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

总结 埃默里大学功能基因组学分子相互作用中心（MicFG）提出要了解 基因组突变在癌症病因学中的功能，通过系统地询问突变等位基因介导的 致癌蛋白质-蛋白质相互作用（PPI），用于靶点识别、验证和干扰原发现， 癌症类型，作为CTD 2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 模型患者肿瘤源性突变的丰富可用数据为以下方面提供了前所未有的机会： 转化研究以开发个性化疗法。正是每个驱动基因中的这些基因组改变， 将肿瘤与其正常对应物区分开来。然而，了解如何利用这些基因组 癌症靶点发现的突变氨基酸分辨率的变化以及如何快速翻译 了解基因型定向癌症治疗的精确肿瘤学仍然是一个艰巨而紧迫的任务， 挑战.我们的提案旨在通过直接关注癌症， 突变产生的蛋白质-蛋白质相互作用（neoPPI）用于治疗发现。为了支持这种方法，我们 已经产生了一个全面的数据库，代表了主要的体细胞错义突变的景观， TCGA泛癌数据集，并建立了独特的生物发光共振能量转移为基础的 定量高通量野生型/突变体差异筛选（qHT-dS）平台。我们假设 致癌neoPPI可以通过利用癌症错义突变景观快速发现， 实施组合的高通量信息学和差异PPI筛选平台，用于发现和 验证癌症治疗发现的靶点。为了检验这一假设，提出了三个具体目标： (i)通过使用qHT-dS进行差异筛选，识别和验证癌症突变产生的neoPPI 平台，（ii）确定neoPPI干扰物作为途径干扰物，以及（iii）开发和系统地应用 用于neoPPI发现的集成信息学管道。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、作为社区资源的HTS neoPPI测定，以及 发现（ii）肿瘤特异性neo-PPI作为有希望的癌症特异性靶点，（iii）选定的neo-PPI干扰物 致癌途径破坏，和（iv）neoPPI告知潜在的生物标志物。补充功能 其他人在体内模型中对突变等位基因的注释，我们系统地鉴定了癌症基因变异- 介导的neo-PPI可能揭示基因型导向治疗发现的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.