Systems Biology based Proteogenomic Translator for Cancer Marker Discovery towards Precision Medicine

基于系统生物学的蛋白质基因组翻译器，用于癌症标记物发现，迈向精准医学

• 批准号: 9759648
• 负责人: ERIC E SCHADT
• 金额: $ 88.04万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-19 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9759648
• 关键词: Affect; Antineoplastic Agents; Biological; Biological Assay; Cancer Biology; Clinical; Collection; Complex; Computer software; DNA Sequence Alteration; Data; Data Analyses; Data Set; Databases; Disease; Disease Outcome; Drug Targeting; Drug resistance; Ensure; Gene Proteins; Genes; Genomics; Goals; Imagery; Individual; Internet; Investigation; Knowledge; Learning; Literature; Malignant Neoplasms; Marker Discovery; Methods; Methylation; Mining; Modeling; Molecular; Network-based; Normal tissue morphology; Oncogenes; Pathway Analysis; Pathway interactions; Pattern; Phenotype; Play; Post-Translational Protein Processing; Property; Proteins; Proteomics; Research; Resources; Role; Series; Set protein; Shapes; Signal Transduction Pathway; Statistical Data Interpretation; Statistical Methods; Structure; System; Systems Biology; Techniques; Testing; Therapeutic; Time; Tumor Subtype; Tumor Tissue; Visualization software; Work; actionable mutation; analysis pipeline; assay development; base; bioinformatics tool; biomarker selection; cancer biomarkers; cancer type; candidate marker; candidate selection; clinically relevant; computerized tools; data modeling; disease phenotype; disorder subtype; experience; experimental study; genetic regulatory protein; genomic data; high dimensionality; human disease; improved; malignant breast neoplasm; multidimensional data; mutational status; neoplastic cell; novel; precision medicine; predictive marker; programs; protein biomarkers; proteogenomics; success; tool; transcriptome; transcriptomics; tumor; web services

PROJECT SUMMARY/ABSTRACT The goal of our PGDAC is to improve understanding of the proteogenomic complexity of tumors. Towards this goal, our First Aim is to apply network based system learning to reveal causative molecular regulatory relationships contributing to varieties of phenotypes in cancer using CPTAC proteomic/genomic data. We will start with a mixed effects model to (1) fix the batch effects in data from multi-plex proteomics experiments; and (2) handle the large amount of missing data from abundance-dependent missing mechanisms in proteomic data (Aim 1.1). We will then utilize a multivariate penalized regression framework to construct the global regulatory networks between genomic alterations (such as DNA mutations, CNA, methylations), and protein as well as their PTM (post translational modification) abundances (Aim 1.2). Such regulatory networks help to elucidate how protein or pathway activities are shaped by genomic alterations in tumor cells. We will also construct protein co-expression networks based on global-, phosphor-, glyco- and other PTM-proteomics data (Aim 1.3). When constructing these networks, we will use advanced computational tools to effectively borrow information from literatures, databases, and transcriptome profiles. In addition, we will model tumor and normal tissues jointly, so that tumor specific interactions and network modules will be inferred with better accuracy. Both Aims 1.2 and 1.3 will lead to a big collection of network modules, as well as functionally related protein sets (e.g. proteins regulated by the same genomic alteration). These network modules and protein sets will then be tested for their associations with disease phenotypes (Aim 1.4). In the end, we will derive a more integrated view of commonalities and differences across multiple tumor types via a Pan-cancer analysis (Aim 1.5). Our Second Aim is to further develop methods, software, and web-tools to optimize the data analysis in our PGDAC. We will develop novel statistical/computational tools tailored to CPTAC proteomics data; implement these methods as computationally efficient software; and construct an integrated data analysis pipeline (Aim 2.1). We also plan to develop a set of web service tools for visualization and biological annotation of protein networks and clinical interpretation of proteomic data (Aim 2.2). Our Third Aim is to nominate novel protein-based cancer biomarkers and drug targets for further investigation by targeted proteomics assays. We will first utilize a prediction based scoring system to identify protein biomarkers that predict altered cancer pathways, network modules and individual oncogenes; disease outcome and drug resistance; and therapeutically distinct disease subtypes (Aim 3.1) We will then utilize network based tools to identify driver players in selected proteins signature sets (Aim 3.2). These driver proteins could play important roles in shaping the overall function of regulatory system, and thus serve as good candidates for cancer biomarkers and drug targets. We will also take into consideration of domain knowledge of different diseases, as well as technique constrains for developing targeted proteomics assays in biomarker selection. !

项目摘要/摘要 我们的PGDAC的目标是提高对肿瘤蛋白质基因组学复杂性的理解。朝向这个方向 目标，我们的第一个目标是应用基于网络的系统学习来揭示致病分子调控。 利用CPTAC蛋白质组/基因组数据研究癌症不同表型的关系。我们会 从混合效应模型开始，(1)固定来自多组蛋白质组实验的数据中的批次效应；以及 (2)处理蛋白质组学中丰度依赖的缺失机制产生的大量缺失数据 数据(目标1.1)。然后，我们将利用多变量惩罚回归框架来构建全局 基因组改变(如DNA突变、CNA、甲基化)和蛋白质AS之间的调控网络 及其翻译后修饰(PTM)丰度(目标1.2)。这样的监管网络有助于 阐明肿瘤细胞中的基因组变化是如何塑造蛋白质或途径活性的。我们还将 基于全球、磷、糖和其他PTM蛋白质组学数据构建蛋白质共表达网络 (目标1.3)。在建设这些网络时，我们将使用先进的计算工具来有效地借用 来自文献、数据库和转录档案的信息。此外，我们还将模拟肿瘤和正常组织 组织联合，以便肿瘤特异性相互作用和网络模块将以更高的准确性推断。 AIMS 1.2和1.3都将产生大量的网络模块以及功能相关的蛋白质 SET(例如，受相同基因组改变调控的蛋白质)。这些网络模块和蛋白质集将 然后测试它们与疾病表型的相关性(目标1.4)。最终，我们将衍生出一个更多的 通过泛癌分析(AIM)综合查看多种肿瘤类型的共性和差异 1.5)。我们的第二个目标是进一步开发方法、软件和网络工具来优化数据分析 我们的PGDAC。我们将为CPTAC蛋白质组学数据开发新的统计/计算工具； 将这些方法作为计算效率高的软件实施；并构建集成的数据分析 管道(目标2.1)。我们还计划开发一套用于可视化和生物注释的Web服务工具 蛋白质网络和蛋白质组数据的临床解释(目标2.2)。我们的第三个目标是提名小说 以蛋白质为基础的癌症生物标记物和药物靶标，用于靶向蛋白质组学分析的进一步研究。我们 将首先利用基于预测的评分系统来识别预测癌症改变的蛋白质生物标记物 途径、网络模块和个体癌基因；疾病转归和耐药性；以及 治疗上不同的疾病亚型(目标3.1)然后我们将利用基于网络的工具来识别司机 选定蛋白质签名集中的玩家(目标3.2)。这些驱动蛋白可能在 塑造调控系统的整体功能，从而成为癌症生物标记物的良好候选者 和毒品目标。我们还将考虑不同疾病的领域知识，以及 生物标记物选择中发展靶向蛋白质组学分析的技术限制。 好了！