(PQ8) Patient- and tumor-specific biomarkers and mechanisms that predict irAEs resulting from checkpoint inhibition

(PQ8) 患者和肿瘤特异性生物标志物和预测检查点抑制引起的 irAE 的机制

基本信息

批准号：
10359717
负责人：
Justin M Balko
金额：
$ 50.71万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10359717
关键词：
Address Adverse reactions Affect Antigen Targeting Antigens Autoantibodies Autoantigens Autoimmune Diseases Autoimmunity Autopsy B-Lymphocytes Biological Markers Biological Specimen Banks Biopsy CTLA4 gene Cancer Patient Cell Compartmentation Cells Clinical Clonal Expansion Clone Cells Colitis Coupled Custom Data Development Disease Encephalitis Etiology Frequencies Genetic Immune checkpoint inhibitor Immunotherapy Incidence Institution Libraries Longitudinal Studies Lymphocyte Malignant Neoplasms Mediating Metastatic Melanoma Methods Muscle Myocarditis Myocardium Myositis Nature Nivolumab Normal tissue morphology Organ Outcome PD-1/PD-L1 Pathogenicity Pathology Patients Peptide/MHC Complex Peptides Peripheral Prospective Studies Proteome RNA Refractory Reporting Research Personnel Risk Risk Factors Risk Management Role Sampling Self Tolerance Site Skeletal Muscle Neoplasm Specimen T cell clonality T cell receptor repertoire sequencing T-Cell Activation T-Cell Receptor T-Lymphocyte T-Lymphocyte Epitopes T-cell receptor repertoire Technology Testing Therapeutic Uses Time Tissue Banks Tissues Transcript Translating Treatment outcome Tumor Markers Tumor Tissue Validation Yeasts anti-tumor immune response autoimmune toxicity autoreactivity biomarker development cancer therapy case control checkpoint inhibition checkpoint therapy clinical biomarkers clinically translatable digital exhaustion experience immune checkpoint blockade immune-related adverse events insight inter-institutional ipilimumab multidisciplinary novel novel therapeutics peripheral blood point of care predictive marker prevent prospective protein aminoacid sequence reconstitution repository response response biomarker screening sensor single-cell RNA sequencing success transcriptome transcriptomics tumor

项目摘要

PROJECT SUMMARY/ABSTRACT In this proposal, we will identify clinically-translatable predictive and early-response biomarkers for the development of immune-related adverse events (irAEs) caused by immune checkpoint inhibitor (ICI) therapy in cancer patients. Using both focused and unbiased screening approaches, we will leverage a large inter-institutional and multi-disciplinary team of investigators, as well as a large (>350 patients) retrospective and prospectively growing tissue and peripheral blood bank of specimens from ICI treated patients, many of whom developed severe irAEs. Using this tissue bank, as well as additional specimens prospectively collected at our institution and through collaborating institutions, we will identify TCRs and autoantibodies that are expanded or upregulated in HLA-matched patients experiencing severe irAEs. Using wide-net technologies (whole-proteome peptide microarray, 1 billion yeast pMHC display libraries, digital spatial profiling), we will identify pathogenic T and B cell antigens in peripheral blood and tissue before and after ICI therapy. In longitudinal studies, changes in TCR clonality, changes in autoantibody screening, and CyTOF for T cell compartments will be performed in patients experiencing irAE and in clinically/HLA-matched controls. Findings will be compared to treatment outcomes (clinical response and organ-specific irAEs) and we will test whether these biomarkers can be detected prior to ICI therapy initiation. Translatable autoantibody biomarkers will be validated with a novel point-of-care custom array technology for clinical utility. Finally we will profile the TCR repertoire in matched tumor and site-of-irAE specimens using single-cell RNA sequencing of T cells, coupled with antigen identification through a highly novel ~1 billion yeast pMHC display library approach to identify the pathogenic mechanism behind irAEs. Using these data, we will address three specific aims in this proposal: 1) we will prospectively characterize on-treatment cell-mediated mechanisms of irAEs; 2) we will determine whether irAE-associated autoantibodies or TCRs can be identified prior to treatment with ICIs; and 3) we will identify the antigen targets of pathogenic TCRs and profile their expression across tumor and diseased tissue. Due to the overwhelming success of ICIs, these treatments will be used in increasing numbers of patients and moved to earlier lines of therapy. Thus, the numbers of patients at risk for irAEs will continue to rise; this proposal will address the growing unmet need of how to identify and manage patients at risk for severe adverse sequelae from ICIs, while making new discoveries that identify the pathogenic mechanism of irAEs.

项目概要/摘要在本提案中，我们将确定临床可转化的预测和早期反应生物标志物，用于开发癌症患者因免疫检查点抑制剂（ICI）治疗引起的免疫相关不良事件（irAE）。使用我们将采用有针对性和公正的筛选方法，利用大型跨机构和多学科团队研究人员以及大量（> 350 名患者）回顾性和前瞻性生长的组织和外周血来自 ICI 治疗患者的标本库，其中许多人出现了严重的 irAE。使用该组织库，以及我们的机构和通过合作机构前瞻性收集的其他标本，我们将确定 TCR 和自身抗体在经历严重 irAE 的 HLA 匹配患者中扩增或上调。使用宽网技术（全蛋白质组肽微阵列、10 亿个酵母 pMHC 展示库、数字空间分析），我们将在 ICI 治疗前后识别外周血和组织中的致病性 T 和 B 细胞抗原。在纵向研究中，TCR 克隆性的变化、自身抗体筛选的变化以及 T 细胞区室的 CyTOF 将在经历 irAE 的患者和临床/HLA 匹配的对照中进行。研究结果将与治疗结果（临床反应和器官特异性 irAE），我们将测试这些生物标志物是否可以在 ICI 治疗开始之前检测到。可翻译的自身抗体生物标志物将通过新型护理点进行验证用于临床应用的定制阵列技术。最后，我们将分析匹配肿瘤和 irAE 位点中的 TCR 库使用 T 细胞的单细胞 RNA 测序，并通过高度新颖的 ~1 进行抗原鉴定十亿酵母 pMHC 展示库方法可识别 irAE 背后的致病机制。使用这些数据，我们将在该提案中实现三个具体目标：1）我们将前瞻性地描述治疗过程 irAE 的细胞介导机制； 2) 我们将确定是否可以使用 irAE 相关自身抗体或 TCR 在 ICI 治疗前进行识别； 3）我们将鉴定致病性 TCR 的抗原靶标并分析其特征在肿瘤和患病组织中表达。由于 ICI 取得了压倒性的成功，这些治疗方法将用于越来越多的患者，并转移到早期的治疗方案。因此，有 irAE 风险的患者数量将继续增加；该提案将解决如何识别和管理面临 ICI 严重不良后遗症风险的患者的需求日益增长，同时使确定 irAE 致病机制的新发现。