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.

摘要 近年来，人们对一般癌症表位和新表位的兴趣急剧增加。 包括特定肿瘤中出现的突变。目前的研究方向是研究 给定肿瘤中的表位负荷与检查点阻断治疗的成功有关，以及如何利用 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。为这些 出于某些目的，在不同个体中反复识别的新表位是特别感兴趣的， 也重新点燃了对经典肿瘤相关抗原中鉴定的表位的兴趣。沿着的是 除了癌症表位之外，还对特异性识别它们的TCR和BCR感兴趣，因为它们具有 有可能用于治疗方法，它们可以帮助基础研究推断T细胞的特异性， 或在单细胞测序数据中表征的B细胞。这种对表位的兴趣的复苏创造了一个 需要对所有表位数据进行编目，并使科学界能够获得这些数据，这些数据也与生物学相关， 免疫学和临床背景。最终的目标是“完整循环”，并将表位识别和 免疫学读数与临床结果和治疗策略类似。与此同时，迫切需要 开发表位预测和分析工具的资源，提供预测策略， 在相关生物学、免疫学和临床背景下对其性能进行客观评价。 近年来还公布了多种原创方法， 在癌症表位预测方面取得了令人印象深刻的进展。然而，其中一些研究很难 评估，因为方法和/或数据集没有以容易获得的格式完全提供， 可执行文件。因此，它们的性能无法在独立数据集上进行适当的基准测试。这是 还因为对独立数据集进行有效的基准测试需要组装新的数据集， 足够的规模和多样性。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），这将提供一个免费的 可访问的，全面的癌症表位和受体数据收集从文献中策划，并提供 易于获得的表位和TCR/BCR靶点预测和分析工具。由于癌症表位数据是 经过精心策划，它们将被用作预测工具表现如何的透明基准，并用于开发 CEDAR分析资源部分的新预测工具。CEDAR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），该数据库已全面投入使用， 被全球研究人员使用。CEDAR将直接补充目前通过NIH资助的其他项目 ITCR计划，提供与癌症组学数据相关的资源和工具。最后，我们将开展外联活动， 改进功能、用户界面和与其他ITCR工具的互操作性，并促进使用 癌症研究中的CEDAR。