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

• 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
• 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

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）：随着肿瘤的生长和演变，肿瘤细胞、间质细胞和免疫细胞发生变化，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，因为一些细胞可能是高优先级靶点（即癌症干细胞），而其他细胞可能天生对治疗具有抗性（即缺氧细胞）。肿瘤内表型异质性只能使用单细胞方法（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。最近引入的称为“质量细胞术”的非光学细胞术技术能够测量每个细胞多达45个基于抗体的参数。甚至与先进的15参数流式细胞术相比，质谱流式细胞术可以区分109倍以上的细胞表型。在我们的原理验证实验中，我们已经开始使用这种技术来定义癌症系统的细胞分化和细胞内信号传导。在本申请中，我们详细介绍了一个强大的管道，用于通过质谱流式细胞术对大量实体肿瘤进行系统研究，包括优化的样品制备和先进的数据分析技术。我们将对一组具有良好特征的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，我们预测通过质谱细胞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）开发用于表征胶质母细胞瘤样本的瘤内表型异质性的优化组织制备方法和质谱仪试剂;（2）使用同行评议的基因表达谱作为金标准，鉴定预测主要肿瘤亚群的单细胞特征;（3）利用单细胞团细胞计数数据和基因表达数据的互补性质，以产生预测患者对治疗的反应的集成系统水平特征。这些目标的成功完成将立即影响我们对胶质母细胞瘤的理解，揭示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谱细胞术，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，并前瞻性地对患者进行分层，以进行精准医学。