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: 10581488
• 负责人: ELLIS L REINHERZ
• 金额: $ 66.08万
• 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/10581488
• 关键词: Algorithms; Alkylation; Alleles; Alternative Splicing; Antigen Presentation; 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; HLA Antigens; HLA-A gene; Immune; Immune system; Immunologic Monitoring; Immunology procedure; Immunotherapy; Individual; Industrialization; Institution; Ions; Liquid Chromatography; Machine Learning; Major Histocompatibility Complex; Malignant Neoplasms; Maps; Marketing; Mass Spectrum Analysis; Mediating; Messenger RNA; Methods; Minority; Modification; Needle biopsy procedure; Operative Surgical Procedures; Patients; Pattern; Peptide Synthesis; Peptide/MHC Complex; Peptides; Performance; Poly A; 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; Writing; bioinformatics pipeline; cancer cell; cancer genome; cancer immunotherapy; chemical synthesis; 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; web server

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.

抽象的 编程免疫系统以检测新抗原和破坏肿瘤对于有效 免疫疗法。到目前为止，下一代测序对肿瘤的新皮上的生物信息学预测 （NGS）信息已单独或与免疫学测定一起使用，以间接推断新皮标 鉴别。不幸的是，只有一小部分预测表位被表面播放为HLA结合 肽（PMHC），这是细胞溶解淋巴细胞（CTL）靶向的过程。此外，免疫学测定 遭受高误报和假负率的高度折磨，使正确的识别混淆。传统的 质谱法（MS）询问PMHC的方法，称为细胞的免疫肽组，遭受了 从较差的HLA恢复中，需要多次样本运行以实现足够的肽覆盖范围和 需要大量的肿瘤细胞，所有特征对于常规临床使用不切实际。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 识别，整合基于NGS的基因组学和转录组学，生物信息学，化学肽学和 MS的一种新颖的超敏感形式。我们的跨学科/多机构战略联盟结合了基本 Dana Farber癌症研究所具有工业专业知识的研究 技术。我们建议部署Attomole（10-18）泊松检测液相色谱数据 独立采集（LC-DIA）MS方法用于电子记录并捕获整个抗原发现的方法 免疫肽组在一个小型中既包含许多自肽和稀疏的新抗原 肿瘤细胞的数量（106）通过临床针头活检检索。这种方法改变了上述MS 微积分并允许在数据收集后的任何时刻使用现有的商业搜索 销售MS仪器。在AIM 1中，应在高度上化学合成新的Epitope候选者 JPT由MS碎片分析和洗脱运行的每纳米级最多6,000肽的吞吐量池 使用LC-DIAMS在单个肿瘤上使用LC-DIAM的确定新抗原鉴定的参考标准 基于DFCI技术的示例，优化每个步骤。在AIM 2中，我们将使用来自肿瘤细胞的NGS数据 与库拉克洛德（Curacloud）的生物信息学结合，以预测编码和非编码区域产生 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NeoEpitope 纳入MS数据和其他结果的排名算法应供候选优先级。一个 最终用户服务应建立涉及所有上述集成技术的。从最初的肿瘤 为识别新皮标的活检，预计将有大约一个月的时间尺度。这个通用 NeoEpitope Precision识别管道适用于多种免疫疗法方案以及免疫 监测原始和任何转移性部位的肿瘤演化，告知治疗调整为 必需的。