THE CANCER EPITOPE DATABASE AND ANALYSIS RESOURCE

癌症表位数据库和分析资源

基本信息

批准号：
10647651
负责人：
Bjoern Peters
金额：
$ 83.99万
依托单位：
LA JOLLA INSTITUTE FOR IMMUNOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-05 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10647651
关键词：
Address Adopted Adoptive Transfer Animal Model Antibodies Attention Autoimmunity B-Cell Antigen Receptor B-Lymphocytes Benchmarking Binding Bioinformatics Biological Cancer Biology Cancer Histology Catalogs Clinical Clinical Research Collection Communicable Diseases Communities Complement Contracts Data Data Set Databases Development Disease Outcome Engineering Epitopes Evaluation Exclusion Feedback Funding Goals Human Hypersensitivity Immune Immunologics Immunology Immunotherapy Individual Informatics Information Technology Infrastructure Intuition Journals Learning Link Literature Machine Learning Malignant Neoplasms Methodology Modification Mutation National Institute of Allergy and Infectious Disease Nature Ontology Outcome Performance Procedures Process Property Publications Publishing Recurrence Reporting Research Research Personnel Resources Role Sampling Specificity Study models T-Cell Immunologic Specificity T-Cell Receptor T-Lymphocyte T-Lymphocyte Epitopes Technology Therapeutic Transplantation Tumor Antigens Tumor Tissue Tumor-Infiltrating Lymphocytes United States National Institutes of Health Vaccination Vaccines Validation anticancer research cancer immunotherapy clinical phenotype cost effective database structure design disease phenotype experience functional improvement immune checkpoint blockade immunogenicity interest interoperability meetings neoantigens novel outreach outreach program personalized medicine predictive tools programs prospective prototype receptor segregation single cell sequencing success tool translational cancer research treatment strategy tumor tumor immunology web site web-accessible

项目摘要

ABSTRACT Recent years have witnessed a dramatic rise in interest towards cancer epitopes in general, and neoepitopes that encompass mutations arising in a given tumor in particular. Current lines of research examine how the epitope load in a given tumor relates to the success of checkpoint blockade treatments, and how to utilize epitope-based vaccines and adoptive transfer of epitope-specific T cells for personalized therapies. For these purposes, neoepitopes that are recurrently recognized in different individuals are of particular interest, which has also re-ignited interest in epitopes identified in classic tumor-associated antigens. Along with the interest in cancer epitopes, there is also interest in the TCRs and BCRs specifically recognizing them, as these have the potential to be used in therapeutic approaches, and they can aid in basic studies to infer the specificity of T cells or B cells characterized in single cell sequencing data. This resurgence of interest in epitopes has created a need to catalog and make accessible to the scientific community all epitope data, also linked to the biological, immunological, and clinical contexts. The ultimate goal is to come “full circle” and link epitope recognition and immunological readouts to clinical outcomes and treatment strategies alike. In parallel, there is an urgent need to develop resources for epitope prediction and analysis tools that provide access to predictive strategies and provide objective evaluations of their performance in the relevant biological, immunological, and clinical contexts. Recent years have also witnessed the publication of multiple original methodologies that reported sometimes impressive gains in the predictions of cancer epitopes. However, several of these studies were difficult to evaluate, because the methodologies and/or datasets were not fully available in a format that was readily executable. As a result, their performance could not be properly benchmarked on independent datasets. This is also because effective benchmarking on independent datasets requires the assembly of novel datasets of sufficient size and diversity. To overcome all of these information technology challenges, we propose to design and implement the Cancer Epitope Database and Analysis Resource (CEDAR), which will provide a freely accessible, comprehensive collection of cancer epitope and receptor data curated from the literature, and provide easily accessible epitope and TCR/BCR target prediction and analysis tools. As the cancer epitope data are curated, they will be used as a transparent benchmark of how well prediction tools perform, and also to develop new prediction tools for the analysis resource component of CEDAR. CEDAR will leverage our expertise from developing the Immune Epitope Database and Analysis Resource (IEDB), which is fully operational and widely used by researchers globally. CEDAR will directly complement other projects currently funded through the NIH ITCR program that provide resources and tools related to cancer omics data. Finally, we will engage in outreach activities to improve functions, user interfaces, and interoperability with other ITCR tools and promote the use of CEDAR in cancer research.

抽象的近年来，人们对癌症表位和新表位的兴趣急剧上升目前的研究主要包括特定肿瘤中发生的突变。给定肿瘤中的表位负载与检查点封锁治疗的成功以及如何利用有关基于表位的疫苗和表位特异性 T 细胞的过继转移用于个性化治疗。出于目的，在不同个体中反复识别的新表位特别令人感兴趣，这已也重新点燃了人们对经典肿瘤相关抗原中鉴定的表位的兴趣。癌症表位，人们对专门识别它们的 TCR 和 BCR 也很感兴趣，因为它们具有具有用于治疗方法的潜力，并且它们可以帮助基础研究推断 T 细胞的特异性单细胞测序数据中表征的 B 细胞或 B 细胞对表位的兴趣重新兴起，创造了一个新的领域。需要对所有表位数据进行编目并向科学界提供，这些数据也与生物、最终目标是“回到原点”并将表位识别与免疫学和临床背景联系起来。同时，迫切需要临床结果和治疗策略的免疫学读数。开发表位预测和分析工具的资源，提供预测策略和对其在相关生物学、免疫学和临床背景下的表现进行客观评估。近年来也见证了多种原始方法论的出版，这些方法论有时报告然而，其中一些研究在癌症表位的预测方面取得了令人瞩目的成果。评估，因为方法和/或数据集尚未以易于使用的格式完全可用因此，它们的性能无法在独立数据集上进行正确的基准测试。还因为对独立数据集的有效基准测试需要组装新的数据集为了克服所有这些信息技术挑战，我们建议进行设计。并实施癌症表位数据库和分析资源（CEDAR），该资源将免费提供从文献中整理出的可访问、全面的癌症表位和受体数据集合，并提供易于获取的表位和 TCR/BCR 靶点预测和分析工具作为癌症表位数据。经过精心策划，它们将被用作预测工具性能的透明基准，并用于开发 CEDAR 分析资源组件的新预测工具将利用我们的专业知识。开发免疫表位数据库和分析资源（IEDB），该资源已全面投入使用并广泛应用全球研究人员使用的 CEDAR 将直接补充目前由 NIH 资助的其他项目。 ITCR 计划提供与癌症组学数据相关的资源和工具最后，我们将进行外展活动。改进功能、用户界面以及与其他 ITCR 工具的互操作性并促进使用的活动 CEDAR 在癌症研究中的应用。