Proteogenomic translator for cancer biomarker discovery towards precision medicine

用于癌症生物标志物发现和精准医学的蛋白质基因组翻译

• 批准号: 10655588
• 负责人: Avi Ma'ayan
• 金额: $ 82.41万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10655588
• 关键词: Address; Algorithms; Antineoplastic Agents; Biological; Biological Assay; Biomedical Research; Cancer Biology; Collaborations; Collection; Communities; Complex; Computer software; Computing Methodologies; DNA; DNA Sequence Alteration; Data; Data Analyses; Data Set; Databases; Disease; Disease Outcome; Drug Targeting; Drug resistance; Ensure; Fostering; Genes; Genomics; Germ-Line Mutation; Goals; Immune; Immune Evasion; Immunologist; Immunophenotyping; Immunotherapy; Individual; Investigation; Knowledge; Label; Learning; Literature; Malignant Neoplasms; Mass Spectrum Analysis; Methods; Methylation; Mining; Modeling; Molecular; Multiomic Data; Mutation; Network-based; Oncologist; Online Systems; Pathway interactions; Pattern; Pharmaceutical Preparations; Phenotype; Phosphotransferases; Physicians; Process; Proteins; Proteomics; Quality Control; RNA; Research; Resources; Sampling; Scientist; Set protein; Shapes; Signal Transduction Pathway; Statistical Data Interpretation; System; Systems Biology; Testing; Therapeutic; Tissues; Transcript; Translating; Visualization; Work; assay development; bioinformatics tool; biomarker discovery; biomarker identification; cancer biomarkers; cancer therapy; candidate marker; cell type; clinically relevant; computerized tools; crowdsourcing; data analysis pipeline; data integration; data modeling; data portal; data quality; design; disease phenotype; disorder subtype; immune activation; improved; insight; machine learning prediction; member; multiple omics; multiple reaction monitoring; neoplastic cell; novel; open source; precision medicine; predictive marker; programs; protein biomarkers; proteogenomics; small molecule; success; task analysis; tool; transcription factor; transcriptome; transcriptomics; treatment response; treatment strategy; tumor; tumor growth; tumor microenvironment; user friendly software; user-friendly; web app; web services; web-based tool

PROJECT SUMMARY The goal of our PGDAC is to improve our understanding of the proteogenomic complexity of tumors. Towards this goal, our First Aim is to apply multiomics and network based system learning to reveal causative molecular regulatory relationships contributing to varieties of phenotypes in cancer using CPTAC proteogenomic data. We will start with rigorous preprocessing and quality control using a pipeline tailored to MS-based proteomics data to detect and correct batch effects, outliers, sample labeling errors, as well as to impute missing values (Aim 1.1). We will then utilize novel statistical tools to jointly model ≥6 types of omics data to systematically characterize functional impact of DNA alterations (such as DNA mutations, CNA, and methylations) (Aim 1.2). Such cis-/trans-regulatory networks will help us to elucidate how protein or pathway activities are shaped by genomic alterations in tumor cells. We will also construct protein/PTM co-expression networks based on global-, phospho-, glyco- and other PTM-proteomics data (Aim 1.3). When constructing these networks, we will use and create advanced computational tools to effectively borrow information from literature, publicly available open databases, and transcriptome profiles. Moreover, we will study cell type composition from bulk tissue using novel multi-omics deconvolution analyses, and identify immune subtypes with distinct immune activation or evasion mechanisms (Aim 1.4). Furthermore, we will perform comprehensive investigation of kinase and transcription factor activities by leveraging publicly available data extracted and processed from many regulatory network databases (Aim 1.5). All Aims 1.2-1.5 will contribute to a large collection of functionally related protein/PTM sets, co-expression network modules, immune signatures, as well as kinase/TF activity scores. These features and feature-sets will then be tested for their associations with disease phenotypes (Aim 1.6). For all analysis tasks in Aim 1, we will derive an integrated view of commonalities and differences across multiple tumor types via Pan-Cancer analyses. Our Second Aim is to further develop methods, software, and web-based tools to optimize the data analyses of our PGDAC. We will develop novel statistical/computational tools; implement these methods as computationally efficient and user- friendly software; and construct an integrated data analysis pipeline (Aim 2.1). We also plan to develop a set of web-based services for querying, visualizing, and interpreting analysis results from CPTAC studies (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 apply machine-learning-based prediction models on features and feature-sets from Aim 1 to identify protein biomarkers that predict disease outcome, treatment responses, and therapeutically distinct disease subtypes (Aim 3.1). We will also query disease related gene, protein, and PTM signatures against function perturbation databases, such as the LINCS L1000 database, to prioritize small molecules and drugs that could be tested for attenuating tumor growth or treatment response (Aim 3.2).

项目摘要 我们的PGDAC的目标是提高我们对肿瘤蛋白质组复杂性的理解。朝向 为了实现这一目标，我们的第一个目标是应用多组学和基于网络的系统学习来揭示因果关系， 利用CPTAC研究癌症中多种表型的分子调控关系 蛋白基因组学数据。我们将从严格的预处理和质量控制开始， 基于MS的蛋白质组学数据可检测和纠正批次效应、离群值、样品标记错误， 估算缺失值（目标1.1）。然后，我们将利用新的统计工具联合建模≥6种类型的组学 数据，以系统地表征DNA改变的功能影响（如DNA突变，CNA， 甲基化）（目标1.2）。这种顺式/反式调控网络将有助于我们阐明蛋白质或途径如何在细胞内发挥作用。 活性是由肿瘤细胞中的基因组改变形成的。我们还将构建蛋白质/PTM共表达系统， 基于全局、磷酸化、糖基和其他PTM蛋白质组学数据的网络（目标1.3）。施工时 这些网络，我们将使用和创建先进的计算工具，有效地借用信息， 文献、公开可用的开放数据库和转录组谱。此外，我们将研究细胞类型 使用新的多组学去卷积分析，从大量组织中提取免疫组分，并鉴定免疫亚型 具有不同的免疫激活或逃避机制（目的1.4）。此外，我们将全面执行 通过利用提取的公开可用数据研究激酶和转录因子活性， 从许多监管网络数据库中处理（目标1.5）。所有目标1.2-1.5都将有助于 功能相关的蛋白质/PTM集合、共表达网络模块、免疫特征的集合，以及 作为激酶/TF活性评分。然后将测试这些功能和功能集与 疾病表型（目标1.6）。对于目标1中的所有分析任务，我们将得出以下综合视图： 通过泛癌症分析，了解多种肿瘤类型的共性和差异。我们的第二个目标是 进一步开发方法，软件和基于网络的工具，以优化我们的PGDAC的数据分析。我们将 开发新的统计/计算工具;实施这些方法，计算效率和用户- 友好的软件;和构建一个集成的数据分析管道（目标2.1）。我们还计划开发一套 基于Web的服务，用于查询、可视化和解释CPTAC研究的分析结果（目标2.2）。 我们的第三个目标是提名新的基于蛋白质的癌症生物标志物和药物靶点进行进一步研究 通过靶向蛋白质组学分析。我们将首先在特征上应用基于机器学习的预测模型， 目标1的特征集，以鉴定预测疾病结局、治疗反应和 治疗上不同的疾病亚型（目标3.1）。我们还将查询疾病相关基因、蛋白质和PTM 签名对函数扰动数据库，如LINCS L1000数据库，以优先考虑小 可以测试用于减弱肿瘤生长或治疗反应的分子和药物（目标3.2）。