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: 10418693
• 负责人: David Eric Gerber
• 金额: $ 57.47万
• 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/10418693
• 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 尽管对癌症免疫疗法，免疫相关广告事件（IRAE）进行了广泛研究 仍然是一个关键且理解不善的问题。为了满足这一关键需求，我们组装了 具有广泛和相关专业知识的多学科研究团队。该提议的共同点有 癌症免疫疗法，免疫学，测定开发和生物信息学方面的专业知识。在一起，我们 已经组装了约400名接受ICI治疗的癌症患者的队列，收集纵向治疗，有效性， 和毒性数据，以及在治疗前基线，整个治疗过程中的血液样本，以及 毒性。在我们的实际数据集中，超过10％的病例有自身免疫性疾病的病史， 提供有关在临床试验中被广泛排除但经常治疗的人群中使用ICI的见解 这些疗法违反了方案。我们的高质量临床数据注释 - 没有相关研究 几乎没有意义 - 大调是IRAE可能发生在ICI倡议之后几个月的现实，并且要多得多 复杂的检测和表征与常规化疗或分子靶向的毒性相比 疗法。通过现有的资金机制，我们已经完成了自身抗体，Cytokine， 在这些情况下，遗传和功能分析。但是，我们目前没有资源 这些潜水员实验室和临床数据的全面，综合分析。总体目标 该U01建议是确定ICI效率和毒性之间的最佳平衡，最终 确定一组可用于选择患者，治疗类型和持续时间的生物标志物以及 临床监测。我们将通过确定细胞免疫，全面数据来实现这一目标 分析和临床验证。我们有三个目标：（1）确定患者的细胞免疫学 经历IRAE和/或获得ICI的有益反应。我们将执行质量细胞术 （Cytof）和T细胞受体测序在多个时间点处进行。 （2）确定遗传，体液和 与IRAE和/或ICI的有益反应相关的细胞因子。我们将开发一个数据库 将细胞和T细胞受体测序数据与临床效率和毒性进行整合和分析 数据以及已经通过其他机制完成的测定的数据。 （3）进行分析 以及新兴生物标志物的临床验证。我们将采用最佳的分类表型 从我们的全面和集成的数据分析到我们现有队列的一组患者的测试集， 最终确定具有临床应用潜力的生物标志物的子集。这些目标在一起 直接解决了降低IRAE的事件和/或严重程度的FOA目的 肿瘤效率。