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

系统地发现癌症中新形态蛋白-蛋白质相互作用对致癌途径的干扰

基本信息

批准号：
9363451
负责人：
HAIAN FU
金额：
$ 79.13万
依托单位：
EMORY UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9363451
关键词：
Address Alleles Amino Acids Binding Bioinformatics Biological Assay Biology Bioluminescence Biosensor Cancer Biology Cancer Etiology Cancer Model Cells Chemicals Clinic Collection Communities Complement Coupled DNA Sequence Alteration Data Data Set Databases Differentiated Gene Drug resistance Energy Transfer Expression Library Gene Mutation Genes Genetic Engineering Genomics Genotype Hematologic Neoplasms Informatics Knowledge Libraries Malignant Neoplasms Mediating Missense Mutation Modeling Molecular Mutate Mutation Normal Cell Oncogenes Oncogenic Organoids Pathway interactions Patients Proteins Research Personnel Resolution Resources Signal Pathway Solid Source Suppressor Genes Technology Testing The Cancer Genome Atlas Therapeutic Translating Translational Research Tumor Suppressor Genes Tumor-Derived Universities Up-Regulation Validation ataxia telangiectasia mutated protein base cancer cell cancer genomics cancer therapy cancer type comparative expression vector functional genomics gain of function genetic variant genome sequencing genome-wide high throughput technology in vivo Model member mutant novel oncology personalized medicine potential biomarker precision oncology predictive marker protein protein interaction screening small molecule structural biology targeted treatment tool translational study tumor tumor growth tumor progression

项目摘要

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.

概括埃默里大学功能基因组学分子相互作用中心（MicFG）提出了解通过系统地研究突变等位基因介导的基因组突变在癌症病因学中的功能致癌蛋白-蛋白相互作用 (PPI)，用于靶标识别、验证和扰动发现癌症类型，作为 CTD2 网络的贡献成员。为了协同努力，我们有一个团队在肿瘤学、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。患者肿瘤衍生突变的丰富可用数据为研究提供了前所未有的机会开发个性化疗法的转化研究。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用于癌症靶点发现的突变氨基酸分辨率的变化以及如何快速转化这种变化精准肿瘤学的基因型导向癌症疗法的知识仍然是一个令人畏惧和紧迫的问题挑战。我们的提案旨在通过团队努力直接关注癌症来解决这一关键瓶颈用于治疗发现的突变产生的蛋白质-蛋白质相互作用（neoPPI）。为了支持这种方法，我们生成了一个综合数据库，代表了主要体细胞错义突变的情况 TCGA泛癌数据集，并建立了独特的基于生物发光共振能量转移定量高通量野生型/突变体差异筛选（qHT-dS）平台。我们假设通过利用癌症错义突变景观和实施组合的高通量信息学和差异 PPI 筛选平台，以发现和验证治疗发现的癌症靶点。为了检验这一假设，提出了三个具体目标： (i) 通过 qHT-dS 的差异筛选来识别和验证癌症突变产生的 neoPPI 平台，(ii) 识别 neoPPI 干扰物作为途径扰动物，以及 (iii) 开发和系统应用用于 neoPPI 发现的集成信息学管道。我们的研究将导致（i）癌症突变的产生表达载体库、大规模 PPI 数据集、HTS neoPPI 检测作为社区资源，以及发现 (ii) 肿瘤特异性 neoPPI 作为有希望的癌症特异性靶点，(iii) 选定的 neo-PPI 干扰因素致癌途径破坏，以及（iv）neoPPI 告知潜在的生物标志物。功能互补其他人在体内模型中对突变等位基因的注释，我们对癌症基因变异的系统鉴定- 介导的新 PPI 可能会为基因型导向的治疗发现揭示有希望的癌症特异性靶点。