Cancer Immune Monitoring and Analysis Center

癌症免疫监测分析中心

• 批准号: 10730296
• 负责人: FRANK S HODI
• 金额: $ 197.39万
• 依托单位: DANA-FARBER CANCER INST
• 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/10730296
• 关键词: Acceleration; Address; Adoptive Cell Transfers; Allogenic; Antigens; Automobile Driving; Biological; Biological Assay; Biological Markers; Biology; Blood; Blood specimen; Cancer Therapy Evaluation Program; Cells; Clinical; Clinical Trials; Clone Cells; Collaborations; Communication; Complex; Coupled; Dana-Farber Cancer Institute; Data; Data Analyses; Data Set; Detection; Development; Epitopes; Evaluation; Fostering; Genomics; Goals; Immune; Immune response; Immunologic Monitoring; Immunooncology; Immunotherapeutic agent; Immunotherapy; Individual; Institution; Knowledge; Laboratories; Learning; Link; Longevity; Machine Learning; Malignant Neoplasms; Methods; Mission; Modality; Monitor; Motivation; Movement; Multiomic Data; Oncolytic viruses; Patients; Performance; Peripheral; Phase; Phenotype; Population; Principal Investigator; Process; Protocols documentation; Radiation therapy; Recording of previous events; Research; Resistance; Resources; Role; Series; Site; Specimen; Stains; Standardization; Stem cell transplant; T-Cell Immunologic Specificity; T-Cell Receptor; T-Lymphocyte; T-cell receptor repertoire; Testing; Therapeutic; Tissue Sample; Tissues; Toxic effect; United States National Institutes of Health; Vaccine Therapy; Validation; Variant; Work; Writing; bioinformatics tool; biomarker identification; cancer cell; cancer immunotherapy; cancer therapy; cancer type; chimeric antigen receptor T cells; computerized data processing; computerized tools; data integration; data standards; design; flexibility; high dimensionality; imaging modality; immune checkpoint blockade; immunotherapy clinical trials; immunotherapy trials; improved; innovation; inter-institutional; investigator-initiated trial; molecular marker; multiple omics; novel; novel strategies; novel therapeutics; patient subsets; personalized cancer therapy; process improvement; programs; response; response biomarker; single cell analysis; standard of care; success; therapy resistant; tool; tool development; transcriptomics; treatment response; tumor; tumor microenvironment; tumor-immune system interactions; wasting

Summary Because cancer immunotherapy constitutes one of the most transformative therapeutic movements over the history of cancer treatment, there are now hundreds, if not thousands, of ongoing clinical trials in this field. Despite the remarkable success for a subset of patients, there are substantial gaps in our knowledge. No more than 30% of patients respond to immune checkpoint blockade and related therapies, many responding patients develop therapeutic resistance, and many responding and non-responding patients suffer from immune-related toxicities. There is a clear need to identify critical biomarkers of response, resistance, and toxicity. To improve our ability to meet this challenge, the NCI-sponsored Cancer Immune Monitoring and Analysis Center (CIMAC) program was created to enable consistent and comparable interrogation of patient biospecimens across multiple clinical trials. The program has successfully established a common set of assays harmonized across the four CIMACs, augmented this basic set with more specialized tests that may be CIMAC-specific but are still accessible across institutions, deployed these tools across dozens of phase I/II immunotherapy trials, and generated a pool of standardized data to facilitate inter-trial comparisons. The CIMAC created at Dana-Farber Cancer Institute (DFCI) built on the existing Center of Immuno-Oncology (CIO) and the long-standing collaboration with the Broad Institute of MIT and Harvard. The DFCI/Broad CIMAC, joining with the other CIMACS, now proposes to continue the network mission to compare tumor and immune response features of individual cancer types treated by multiple immunotherapeutic modalities and multiple cancer types treated by a single immunotherapeutic modality in order to dissect common and individualized mechanisms of action. One driving motivation for this renewal application is that the power to identify biomarkers increases with the number of clinical trials analyzed. Thus, Aim 1 focuses on deploying the suite of assays established over the past five years to biospecimens collected from additional innovative immunotherapy clinical trials to standardize biomarker analysis and interpretation. To broaden the scope of parameters being examined, Aim 2 proposes to develop and verify performance for novel and more complex assays to expand the network capabilities and advance discovery. Of particular value will be methods that improve multiomic single cell profiling, multiomic spatial analysis, and immunopeptidome characterization. Aim 3 addresses the challenge of integrating multiomic data across an increasing number of clinical trials by building novel computational tools that can efficiently process massive amounts of data and generate interpretable representations of the results. Altogether, these efforts will support the role of immuno-oncology in the personalized treatment of cancer because only by the analyses of large well-annotated datasets can we identify links between biology, molecular markers and clinical response.

总结 因为癌症免疫疗法构成了最具变革性的治疗运动之一， 癌症治疗的历史，现在有数百个，如果不是数千个，在这个领域正在进行的临床试验。 尽管在一部分患者中取得了显著的成功，但我们的知识仍有很大的差距。不再 超过30%的患者对免疫检查点阻断和相关疗法有反应，许多有反应的患者 产生治疗耐药性，许多有反应和无反应的患者患有免疫相关的 毒性明确需要鉴定反应、抗性和毒性的关键生物标志物。提高 我们有能力迎接这一挑战，NCI-sponsored癌症免疫监测和分析中心（CIMAC） 创建了一个程序，以实现对多个患者生物标本的一致和可比询问 临床试验该计划已成功地建立了一套通用的检测方法， CIMAC，增加了这个基本的一套更专门的测试，可能是CIMAC特定的，但仍然是 跨机构访问，在数十个I/II期免疫治疗试验中部署这些工具， 生成了一个标准化数据库，以便于试验间的比较。在达纳法伯创建的CIMAC 癌症研究所（DFCI）建立在现有的免疫肿瘤学中心（CIO）和长期的 与麻省理工学院布罗德研究所和哈佛大学的合作。DFCI/Broad CIMAC，与其他 CIMACS，现在建议继续网络使命，比较肿瘤和免疫反应的特点， 通过多种免疫疗法治疗的单个癌症类型和通过免疫疗法治疗的多种癌症类型。 单一的免疫模式，以剖析共同的和个性化的作用机制。一 这种更新应用的驱动动机是，识别生物标志物的能力随着数量的增加而增加。 分析的临床试验。因此，目标1侧重于部署在过去五年中建立的检测套件 从其他创新免疫治疗临床试验中收集的生物标本，以标准化生物标志物 分析和解释。为了扩大所审查参数的范围，目标2建议制定 并验证新的和更复杂的分析的性能，以扩展网络功能， 的发现特别有价值的将是改善多组单细胞谱分析、多组空间分析和多组空间分析的方法。 分析和免疫肽组表征。目标3解决了整合多组学数据的挑战 通过构建新型计算工具， 大量的数据，并生成结果的可解释的表示。这些努力将 支持免疫肿瘤学在癌症个性化治疗中的作用，因为只有通过分析 大的注释良好的数据集可以识别生物学、分子标记和临床反应之间的联系。

项目成果

Mass cytometry staining for human bone marrow clinical samples.

• DOI: 10.1016/j.xpro.2022.101163
• 发表时间: 2022-03-18
• 期刊: STAR protocols
• 作者: Hallisey M;Dennis J;Abrecht C;Pistofidis RS;Schork AN;Lightbody ED;Heilpern-Mallory D;Severgnini M;Ghobrial IM;Hodi FS;Baginska J
• 通讯作者: Baginska J

Simplified mass cytometry protocol for in-plate staining, barcoding, and cryopreservation of human PBMC samples in clinical trials.

在临床试验中，用于人类PBMC样品的板内染色，条形码和冷冻保存的简化质量细胞仪方案。

• DOI: 10.1016/j.xpro.2022.101362
• 发表时间: 2022-06-17
• 期刊: STAR PROTOCOLS
• 作者: Abrecht, Charlotte;Hallisey, Margaret;Dennis, Jenna;Nazzaro, Matthew;Brainard, Martha;Hathaway, Emma;Schork, Abigail N.;Hodi, Stephen;Severgnini, Mariano;Baginska, Joanna
• 通讯作者: Baginska, Joanna