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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：
10401896
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/10401896
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

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.

摘要