A precision tumor neoantigen identification pipeline for cytotoxic T-lymphocyte-based cancer immunotherapies

摘要 对免疫系统进行编程以检测新抗原并摧毁肿瘤是有效的关键 免疫疗法。到目前为止，基于下一代测序的肿瘤新表位的生物信息学预测 (NGS)信息已单独或与免疫学分析一起用于间接推断新表位 身份证明。不幸的是，预测的表位中只有一小部分是表面显示的与人类白细胞抗原结合的 多肽(PMHC)，这是细胞溶解T淋巴细胞(CTL)靶向所需的过程。此外，免疫学检测 同时遭受高假阳性和假阴性的困扰，混淆了正确的识别。传统型 质谱学(MS)方法询问pMHC，被称为细胞的免疫肽，受到了 从不良的人类白细胞抗原回收，需要多个样本运行，以实现足够的多肽覆盖和 需要大量的肿瘤细胞，所有这些特征都不适用于常规的临床用途。我们的学术--工业 合作伙伴关系(AIP)推进了一条商业管道的创建，以输送个性化的肿瘤新抗原 鉴定，整合基于NGS的基因组学和转录组学，生物信息学，化学肽组学和 一种新颖、超敏感的女士形式我们的跨学科/多机构战略联盟结合了基本的 达纳·法伯癌症研究所的研究与库拉索特公司和JPT多肽的工业专业知识 技术。我们建议部署阿托莫尔(10-18)泊松检测液相色谱数据 独立获取(LC-DIA)MS抗原发现方法，以电子记录和捕获整个 由多个自体多肽和稀疏新抗原组成的免疫肽从小到小一次运行 临床针吸活检获得的肿瘤细胞数(106个)。这种方法改变了前述MS 微积分并允许在使用现有商业数据收集数据后的任何时间点进行新抗原搜索 市面上销售的MS仪器。在目标1中，新表位候选者应以高 JPT运行的每纳米级最多6,000个多肽的吞吐量池，用于MS碎片分析和洗脱 应用LC-DIAMS在单个肿瘤上绘制确定新抗原鉴定的参考标准 基于DFCI技术的样品，优化每一步。在目标2中，我们将使用来自肿瘤细胞的NGS数据 与库拉索特生物信息学联合预测来自编码区和非编码区的新表位 能够与患者的每个HLA-A、-B和/或-C等位基因相互作用。基于机器学习的新表位研究 应开发结合MS数据和其他结果的排序算法，以确定候选人的优先顺序。一个 应建立包含上述所有综合技术的最终用户服务。从最初的肿瘤 从活组织检查到鉴定新表位，预计大约需要一个月的时间。这是通用的 新表位精密鉴定流水线适用于多种免疫治疗方案以及免疫 监测原始和任何转移部位的肿瘤演变，为治疗调整提供信息 必填项。

ABSTRACT Programming the immune system to detect neoantigens and destroy tumors is critical for effective immunotherapy. Until now, bioinformatic prediction of neoepitopes on tumors from Next Generation Sequencing (NGS) information has been used alone or in conjunction with immunological assays to indirectly infer neoepitope identification. Unfortunately, only a small fraction of predicted epitopes are surface-displayed as HLA-bound peptides (pMHC), a process required for cytolytic T lymphocyte (CTL) targeting. Moreover, immunologic assays suffer from both high false positive and false negative rates, confounding correct identification. Conventional mass spectrometry (MS) approaches to interrogate the pMHC, referred to as the cell's immune peptidome, suffer from poor HLA recovery, requirement for multiple sample runs to achieve adequate peptide coverage and necessitate large numbers of tumor cells, all features impractical for routine clinical use. Our Academic-Industrial Partnership (AIP) advances the creation of a commercial pipeline to deliver personalized tumor neoantigen identification, integrating NGS-based genomics and transcriptomics, bioinformatics, chemical peptidomics and a novel, ultrasensitive form of MS. Our interdisciplinary/multi-institutional strategic alliance combines basic research at Dana Farber Cancer Institute with industrial expertise at Curacloud Corporation and JPT Peptide Technologies. We propose deployment of an attomole (10-18) Poisson detection liquid chromatography-data independent acquisition (LC-DIA) MS method for antigen discovery to electronically record and capture the entire immune peptidome comprising both numerous self-peptides and sparse neoantigens in a single run from small numbers of tumor cells (106) retrieved by clinical needle biopsy. This approach changes the aforementioned MS calculus and permits neoantigen search at any point following data collection using existing commercially marketed MS instrumentation. In Aim 1 neoepitope candidates shall be chemically synthesized in high throughput pools of up to 6,000 peptides per nanoscale run by JPT for MS fragmentation analysis and elution mapping reference standards for definitive neoantigen identification using LC-DIAMS on individual tumor samples based on DFCI technology, optimizing each step. In Aim 2 we shall use NGS data from tumor cells in conjunction with bioinformatics at Curacloud to predict neoepitopes arising from coding and non-coding regions capable of interacting with each HLA-A, -B and/or -C allele of a patient. Machine learning-based neoepitope ranking algorithms incorporating MS data and other results shall be developed for candidate prioritization. An end user service shall be established involving all aforementioned integrative technologies. From initial tumor biopsy to identification of neoepitopes, a time scale of approximately one month is anticipated. This generic neoepitope precision identification pipeline is applicable to multiple immunotherapy protocols as well as immune monitoring of tumor evolution at the original and any metastatic site, informing therapeutic adjustments as required.