标 3 将利用最新的冷冻电镜在结构表征关键复合物方面取得的进展，包括 PI3K 通路中的复合物。先前目标的预测将在 Aim 4 中的细胞系、原代细胞和小鼠模型中进行测试，并使用临床数据。