Finding the optimal balance of immunotherapy efficacy and toxicity.

寻找免疫治疗功效和毒性的最佳平衡。

• 批准号: 10376977
• 负责人: David Eric Gerber
• 金额: $ 4.02万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-12 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10376977
• 关键词: Address; Autoantibodies; Autoimmune; Autoimmune Diseases; Autoimmunity; Bioinformatics; Biological Assay; Biological Markers; Biology; Blood specimen; Cancer Patient; Cellular Immunity; Clinical; Clinical Data; Clinical Trials; Collaborations; Combination immunotherapy; Complex; Correlative Study; Cytometry; Cytotoxic T-Lymphocytes; DNA Resequencing; Data; Data Analyses; Data Set; Databases; Development; Disease; Equilibrium; Event; Exclusion; Funding Mechanisms; Future; Genetic; Goals; Immune; Immune Tolerance; Immune checkpoint inhibitor; Immune response; Immune system; Immunology; Immunotherapy; Incidence; Inflammatory; Institute of Medicine (U.S.); Interdisciplinary Study; Laboratories; Molecular; Monitor; Musculoskeletal; Organ; Outcome; Pathway interactions; Patient Selection; Patients; Pattern; Peripheral Blood Mononuclear Cell; Phenotype; Population; Protocols documentation; Public Health; Publishing; Recommendation; Recording of previous events; Regimen; Reporting; Research; Research Personnel; Resources; SNP array; Sampling; Selection for Treatments; Severities; T cell receptor repertoire sequencing; T-Lymphocyte Subsets; Testing; Time; Toxic effect; Treatment Efficacy; Tumor Biology; Validation; Variant; adjudicate; assay development; base; biomarker panel; cancer immunotherapy; cancer therapy; checkpoint therapy; chemokine; chemotherapy; clinical application; clinical efficacy; clinically significant; cohort; cytokine; experience; immune-related adverse events; immunological diversity; immunoregulation; insight; molecular targeted therapies; multidisciplinary; single-cell RNA sequencing; tumor; tumor immunology

U01 Abstract Despite extensive research into cancer immunotherapy, immune-related adverse events (irAE) remain a critical and poorly understood issue. To address this critical need, we have assembled a multidisciplinary research team with broad and relevant expertise. The co-PIs of this proposal have expertise in cancer immunotherapy, immunology, assay development, and bioinformatics. Together, we have assembled a cohort of ~400 cancer patients treated with ICI, collecting longitudinal treatment, efficacy, and toxicity data, as well as blood samples at pre-treatment baseline, throughout therapy, and at time of toxicity. In our real-world data set, over 10 percent of cases have a history of autoimmune disease, providing insight into use of ICI in a population widely excluded from clinical trials yet routinely treated with these therapies off protocol. Our high-quality clinical data annotation—without which correlative studies have little meaning—addresses the reality that irAE may occur months after ICI initiation and are far more complex to detect and characterize than toxicities of conventional chemotherapy or molecularly targeted therapies. Through existing funding mechanisms, we have already completed autoantibody, cytokine, genetic, and functional assays in these cases. However, we do not currently have resources for comprehensive, integrated analysis of these diverse laboratory and clinical data. The overarching goal of this U01 proposal is to determine the optimal balance between ICI efficacy and toxicity, ultimately identifying a set of biomarkers useful for selection of patients, treatment type and duration, and clinical monitoring. We will achieve this through determination of cellular immunity, comprehensive data analysis, and clinical validation. We have three Aims: (1) Determine cellular immunity in patients experiencing irAE and/or achieving beneficial responses from ICI. We will perform mass cytometry (CyTOF) and T-cell receptor sequencing at multiple time-points. (2) Determine genetic, humoral, and cellular factors associated with irAE and/or beneficial responses from ICI. We will develop a database to integrate and analyze the CyTOF and T-cell receptor sequencing data with clinical efficacy and toxicity data, as well as data from the assays already completed through other mechanisms. (3) Perform analytical and clinical validation of emerging biomarkers. We will apply the best classifying phenotypes emerging from our comprehensive and integrated data analysis to a test set of patients from our existing cohort, eventually identifying a subset of biomarkers with potential for clinical application. Together, these Aims directly address the FOA purpose of reducing the incidence and/or severity of irAE while retaining anti- tumor efficacy.

U01摘要 尽管对癌症免疫治疗进行了广泛的研究，但与免疫相关的不良事件(IRAE) 仍然是一个关键的问题，人们对此知之甚少。为了满足这一迫切需求，我们已经组建了一个 具有广泛和相关专业知识的多学科研究团队。这项提案的共同绩效指标有 在癌症免疫治疗、免疫学、分析开发和生物信息学方面的专业知识。在一起，我们 汇集了约400名接受ICI治疗的癌症患者，收集了纵向治疗，疗效， 和毒性数据，以及治疗前基线、整个治疗过程和治疗时的血液样本 毒性。在我们的真实数据集中，超过10%的病例有自身免疫性疾病的病史， 为广泛排除在临床试验但常规治疗的人群中使用ICI提供洞察力 这些疗法都不符合常规。我们的高质量临床数据注释-没有哪些相关研究 几乎没有意义-解决了这样一个现实，即IRE可能在ICI开始几个月后发生，而且远远超过 检测和表征比传统化疗或分子靶向毒性更复杂的药物 治疗。通过现有的资助机制，我们已经完成了自身抗体，细胞因子， 在这些情况下进行遗传和功能分析。然而，我们目前没有资源用于 对这些不同的实验室和临床数据进行全面、综合的分析。的首要目标是 这份U01提案旨在最终确定ICI疗效和毒性之间的最佳平衡 确定一组对选择患者、治疗类型和持续时间有用的生物标志物，以及 临床监测。我们将通过测定细胞免疫、综合数据来实现这一点 分析和临床验证。我们有三个目标：(1)测定患者的细胞免疫 体验IRAE和/或从ICI获得有益的回应。我们将进行质量细胞分析 (CyTOF)和多个时间点的T细胞受体测序。(2)确定遗传、体液和 与IRAE相关的细胞因子和/或ICI的有益反应。我们将开发一个数据库 综合分析细胞毒副反应和T细胞受体测序数据与临床疗效和毒性 数据，以及已经通过其他机制完成的化验数据。(3)进行分析 以及新兴生物标记物的临床验证。我们将应用出现的最好的分类表型 从我们全面和综合的数据分析到我们现有队列中的一组测试患者， 最终确定具有临床应用潜力的生物标志物的子集。总而言之，这些目标 直接解决FOA的目的，即降低IRE的发生率和/或严重性，同时保持反 肿瘤疗效。