Stanford Cancer Immune Monitoring and Analysis Center (CIMAC)

斯坦福癌症免疫监测与分析中心 (CIMAC)

• 批准号: 10730465
• 负责人: Sean Curtis Bendall
• 金额: $ 183.61万
• 依托单位: STANFORD UNIVERSITY
• 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/10730465
• 关键词: ATAC-seq; Biological Assay; Biological Markers; Biometry; Cancer Center; Cancer Control; Cells; Clinical; Clinical Data; Clinical Trials; Collaborations; Correlative Study; Data; Data Commons; Databases; Elasticity; Ensure; Genomics; Grant; Guidelines; Image; Immune; Immunologic Monitoring; Immunology procedure; Immunotherapy; Lead; Machine Learning; Malignant Neoplasms; Measures; Mining; Mission; Outcome; Phenotype; Polysaccharides; Proteomics; Publishing; Quality Control; Regimen; Research; Sampling; Specific qualifier value; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Standardization; T cell receptor repertoire sequencing; Techniques; Technology; Testing; Work; cancer clinical trial; data submission; innovation; meetings; nano-string; novel; predictive marker; regression algorithm; transcriptome sequencing; transcriptomics; treatment response; working group; ATAC-seq; Biological Assay; Biological Markers; Biometry; Cancer Center; Cancer Control; Cells; Clinical; Clinical Data; Clinical Trials; Collaborations; Correlative Study; Data; Data Commons; Databases; Elasticity; Ensure; Genomics; Grant; Guidelines; Image; Immune; Immunologic Monitoring; Immunology procedure; Immunotherapy; Lead; Machine Learning; Malignant Neoplasms; Measures; Mining; Mission; Outcome; Phenotype; Polysaccharides; Proteomics; Publishing; Quality Control; Regimen; Research; Sampling; Specific qualifier value; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Standardization; T cell receptor repertoire sequencing; Techniques; Technology; Testing; Work; cancer clinical trial; data submission; innovation; meetings; nano-string; novel; predictive marker; regression algorithm; transcriptome sequencing; transcriptomics; treatment response; working group

Abstract For this CIMAC renewal, the Stanford Cancer Immune Monitoring and Analysis Center (CIMAC) will continue to collaborate with NCI and the CIMAC/CIDC network to identify and, where appropriate, lead correlative studies for trials testing novel immunotherapy regimens. We will participate in working group calls, network meetings, and coordination with clinical teams. The Stanford CIMAC performs highly comprehensive assays of immune phenotype and function for NCI-identified clinical trials. These will include already validated and harmonized Tier 1 assays, validated Tier 2 assays, and newly proposed exploratory Tier 3 assays. For Tier 1 assays, we propose CyTOF, singleplex IHC, Olink, TCRseq and RNAseq. For Tier 2, we propose single-cell TCRseq, MIBI, ATACseq, and CyTOF proteomics. For Tier 3, we propose single-cell glycan imaging (by MALDI-ToF), spatial transcriptomics (Nanostring DSP platform), and single-cell genomics/proteomics (Mission Bio Tapestri and BD Rhapsody platforms). We will use longitudinal reference materials for tracking inter-batch and inter-project consistency. We will assess quality control measures on all assays before uploading data to the Cancer Immune Data Commons (CIDC) according to their specifications. We will also perform biostatistical analysis of results for all assays performed, in relation to clinical outcome data. For those trials where Stanford is the lead CIMAC, we will perform integrative analysis across assays, using appropriate machine learning techniques and multivariate regression algorithms such as LASSO or Elastic Net. We will work closely with the clinical teams to obtain standardized clinical data, and to disseminate and publish results in accordance with NCI guidelines.

抽象的 对于CIMAC更新，斯坦福癌症免疫监测和分析中心 （CIMAC）将继续与NCI和CIMAC/CIDC网络合作，以识别和，，，， 在适当的情况下，用于测试新型免疫疗法方案的试验的铅相关研究。我们 将参加工作组电话，网络会议和与临床团队的协调。 斯坦福·西莫克（Stanford CIMAC）对免疫表型进行了高度全面的测定 NCI识别的临床试验的功能。这些将包括已经经过验证和协调的 第1层测定，经过验证的第2层测定和新提出的探索性层3分析。对于第1层 测定法，我们提出了Cytof，SinglePlex IHC，Olink，TCRSEQ和RNASEQ。对于第2层，我们 提出单细胞TCRSEQ，MIBI，ATACSEQ和CYTOF蛋白质组学。对于第3层，我们建议 单细胞聚糖成像（通过MALDI-TOF），空间转录组学（纳米弦DSP平台）， 和单细胞基因组/蛋白质组学（Mission Bio tapestri和BD Rhapsody平台）。我们将 使用纵向参考材料跟踪批处理间和项目间一致性。我们 将数据上传到癌症免疫之前，将评估所有测定法的质量控制措施 数据共享（CIDC）根据其规格。 我们还将对所有执行的测定结果进行生物统计分析，以 临床结果数据。对于那些斯坦福（Stanford）是首席CIMAC的试验，我们将表演 跨分析的集成分析，使用适当的机器学习技术和 多元回归算法，例如套索或弹性网。我们将与 临床团队获得标准化的临床数据，并传播和发布结果 根据NCI指南。