High-Dimensional Immune Monitoring of NCI-Supported Immunotherapy Trials

NCI 支持的免疫治疗试验的高维免疫监测

• 批准号: 10730469
• 负责人: Sacha Gnjatic
• 金额: $ 198.36万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10730469
• 关键词: 16S ribosomal RNA sequencing; Acceleration; Achievement; Administrator; Adverse event; Agreement; Antigens; Architecture; Biological Assay; Biological Markers; Biometry; Blood; Cancer Center; Cell Therapy; Cells; Characteristics; Clinical; Clinical Trials; Collaborations; Communities; Complex; Correlative Study; Dana-Farber Cancer Institute; Data; Data Commons; Data Scientist; Data Set; Dedications; Ensure; FDA approved; Fruit; Funding; Genetic; Goals; Heart; Hematologic Neoplasms; Human; Human Resources; Immune; Immunologic Monitoring; Immunologics; Immunologist; Immunooncology; Immunotherapy; Institution; Joints; Knowledge; Laboratories; Leadership; Malignant Neoplasms; Manuscripts; Methodology; Mission; Modality; Modeling; Molecular; Molecular Profiling; Mutation; Outcome; Output; Pathologist; Pathology; Pathway interactions; Patients; Phenotype; Play; Postdoctoral Fellow; Practice Guidelines; Procedures; Process; Prognostic Marker; Proteomics; Protocols documentation; Recommendation; Reproducibility; Research; Resistance; Resources; Sampling; Scientist; Serum; Solid Neoplasm; Specialist; Specimen; Standardization; T cell receptor repertoire sequencing; Technology; Thinking; Time; Tissues; Tumor Tissue; Universities; Viral; Work; biomarker discovery; cancer clinical trial; cancer immunotherapy; cancer therapy; candidate marker; clinically actionable; complex data; cost; data integration; data repository; data sharing; demographics; design; early phase clinical trial; empowerment; exceptional responders; expectation; fitness; high dimensionality; immune checkpoint; immune checkpoint blockade; immunotherapy clinical trials; immunotherapy trials; improved; innovation; interest; machine learning method; medical schools; microbiome; multidimensional data; multiple omics; new therapeutic target; next generation; novel; novel marker; pharmacodynamic biomarker; predictive marker; programmed cell death ligand 1; programs; repository; response; response biomarker; sample collection; technology platform; transcriptome sequencing; transcriptomics; tumor; tumor-immune system interactions

SUMMARY Defining who is likely to respond to cancer immunotherapy and who may not benefit, due to primary/secondary resistance or adverse events, is of critical interest for improving immuno-oncology (IO) treatments. Most clinical trials of novel combinations either fail to include any support for complex correlative biomarker discovery, often due to costs of specimen collection and assays, or are performed in an idiosyncratic manner, making non- standardized data difficult to integrate as part of a larger repository. There is a need to go beyond just PD-L1 tissue expression and tumor mutation burden as FDA-approved yet imperfect predictors of immune checkpoint blockade, by generating high-dimensional datasets that characterize the tumor immune microenvironment, systemic circulating immune and proteomic markers from blood, and host-level immune fitness information (microbiome). The Cancer Immune Monitoring and Analysis Centers, Cancer Immunologic Data Commons, and Partnership for Accelerating Cancer Therapies (CIMAC-CIDC-PACT) is a Cancer Moonshot-funded network that brings together laboratory, clinical, and computational scientists, administrators, and regulatory specialists from four institutions, Icahn School of Medicine at Mount Sinai (ISMMS), MD Anderson Cancer Center (MDACC), Dana-Farber Cancer Institute (DFCI), and Stanford University. Our unprecedented, coordinated effort to conduct IO clinical trial correlative studies and define new candidate biomarkers to improve immunotherapy had resulted in 37 clinical trials selected in the first funding period, with ongoing datasets resulted for 17 of them. This was achieved using rigorously assessed SOPs and processes from trial selection to data sharing. We propose to continue and expand these efforts as follows: A set of well-established, harmonized, high-dimensional tumor and blood-based assays will be applied to all newly selected trials, with the aim of evaluating and validating novel biomarker of treatment, clinical response, and/or adverse events (Aim 1). To spur innovation and increase granularity of our understanding of mechanism of action, high-tech yet validated single cell and spatial transcriptomic technologies as well as other omics will be proposed in exceptional patients for unbiased phenotyping, tissue composition, architectural organization, and immune profiling (Aim 2). The resulting unified data repository will be mined to define multi-omic and cross-trial markers, through biostatistical and computational innovative design to integrate datasets from various assays into higher analytical archetypes across studies (Aim 3). Together, our network is at the forefront of immune monitoring standards, by balancing innovation with reproducibility, to comprehensively assess biomarkers and strive for precision cancer treatment. We are poised to create an unparalleled resource of harmonized, clinically annotated datasets shared with the public that will establish best practice guidelines with the research community. Through rigorous implementation of state-of-the-art, cross-compared platforms, the CIMAC-CIDC initiative aims to accelerate identification of clinically actionable biomarkers of response, resistance, and adverse events, and define mechanisms at play.

总结 定义谁可能对癌症免疫治疗有反应，谁可能不会受益，由于原发性/继发性 耐药性或不良事件，对于改善免疫肿瘤学（IO）治疗至关重要。大多数临床 新组合的试验要么不能包括对复杂相关生物标志物发现的任何支持， 由于样本采集和测定的成本，或者以特殊的方式进行，使得非 标准化的数据难以集成为较大存储库的一部分。需要超越PD-L1 组织表达和肿瘤突变负荷作为FDA批准但不完善的免疫检查点预测指标 阻断，通过生成表征肿瘤免疫微环境的高维数据集， 来自血液的全身循环免疫和蛋白质组学标志物，以及宿主水平的免疫适应性信息 （微生物组）。癌症免疫监测和分析中心，癌症免疫学数据共享， 加速癌症治疗伙伴关系（CIMAC-CIDC-PACT）是一个由癌症登月计划资助的网络， 汇集了实验室，临床和计算科学家，管理人员和监管专家， 四个机构，西奈山伊坎医学院（ISMMS），MD安德森癌症中心（MDACC）， Dana-Farber癌症研究所（DFCI）和斯坦福大学。我们史无前例的协调努力 IO临床试验相关研究和定义新的候选生物标志物，以改善免疫治疗， 在第一个资助期选择的37项临床试验中，其中17项的数据集正在进行中。这是 通过严格评估的SOP和从试验选择到数据共享的流程实现。我们建议 继续并扩大这些努力如下：一套完善的，协调的，高维度的肿瘤和 基于血液的检测将应用于所有新选择的试验，目的是评估和验证新的 治疗、临床反应和/或不良事件的生物标志物（目的1）。鼓励创新， 我们对作用机制的理解的粒度，高科技但经过验证的单细胞和空间 转录组学技术以及其他组学将在特殊患者中提出， 表型、组织组成、结构组织和免疫谱分析（目标2）。结果统一 将通过生物统计学和 计算创新设计，将来自各种测定的数据集整合到更高的分析原型中 跨研究（目标3）。总之，我们的网络是在免疫监测标准的最前沿，通过平衡 创新与可重复性，全面评估生物标志物，并致力于精确的癌症治疗。 我们准备创建一个无与伦比的协调，临床注释的数据集资源，与 公众将与研究界建立最佳实践指南。通过严格执行 CIMAC-CIDC倡议旨在加速识别最先进的交叉比较平台， 临床上可行的反应、耐药性和不良事件的生物标志物，并定义起作用的机制。