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

• 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
• 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

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（MICFG）的分子相互作用中心建议了解 通过系统询问突变等位基因介导的癌症病因学中基因组突变的功能 致癌蛋白 - 蛋白质相互作用（PPI），用于靶标识别，验证和扰动性的跨杜 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 研究人员和合作者具有肿瘤学，高吞吐量癌症生物学的补充专业知识和 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 型号。患者肿瘤衍生突变的大量可用数据为前所未有的机会提供了机会 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才 将肿瘤与正常肿瘤区分开。但是，了解如何利用这些基因组 在突变的氨基酸分辨率下进行癌症目标发现的变化以及如何快速翻译这一点 对基因型指导的癌症疗法的知识仍然令人生畏且紧急 挑战。我们的建议旨在通过直接专注于癌症来通过团队的努力来解决这一关键瓶颈 突变创建的蛋白质 - 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 TCGA Pan-Cancer数据集，并建立了基于独特的生物发光能量转移 定量高吞吐量野生型/突变体差分筛选（QHT-DS）平台。我们假设这一点 通过利用癌症错过突变景观和 实施一个合并的高吞吐量信息学和差分PPI筛选平台，以进行发现和 验证治疗发现的癌症靶标。为了检验这一假设，提出了三个具体目标： （i）通过与QHT-DS进行差异筛选来识别和验证癌症突变创建的新疾病 平台，（ii）将NEOPPI破坏者识别为途径，以及（iii）开发和系统地应用 NEOPPI发现的集成信息管道。我们的研究将导致（i）创建癌症突变 表达向量库，大规模PPI数据集，HTS NEOPPI分析作为社区资源，并且 发现（ii）肿瘤特异性的NEOPIS作为有前途的癌症特异性靶标，（iii）选择的NEO-PPI症状 对于致癌途径的破坏，（iv）Neoppi了解了潜在的生物标志物。补充功能 其他人对体内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 介导的NEO-PIPI可能揭示了针对基因型定向治疗发现的有希望的癌症特异性靶标。