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

基本信息

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
项目类别：
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

项目摘要

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).

项目概要我们 PGDAC 的目标是提高我们对肿瘤蛋白质组复杂性的理解。为了这个目标，我们的首要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使用 CPTAC 分析分子调控与癌症各种表型的关系我们将从蛋白质组数据开始，使用专门定制的流程进行严格的预处理和质量控制。基于 MS 的蛋白质组学数据，用于检测和纠正批次效应、异常值、样品标记错误，以及然后，我们将利用新颖的统计工具对 ≥6 种类型的组学进行联合建模。系统地表征 DNA 改变（例如 DNA 突变、CNA 和甲基化）（目标 1.2）。这样的顺式/反式调控网络将帮助我们阐明蛋白质或途径如何发挥作用。我们还将构建蛋白质/PTM 共表达。基于全局、磷酸、糖和其他 PTM 蛋白质组学数据的网络（目标 1.3）。这些网络，我们将使用和创建先进的计算工具来有效地借用信息此外，我们将研究细胞类型。使用新颖的多组学反卷积分析从大块组织中提取成分，并识别免疫亚型具有独特的免疫激活或逃避机制（目标1.4）此外，我们将进行全面的研究。通过利用提取和公开的公开数据来研究激酶和转录因子活性从许多监管网络数据库（目标 1.5）中处理的所有目标 1.2-1.5 将有助于产生巨大的影响。功能相关蛋白质/PTM 集、共表达网络模块、免疫特征的集合然后将测试这些特征和特征集的关联性。对于目标 1 中的所有分析任务，我们将得出一个综合视图。通过泛癌症分析了解多种肿瘤类型的共性和差异。我们将进一步开发方法、软件和基于网络的工具来优化 PGDAC 的数据分析。开发新颖的统计/计算工具；实现这些方法的计算效率和用户友好性友好的软件；并构建一个集成的数据分析管道（目标2.1）。基于网络的服务，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型基于蛋白质的癌症生物标志物和药物靶点以供进一步研究通过有针对性的蛋白质组学分析，我们将首先应用基于机器学习的特征和预测模型。目标 1 中的特征集，用于识别预测疾病结果、治疗反应和的蛋白质生物标志物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。针对函数扰动数据库（例如 LINCS L1000 数据库）的签名，以优先考虑小可以测试减弱肿瘤生长或治疗反应的分子和药物（目标 3.2）。