Integrating mass cytometric and transcriptomic profiles of solid tumors

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一个重要考虑因素，因为一些细胞可能是高优先目标（如癌症干细胞），而另一些细胞可能天生对治疗有抗性（如缺氧细胞）。肿瘤内表型异质性只能通过单细胞方法进行研究，如显微镜或流式细胞术，但这些方法受到光学检测器可以评估每个细胞的信息量的限制。最近引入的非光学细胞术技术，称为“质量细胞术”，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，质量细胞术可以潜在地区分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项技术来定义癌症系统的细胞分化和细胞内信号。在这个应用中，我们详细介绍了一个强大的管道，用于大量实体肿瘤的系统研究，包括优化的样品制备和先进的数据分析技术。我们将在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中进行这些研究。基因表达数据代表了肿瘤中许多细胞的平均遗传程序，我们预测通过细胞计数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大量细胞计数试剂来表征胶质母细胞瘤样本的瘤内表型异质性；(2)利用同行评审的基因表达谱作为金标准，识别预测主要肿瘤亚群的单细胞特征；(3)利用单细胞细胞计数数据和基因表达数据的互补性，产生一个综合的系统级信号，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。一旦大规模细胞术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，并有望为精准医疗对患者进行分层。

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.