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：
8664234
负责人：
WILLIAM A WEISS
金额：
$ 40.87万
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/8664234
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 Factor Heterogeneity Human Hypoxia Immune Investigation Laboratories Malignant Neoplasms Malignant neoplasm of ovary Measurement Measures Medicine Methodology Methods Microscopy Molecular Molecular Profiling Multivariate Analysis Mus Myelin Nature Neoplasm Metastasis Optics Patients Peer Review Pharmaceutical Preparations Phenotype Preparation Process Protocols documentation Publishing Radiation Reagent Reproducibility Research Research Personnel Resistance Sampling Signal Transduction Solid Neoplasm Stimulus Stratification Stromal Neoplasm Surface System Techniques Technology Testing Therapeutic Time Training Tumor Biology Validation Work Xenograft procedure anticancer research base cancer stem cell cancer therapy cell behavior cell type chemotherapy clinical care clinically relevant cohort computerized tools cost design detector improved innovation innovative technologies insight leukemia neoplastic cell next generation non-genetic novel public health relevance research study response sample fixation single cell analysis subcutaneous success technology validation therapy resistant tissue preparation tool transcriptomics treatment planning tumor

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，基质和免疫细胞的变化，由于遗传和非遗传影响而显示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重要考虑因素，因为某些细胞可能是高优先级靶标（即癌症干细胞），而其他细胞可能天生对治疗（即低氧细胞）具有抗性。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仪）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。最近引入的一种称为“质量细胞术”的非光学细胞仪技术，可以测量每个细胞的45个基于45个抗体的参数。甚至与先进的15参数流式细胞仪相比，质量细胞仪可能会区分109倍的细胞表型。在我们的原则实验中，我们开始使用该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量细胞仪（包括优化的样品制备和高级数据分析技术）进行系统研究的强大管道。我们将对具有特征良好的基因表达谱的人类胶质母细胞瘤肿瘤的队列进行这些研究。基因表达数据代表肿瘤中许多细胞的平均遗传程序，我们预测，通过质量细胞术获得的单细胞数据的丰富层将提供互补信息。我们将追求以下特定目的：（1）开发优化的组织制备方法和质量细胞仪试剂，以表征胶质母细胞瘤样品的肿瘤内表型异质性；（2）使用同行评审的基因表达曲线作为金标准，以识别预测主要肿瘤子集的单细胞特征；（3）利用单细胞质量细胞仪数据和基因表达数据的互补性质，以产生一个集成的系统级特征，以预测患者对治疗的反应。这些目标的成功完成将立即通过揭示肿瘤细胞亚群来影响我们对胶质母细胞瘤的理解，这些肿瘤细胞亚群有助于患者之间的分子和临床差异。一旦在癌症研究环境中验证了质量细胞仪，就可以使用拟议的分析工作流程来研究基本肿瘤生物学，发现新的药物靶标，并前瞻性地对患者进行精密医学分类。