THE CANCER EPITOPE DATABASE AND ANALYSIS RESOURCE

癌症表位数据库和分析资源

• 批准号: 10187436
• 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
• 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/10187436
• 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; Ensure; Epitopes; Evaluation; Feedback; Funding; Goals; Human; Hypersensitivity; Immune; Immunologics; Immunology; Immunotherapy; Individual; Informatics; Information Technology; Infrastructure; Intuition; Journals; 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; Receptor Cell; 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; base; cancer immunotherapy; clinical phenotype; cost effective; data tools; database structure; design; disease phenotype; experience; immune checkpoint blockade; immunogenicity; improved functioning; interest; interoperability; meetings; neoantigens; novel; outreach; outreach program; personalized medicine; programs; prospective; prototype; receptor; single cell sequencing; success; tool; translational cancer research; treatment strategy; tumor; tumor immunology; web site; web-accessible

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，总体上，人们对癌症表位的兴趣急剧上升，而neeppitopes 尤其是在给定肿瘤中引起的突变。当前研究考试的界限 给定肿瘤中的表位负荷与检查点封锁治疗的成功有关，以及如何利用 基于表位的疫苗和对个性化疗法的表位特异性T细胞的适应性转移。为此 目的，在不同个体中经常认识的neoepitop是特别感兴趣的，这是 还重新点了对经典肿瘤相关抗原鉴定的表位的兴趣。以及对 癌症表位，在TCR和BCR中也很有趣，因为它们具有 在治疗方法中使用的潜力，它们可以帮助基础研究来推断T细胞的特异性 或在单细胞测序数据中表征的B细胞。对表位的这种兴趣的复兴创造了一个 需要分类并使科学界都可以访问所有情节数据，这也与生物学相关联， 免疫学和临床环境。最终目标是进入“完整圈”并链接表位识别和 免疫学读数对临床结果和治疗策略都一样。并行，迫切需要 开发表位预测和分析工具的资源，以提供预测策略的访问和 在相关的生物学，免疫学和临床环境中提供对其性能的客观评估。 近年来也见证了多种原始方法的发布，这些方法有时报道 在癌症表位的预测中获得了令人印象深刻的收益。但是，其中一些研究很难 评估，因为方法和/或数据集并未以容易的格式完全使用 可执行。结果，他们的性能无法在独立的数据集上正确进行基准测试。这是 同样，在独立数据集上有效的基准测试需要组装新的数据集 足够的大小和多样性。为了克服所有这些信息技术挑战，我们建议设计 并实施癌症表位数据库和分析资源（CEDAR），该资源将提供免费 从文献中策划的癌症表位和接收器数据的可访问，全面收集，并提供 易于访问的表位和TCR/BCR目标预测和分析工具。由于癌症表位数据是 经过精选，它们将被用作预测工具的性能以及开发的透明基准 用于分析资源组件的新预测工具。雪松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），该资源是完全运行且广泛运行的 全球研究人员使用。雪松将直接补充目前通过NIH资助的其他项目 ITCR计划提供与癌症OMICS数据有关的资源和工具。最后，我们将进行外展 改善功能，用户界面以及与其他ITCR工具的互操作性的活动，并促进使用 癌症研究中的雪松。