Informatics tools for identification, prioritization and clinical application of neoantigens

用于新抗原识别、优先排序和临床应用的信息学工具

• 批准号: 10219995
• 负责人: Malachi Griffith
• 金额: $ 40.39万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10219995
• 关键词: Address; Affinity; Alleles; Animal Model; B-Lymphocytes; Binding; Cancer Vaccines; Cellular biology; Characteristics; Clinical; Clinical Trials; Collaborations; Complex; Computer software; Data; Data Set; Databases; Dendritic Cell Vaccine; Development; Disease; Ensure; Evaluation; Foundations; Future; Gene Fusion; Goals; Guidelines; Immunobiology; Immunologic Factors; Immunotherapy; Informatics; Knowledge; Lead; MHC Class I Genes; MHC binding peptide; Measures; Mutation; Nucleotides; Patients; Peptide Transport; Peptides; Performance; Pharmaceutical Preparations; Plasmid Cloning Vector; Predictive Value; Production; Protein Isoforms; Publishing; Resources; Signal Transduction; Somatic Mutation; Source; T cell response; T-Cell Receptor; T-Lymphocyte; Testing; Therapeutic; Training; Transcript; Tumor Antigens; Tumor Biology; Tumor Specific Peptide; Untranslated RNA; Vaccine Clinical Trial; Vaccine Design; Vaccines; Validation; Viral Vector; Visualization; Work; anti-tumor immune response; base; cancer cell; cancer immunotherapy; cancer type; clinical application; design; experience; immune checkpoint blockade; immunogenic; immunogenicity; immunotherapy clinical trials; improved; informatics tool; insertion/deletion mutation; meetings; neoantigens; neoplastic cell; novel; open source; open source tool; plasmid DNA; pre-clinical; predicting response; predictive tools; resistance mechanism; success; tool; tool development; tumor; tumor immunology; vaccine delivery; vaccine trial

Project Summary/Abstract Somatic mutations in cancer cells lead to the production of neoantigens: patient- and tumor-specific peptides that are capable of inducing T cell recognition. Recent clinical trials have established that, when introduced in a vaccine, these neoantigens can stimulate anti-tumor immune responses. The path to producing such a personalized vaccine begins with sequencing a patient’s tumor, identifying candidate somatic mutations and then computationally predicting which neoepitopes will be most effective at stimulating a T-cell response. This prediction step should ideally assess a complex interplay of factors, including the type of somatic mutation, the patient’s class I and II HLA alleles, peptide processing, peptide transport, peptide-MHC binding and many co- factors of immune recognition and signaling. The best current approaches focus almost entirely on a single factor (peptide-MHC binding) and have only a 16-43% success rate in predicting immunogenic peptides. To address this challenge we will develop pVACtools, an informatics toolkit for comprehensive identification, characterization, and clinical application of neoantigens. This tool will be the first to support all major neoepitope sources including insertions, deletions, transcript isoforms, gene fusions, peptides from normally non-coding regions, and B cell or T cell rearrangements (BCRs/TCRs). We will also integrate analysis of Class I and II peptide-MHC binding. All tools will be developed to support foundational pre-clinical work in animal models of immunotherapy. Furthermore, we will test several specific hypotheses relating to new predictors of immunogenicity. To elucidate these factors and enhance prioritization of neoantigens we will create the first open access database of experimentally and clinically validated neoantigens. Using these data we will address the question of what peptide-intrinsic and patient-specific features determine the therapeutic potential of a neoantigen. To validate their translational potential, we will apply our neoantigen tools to clinical trials involving checkpoint blockade drugs and personalized cancer vaccines. We will develop a visualization interface that facilitates clinical review and selection of neoantigen candidates for several vaccine delivery platforms. These tools will be used to perform analysis of >200 cases from ongoing vaccine trials to evaluate their performance and address key outstanding immunobiology questions including: (a) the importance of particular neoantigen sources in specific cancer types, (b) the importance of accurately determining HLA mutation/expression, (c) the significance of having both MHC class I and II restricted peptides in a vaccine, (d) how to identify specific neoepitope/TCR pairings, and (e) how neoantigens contribute to mechanisms of resistance to immunotherapies. These tools will thus enable fundamental studies of T cell biology, lead to more effective personalized cancer vaccine designs, and support better prediction of response to checkpoint blockade therapy. Finally, based on these experiences and in collaboration with our team of clinical vaccine trial leaders, we will develop detailed guidelines and training materials for neoantigen analysis.

项目总结/摘要 癌细胞中的体细胞突变导致新抗原的产生：患者和肿瘤特异性肽 能够诱导T细胞识别。最近的临床试验已经证实，当引入一种 疫苗，这些新抗原可以刺激抗肿瘤免疫应答。生产这种产品的途径 个性化疫苗首先对患者的肿瘤进行测序，识别候选体细胞突变， 然后通过计算预测哪些新表位将最有效地刺激T细胞应答。这 预测步骤应该理想地评估因素的复杂相互作用，包括体细胞突变的类型， 患者的I类和II类HLA等位基因，肽加工，肽转运，肽-MHC结合和许多共- 免疫识别和信号传导的因素。目前最好的方法几乎完全集中在一个单一的 因子（肽-MHC结合），并且在预测免疫原性肽中仅具有16-43%的成功率。到 为了应对这一挑战，我们将开发pVACtools，一个用于全面识别的信息学工具包， 新抗原的表征和临床应用。该工具将是第一个支持所有主要 新表位来源，包括插入、缺失、转录物同种型、基因融合、来自正常 非编码区和B细胞或T细胞重排（BCR/TCR）。我们还将整合类的分析 I和II肽-MHC结合。将开发所有工具，以支持动物基础临床前工作 免疫治疗的模型。此外，我们将测试几个具体的假设有关的新的预测因素， 免疫原性为了阐明这些因素并增强新抗原的优先级，我们将创建第一个 实验和临床验证的新抗原的开放访问数据库。利用这些数据，我们将 什么样的肽内在特征和患者特异性特征决定了A 新抗原为了验证它们的翻译潜力，我们将把我们的新抗原工具应用于临床试验， 检查点封锁药物和个性化癌症疫苗。我们将开发一个可视化界面， 有助于临床审查和选择用于几种疫苗递送平台的新抗原候选物。这些 将使用工具对来自正在进行的疫苗试验的>200个病例进行分析，以评估其性能 并解决关键的突出免疫生物学问题，包括：（a）特定新抗原的重要性 来源，（B）准确确定HLA突变/表达的重要性，（c） 在疫苗中同时具有MHC I类和II类限制性肽的意义，（d）如何鉴定特异性 新表位/TCR配对，以及（e）新抗原如何有助于对新表位/TCR配对的抗性机制。 免疫疗法因此，这些工具将使T细胞生物学的基础研究成为可能， 个性化癌症疫苗设计，并支持更好地预测对检查点封锁的反应 疗法最后，根据这些经验，并与我们的临床疫苗试验领导团队合作， 我们将为新抗原分析制定详细的指南和培训材料。