Integrating mass cytometric and transcriptomic profiles of solid tumors

整合实体瘤的质谱流式细胞术和转录组学特征

基本信息

批准号：
9060270
负责人：
WILLIAM A WEISS
金额：
$ 38.38万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-05-01 至 2017-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9060270
关键词：
Antibodies Basic Cancer Research Biological Biological Assay Biological Markers Blinded Cells Clinical Clinical Trials Complex Cytometry Data Data Analyses Development Discrimination Disease Disease model Dissociation Drug Targeting Epigenetic Process Evaluation Excision Flow Cytometry Gene Expression Gene Expression Profile Genetic Genetic Programming Glean Glioblastoma Gold Growth Health Heterogeneity Human Hypoxia Immune Investigation Malignant Neoplasms Malignant neoplasm of ovary Measurement Measures Methodology Methods Microscopy Molecular Molecular Profiling Multivariate Analysis Mus Myelin Nature Neoplasm Metastasis Optics Patients Peer Review Pharmacotherapy Phenotype Preparation Process Protocols documentation Publishing Reagent Reproducibility Research Research Personnel Sampling Signal Transduction Solid Neoplasm Stimulus Stromal Neoplasm Surface System Techniques Technology Testing Time Training Tumor Biology Validation Work Xenograft procedure anticancer research base cancer stem cell cancer therapy cell behavior cell type chemotherapy clinical biomarkers clinical care clinically relevant cohort computerized tools cost design detector improved innovation innovative technologies insight laboratory development leukemia neoplastic cell new therapeutic target next generation non-genetic novel patient stratification personalized medicine phenotypic biomarker precision medicine profiles in patients radiation response research study response sample fixation single cell analysis subcutaneous success targeted treatment technology validation therapy resistant tissue preparation tool transcriptome transcriptomics treatment planning tumor tumor heterogeneity

项目摘要

DESCRIPTION (provided by applicant): As tumors grow and evolve, tumor, stromal, and immune cells change, displaying new and variable phenotypes as a result of both genetic and non-genetic influences. This intratumoral phenotypic heterogeneity is an important consideration for cancer therapy, as some cells may be high-priority targets (i.e. cancer stem cells), and others may be innately resistant to therapy (i.e. hypoxic cells). Intratumoral phenotypic heterogeneity can only be studied using single-cell approaches such as microscopy or flow cytometry, but these are limited by the amount of information that can be assessed per cell using optical detectors. A recently introduced non-optical cytometry technology, termed "mass cytometry", enables the measurement of up to 45 antibody-based parameters per cell. Compared even to advanced 15-parameter flow cytometry, mass cytometry can potentially differentiate 109-fold more cellular phenotypes. In our proof-of-principle experiments, we have begun using this technology to define the cellular differentiation and intracellular signaling of cancer systems. In this application, we detail a robust pipeline for the systematic investigation o a large number of solid tumors by mass cytometry, including optimized sample preparation and advanced data analysis techniques. We will perform these studies on a cohort of human glioblastoma tumors with well-characterized gene expression profiles. Gene expression data represents the average genetic program of many cells in the tumor, and we predict that the rich layer of single-cell data obtained by mass cytometry will provide complementary information. We will pursue the following Specific Aims: (1) to develop optimized tissue preparation methods and mass cytometry reagents for characterizing the intratumoral phenotypic heterogeneity of glioblastoma samples; (2) to identify single-cell signatures that predict major tumor subsets, using peer-reviewed gene expression profiles as a gold standard; (3) to leverage the complementary nature of single-cell mass cytometry data and gene expression data to yield an integrated systems-level signature that predicts the patient's response to therapy. Successful completion of these aims will immediately impact our understanding of glioblastoma by revealing tumor cell subpopulations that contribute to the molecular and clinical differences between patients. Once mass cytometry is validated in a cancer research setting, the proposed analysis workflow can be used for studying fundamental tumor biology, discovering novel drug targets, and prospectively stratifying patients for precision medicine.

描述（由申请人提供）：随着肿瘤的生长和进化，肿瘤、基质和免疫细胞发生变化，由于遗传和非遗传影响而表现出新的和可变的表型。这种肿瘤内表型异质性是癌症治疗的一个重要考虑因素，因为一些细胞可能是高优先级靶标（即癌症干细胞），而另一些细胞可能天生对治疗有抵抗力（即缺氧细胞）。肿瘤内表型异质性只能使用单细胞方法（例如显微镜或流式细胞术）进行研究，但这些方法受到使用光学检测器可以评估每个细胞的信息量的限制。最近推出的非光学细胞计数技术，称为“质量细胞计数”，可以测量每个细胞多达 45 个基于抗体的参数。与先进的 15 参数流式细胞术相比，质谱细胞术可以区分多 109 倍的细胞表型。在我们的原理验证实验中，我们已经开始使用这项技术来定义癌症系统的细胞分化和细胞内信号传导。在此应用中，我们详细介绍了通过质谱流式细胞仪对大量实体瘤进行系统研究的强大流程，包括优化的样品制备和先进的数据分析技术。我们将对一组具有明确基因表达谱的人类胶质母细胞瘤肿瘤进行这些研究。基因表达数据代表了肿瘤中许多细胞的平均遗传程序，我们预测通过质谱流式技术获得的丰富的单细胞数据层将提供补充信息。我们将追求以下具体目标：（1）开发优化的组织制备方法和质量流式细胞仪试剂，用于表征胶质母细胞瘤样品的瘤内表型异质性； (2) 使用同行评审的基因表达谱作为黄金标准，识别预测主要肿瘤亚群的单细胞特征； (3) 利用单细胞质谱流式数据和基因表达数据的互补性来产生集成的系统级特征，预测患者对治疗的反应。这些目标的成功完成将通过揭示导致患者之间分子和临床差异的肿瘤细胞亚群，立即影响我们对胶质母细胞瘤的理解。一旦质谱流式分析在癌症研究环境中得到验证，所提出的分析工作流程可用于研究基础肿瘤生物学、发现新的药物靶点以及前瞻性地对患者进行分层以实现精准医疗。