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

用于基于细胞毒性 T 淋巴细胞的癌症免疫疗法的精准肿瘤新抗原识别流程

• 批准号: 10332251
• 负责人: ELLIS L REINHERZ
• 金额: $ 71.33万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10332251
• 关键词: Algorithms; Alkylation; Alleles; Alternative Splicing; Antigens; Basic Science; Binding; Bioinformatics; Biopsy; Biopsy Specimen; Blood capillaries; CD8-Positive T-Lymphocytes; Calculi; Cell surface; Cells; Chemicals; Clinical; Code; Complex; Computer software; Cysteine; Cytotoxic T-Lymphocytes; Dana-Farber Cancer Institute; Data; Data Collection; Data Files; Deposition; Detection; Disease; Disease remission; Epitopes; Evolution; Fine-needle biopsy; Future; Gene Fusion; Genomic Segment; Genomics; Gold; HLA Antigens; HLA-A gene; Immune; Immune system; Immunologic Monitoring; Immunology procedure; Immunotherapy; Individual; Industrialization; Ions; Liquid Chromatography; Machine Learning; Major Histocompatibility Complex; Malignant Neoplasms; Mass Spectrum Analysis; Mediating; Messenger RNA; Methods; Minority; Modification; Needle biopsy procedure; Operative Surgical Procedures; Patients; Pattern; Peptide Synthesis; Peptide/MHC Complex; Peptides; Performance; Polyadenylation; Principal Investigator; Process; Proteins; Protocols documentation; Recovery; Reference Standards; Running; Sampling; Service setting; Services; Site; Surface; T-Lymphocyte; Technology; Therapeutic; Time; Tissues; Translating; Translational Research; Untranslated RNA; Vaccination; Western Blotting; base; bioinformatics pipeline; cancer cell; cancer genome; cancer immunotherapy; design; global health; immune checkpoint blockade; industry partner; insertion/deletion mutation; instrumentation; nanoscale; neoantigens; neoplastic cell; next generation sequencing; novel; prediction algorithm; pressure; public database; transcriptome sequencing; transcriptomics; tumor; vaccine development

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.

摘要 对免疫系统进行编程以检测新抗原并摧毁肿瘤对于有效治疗肿瘤至关重要。 免疫疗法。到目前为止，来自下一代测序的肿瘤新表位的生物信息学预测 (NGS)这些信息已经单独使用或与免疫测定结合使用以间接推断新表位 识别.不幸的是，只有一小部分预测的表位是表面展示为HLA结合的， 肽（pMHC），细胞溶解性T淋巴细胞（CTL）靶向所需的过程。此外，免疫测定 存在高假阳性率和假阴性率，从而混淆正确识别。常规 质谱（MS）方法来询问pMHC，被称为细胞的免疫肽组， 由于HLA回收率差，需要多次样品运行以实现足够的肽覆盖， 需要大量的肿瘤细胞，所有这些特征对于常规临床应用都是不切实际的。我们的学术-工业 合作伙伴关系（AIP）推进了商业管道的创建，以提供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 我们的跨学科/多机构战略联盟结合了基本的 Dana Farber癌症研究所的研究，以及Curacloud Corporation和JPT Peptide的工业专业知识 技术.我们建议部署阿托摩尔（10-18）泊松检测液相色谱数据 用于抗原发现的独立采集（LC-DIA）MS方法，以电子方式记录和捕获整个 免疫肽组包含大量的自身肽和稀疏的新抗原， 通过临床穿刺活检获得的肿瘤细胞数量（106）。这种方法改变了上述MS 演算，并允许在数据收集后的任何时间点使用现有的商业上的新抗原搜索 销售的MS仪器。在目标1中，新表位候选物应在高浓度下化学合成。 通过JPT运行的每纳米级多达6，000个肽的通量池，用于MS碎片分析和洗脱 使用LC-DIAMS对单个肿瘤进行确定性新抗原鉴定的参比标准品作图 基于DFCI技术，优化每个步骤。在目标2中，我们将使用来自肿瘤细胞的NGS数据， 与Curacloud的生物信息学相结合，预测编码区和非编码区产生的新表位 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 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级。一个 最终用户服务应建立在上述所有集成技术的基础上。从初始肿瘤 从活检到新表位鉴定，预计时间尺度约为一个月。这个通用 新表位精确识别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，通知治疗调整， 必需的.