Personalized cancer-specific networks

该项目的主要目标是通过系统医学方法使癌症预后个性化。基于蛋白质结构，特别是蛋白质复合体的拓扑和结构决定细胞和组织的功能这一公认的原则，我们将解决以组织特有的方式将遗传数据与依赖于蛋白质复合体功能的分子和临床表型联系起来的及时和迫切的需求。该项目旨在识别特定于组织和特定于肿瘤的计算模型，在这些模型中，患者特定的基因变异以影响健康的方式影响蛋白质相互作用。这样的模型最初将由生物信息学预测得出，然后基于蛋白质组学和遗传学实验产生的验证数据进行迭代改进。具体地说，该项目开发的预测技术将经过严格的实验验证，方法是生成相应的蛋白质复合体，并使用交联实验来确定它们是否以及如何在拓扑和结构上与野生型不同。这些模型将使用一组注释良好的三阴性乳腺癌(TNBC)患者的样本进行验证。该项目建议生成几个重要的数据资源、分析方法、软件工具和服务：1)预测影响球状和跨膜蛋白之间的蛋白质-蛋白质相互作用(PPI)的疾病突变的方法，导致功能丧失或获得；2)受序列变体影响的组织和肿瘤特异性相互作用网络的精选数据集；3)考虑PPI的异构体以及动态和浓度依赖的相互作用网络的方法；4)检测由基因组损伤引起的蛋白质复合体的结构和组成变化的方法和软件工具；5)与实体癌症中的肿瘤-免疫细胞相互作用相关的经过精选的蛋白质复合体和癌症特异性网络；6)通过化学交联和质谱学对一组蛋白质复合体中的基因组变异引起的预测的结构或组成变化进行实验验证；7)用于验证的来自TNBC队列的免疫基因组分析的主要数据和结果。

The key objective of the project is to enable personalized cancer prognosis through a systems medicine approach. Based on the well-established principle that protein structure and, in particular, topology and structure of protein complexes determine the functional capabilities of cells and tissues, we will address a timely and urgent need to link genetic data to molecular and clinical phenotypes that depend on the function of protein complexes in a tissue-specific manner. The project aims at the identification of tissue-specific and tumor-specific computational models, in which patient-specific genetic variation influences protein interactions in ways that affect health. Such models will be initially derived by bioinformatics predictions, and then iteratively refined based on validation data generated by proteomics and genetics experiments. Specifically, predictive techniques developed in the project will be subjected to rigorous experimental verification by generating the corresponding protein complexes and using cross-linking experiments to determine whether and how they differ in topology and structure from the wild type. The models will be validated using samples from a well-annotated cohort of triple-negative breast cancer (TNBC) patients. The project proposes to generate several important data resources, analysis methods, software tools and services: 1) Methods for predicting disease mutations affecting protein-protein interactions (PPIs) between both globular and transmembrane proteins, leading to loss or gain of function; 2) Curated datasets of tissue- and tumor-specific interaction networks impacted by sequence variants; 3) Methods for creating interaction networks that take into account isoforms as well as dynamic and concentration-dependent aspects of PPIs; 4) Methods and software tools to detect structural and compositional alterations in protein complexes induced by genomic lesions; 5) Curated protein complexes and cancer specific networks relevant for tumor-immune cell interactions in solid cancers; 6) Experimental validation by chemical cross-linking and mass spectrometry of predicted structural or compositional alterations induced by genomic variation in a set of protein complexes; 7) Primary data and results from the immunogenomic analyses from the TNBC cohort used for validation.