Informatics tools for identification, prioritization and clinical application of neoantigens

项目摘要/摘要(来自上级奖项：U01 CA248235，按说明执行) 癌细胞的体细胞突变导致新抗原的产生：患者和肿瘤特异性多肽 能够诱导T细胞识别的基因。最近的临床试验证明，当在一种 疫苗方面，这些新抗原可以激发抗肿瘤免疫反应。生产这样一种 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和 然后通过计算预测哪些新表位在刺激T细胞反应方面最有效。这 预测步骤应该理想地评估各种因素的复杂相互作用，包括体细胞突变的类型， 患者的人类白细胞抗原I、II类等位基因、多肽加工、多肽转运、多肽-MHC结合等多方面的研究 免疫识别和信号传递的因素。目前最好的方法几乎完全集中在单一的 因子(肽-MHC结合)，在预测免疫原肽方面只有16-43%的成功率。至 为了应对这一挑战，我们将开发pVAC工具，这是一个用于全面识别的信息学工具包， 新抗原的特性、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、转录本异构体、基因融合、正常多肽 非编码区，以及B细胞或T细胞重排(BCR/TCR)。我们还将整合对班级的分析 I和II肽与MHC结合。将开发所有工具来支持动物的基础临床前工作 免疫治疗的模式。此外，我们将测试与新的预测因素有关的几个具体假设 免疫原性。为了阐明这些因素并加强新抗原的优先顺序，我们将创建第一个 开放获取实验和临床验证的新抗原数据库。使用这些数据，我们将解决 什么肽的内在特征和患者特有的特征决定了一种药物的治疗潜力 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，包括 检查站封锁药物和个性化癌症疫苗。我们将开发一个可视化界面， 促进临床审查和为几个疫苗递送平台选择新抗原候选者。这些 将使用工具对正在进行的疫苗试验中的200例病例进行分析，以评估其性能 并解决悬而未决的主要免疫生物学问题，包括：(A)特定新抗原的重要性 具体癌症类型的来源，(B)准确确定人类白细胞抗原突变/表达的重要性，(C) 疫苗中同时具有MHC I类和II类限制性多肽的意义，(D)如何识别特定的 新表位/TCR配对，以及(E)新抗原如何参与抗药性机制 免疫疗法。这些工具将使T细胞生物学的基础研究成为可能，从而导致更有效的 个性化癌症疫苗设计，并支持更好地预测检查站封锁的反应 心理治疗。最后，根据这些经验，并与我们的临床疫苗试验负责人团队合作， 我们将为新抗原分析制定详细的指南和培训材料。

Project Summary/Abstract (from parent award: U01 CA248235 as per instructions) 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.