Systematic discovery of neomorph protein-protein interactions in cancer for oncogenic pathway perturbation

摘要 埃默里大学功能基因组学分子相互作用中心(MicFG)建议理解 从突变等位基因系统检测看基因组突变在肿瘤病因学中的作用 致癌蛋白质-蛋白质相互作用(PPI)用于靶标识别、验证和干扰原发现 癌症类型，作为CTD2网络的贡献成员。为了协同工作，我们有一个团队， 研究人员和合作者在肿瘤学、高通量癌症生物学和 化学生物学、癌症基因组学、生物信息学、计算结构生物学和癌症验证 模特们。患者肿瘤衍生突变的丰富可用数据提供了前所未有的机会 开展个性化治疗的转化性研究。正是这些每个驱动基因的基因组变化导致了 将肿瘤与正常的肿瘤区分开来。然而，了解如何利用这些基因组 发现癌症靶点的突变氨基酸分辨率的变化以及如何快速翻译 对精确肿瘤学的基因导向癌症治疗的了解仍然是一个令人望而生畏的紧迫问题 挑战。我们的提案旨在通过团队努力，通过直接关注癌症来解决这一关键瓶颈 突变产生的蛋白质-蛋白质相互作用(新PPI)用于治疗发现。为了支持这一方法，我们 已经生成了一个全面的数据库，代表了主要的体细胞错义突变的情况 TCGA泛癌数据集，并建立了基于生物发光共振能量转移的独特方法 定量高通量野生型/突变差异筛选(qHT-DS)平台。我们假设 通过利用癌症错义突变和 实施组合的高通量信息学和差异PPI筛选平台，用于发现和 用于治疗发现的癌症靶点的确认。为了验证这一假设，我们提出了三个具体目标： (I)通过与qHT-DS的差异筛查，识别和验证由癌症突变造成的新的PPI 平台，(Ii)确定新的PPI干扰物为途径干扰物，以及(Iii)开发和系统应用 新PPI发现的综合信息学管道。我们的研究将导致(I)产生癌症突变 表达载体文库、大规模PPI数据集、HTS新PPI分析作为社区资源，以及 发现(Ii)肿瘤特异性的新PPI作为有希望的癌症特异性靶点，(Iii)选定的neo-PPI干扰物 对于致癌途径的破坏，以及(Iv)新PPI通知潜在的生物标记物。补充功能 活体模型中突变等位基因的注释，我们对癌症基因变异的系统鉴定-- 介导性neo-PPI可能为基因导向的治疗发现揭示有希望的癌症特异性靶点。

SUMMARY The Molecular Interaction Center for Functional Genomics (MicFG) of Emory University proposes to understand the functions of genomic mutations in cancer etiology through systematic interrogation of mutant allele-mediated oncogenic protein-protein interactions (PPI) for target identification, validation, and perturbagen discovery across cancer types, as a contributing member of the CTD2 Network. For synergistic effort, we have a team of investigators and collaborators with complementary expertise in oncology, high throughput cancer biology and chemical biology, cancer genomics, bioinformatics, computational structural biology and cancer validation models. The wealth of available data for patient tumor-derived mutations offers unprecedented opportunities for translational research to develop personalized therapies. It is these genomic alterations in each driver gene that differentiate tumors from their normal counterparts. However, understanding how to leverage these genomic changes at the mutated amino acid resolution for cancer target discovery and how to rapidly translate this knowledge into genotype-directed cancer therapies for precision oncology remains a daunting and urgent challenge. Our proposal aims to address this critical bottleneck with a team effort by directly focusing on cancer mutation-created protein-protein interactions (neoPPI) for therapeutic discovery. To support this approach, we have generated a comprehensive database representing the landscape of major somatic missense mutations in TCGA pan-cancer datasets, and established a unique bioluminescence resonance energy transfer-based quantitative high throughput wildtype/mutant differential screening (qHT-dS) platform. We hypothesize that oncogenic neoPPIs can be rapidly uncovered by leveraging the cancer missense mutational landscape and implementing a combined high throughput informatics and differential PPI screening platform for discovery and validation of cancer targets for therapeutic discovery. To test this hypothesis, three specific aims are proposed: (i) to identify and validate cancer mutation-created neoPPIs through differential screening with the qHT-dS platform, (ii) to identify neoPPI disruptors as pathway perturbagens, and (iii) to develop and systematically apply integrated informatics pipelines for neoPPI discovery. Our studies will lead to (i) creation of cancer mutation expression vector libraries, large-scale PPI datasets, HTS neoPPI assays as a community resource, and discovery of (ii) tumor-specific neoPPIs as promising cancer-specific targets, (iii) selected neo-PPI perturbagens for oncogenic pathway disruption, and (iv) neoPPI informed potential biomarkers. Complementing the functional annotation of mutant alleles in in vivo models by others, our systematic identification of cancer gene variant- mediated neo-PPIs may reveal promising cancer-specific targets for genotype-directed therapeutic discovery.