Characterization of high-grade serous ovarian cancer subtypes via single-cell profiling

通过单细胞分析表征高级别浆液性卵巢癌亚型

• 批准号: 10407165
• 负责人: Jennifer A. Doherty
• 金额: $ 59.86万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10407165
• 关键词: Antibodies; Bar Codes; Biological; Cancer Etiology; Cell Fraction; Cell Surface Proteins; Cell surface; Cells; Cessation of life; Data; Data Set; Disease; Epithelial ovarian cancer; Gene Expression; Gene Expression Profiling; Genes; Immune; Immunohistochemistry; Immunotherapy; Individual; Knowledge; Letters; Link; Malignant Neoplasms; Malignant neoplasm of ovary; Measures; Membrane Proteins; Mission; Molecular; Pathway interactions; Pattern; Population; Population Analysis; Proteins; Public Health; Publishing; RNA; Reporting; Research; Research Personnel; Resolution; Sampling; Serous; Solid Neoplasm; Standardization; Stromal Cells; Survival Analysis; Techniques; Testing; Time; Transcript; Tumor Biology; Variant; Woman; Work; Xenograft procedure; base; cancer cell; cancer gene expression; cancer subtypes; cell type; cohort; disorder subtype; experimental study; genome-wide; improved; inclusion criteria; molecular subtypes; new technology; patient derived xenograft model; primary outcome; single-cell RNA sequencing; survival prediction; transcriptome; transcriptome sequencing; transcriptomics; translational impact; treatment strategy; tumor

High-grade serous ovarian cancer (HGSOC) subtypes have been identified across multiple studies; however, the biologic basis of these subtypes remains poorly understood. The central hypothesis of this proposal is that differences in the cellular composition of HGSOC tumors drives the expression patterns that characterize at least some of the previously described HGSOC subtypes. New technologies that barcode antibodies and transcripts from individual cells before sequencing can characterize gene expression at single cell resolution and detect cell types, which allows the central hypothesis to be directly tested. These combined advances lay the groundwork to identify the basis, in terms of cell composition and pathway expression, of HGSOC subtypes through two aims. Aim 1: Characterize transcriptomes and selected proteins at single cell resolution, and deconvolve existing tumor gene expression data. Single cell RNA and surface protein abundances will be measured at the single cell level for high-grade serous ovarian cancers, unsupervised analysis will be used to identify cell populations, cell surface proteins will be analyzed to characterize the immune compartment, cell-type marker genes will be defined, and marker genes will be used deconvolve matched bulk RNA-seq samples. This will allow existing data from larger cohorts to be deconvolved allowing survival analyses to be performed on tumors stratified by cell composition. Aim 2: Characterize the transcriptomic profile of cancer cells within HGSOC tumors to identify pathways that are variably expressed within cancer cells. Gene expression within cancer cells will be measured using two complementary approaches: (i) orthotopic patient derived xenografts (PDXs) and (ii) single cell RNAseq. For each pathway, an enrichment score will be generated and pathways with expression levels that vary substantially across the cohort will be identified. Combining the expression levels of genes within variable pathways with cancer cell fraction estimates from existing datasets will enable inference of the extent to which these variable pathways differ between reported subtypes after controlling for cancer cell abundances. The proposal is expected to result in two primary outcomes: 1) an understanding of the extent to which cell composition and pathway expression contribute to HGSOC gene expression subtypes; and 2) estimates of the proportions of cell types in existing studies with public gene expression data. A short-term impact is expected through improved survival predictors of HGSOC subtypes based on variation identified from cell composition and pathway expression and the work is expected to be impactful in the longer-term because determining the biologic basis of subtypes is a key step towards developing treatments that target their specific vulnerabilities.

已经通过多项研究确定了高级别浆液性卵巢癌(HGSOC)亚型；然而， 这些亚型的生物学基础仍然知之甚少。这项提议的中心假设是 HGSOC肿瘤细胞组成的差异决定了其表达模式的特点 至少一些前面描述的HGSOC子类型。条形码抗体和新技术 测序前单个细胞的转录本可以在单细胞分辨率下表征基因表达 并检测细胞类型，这使得中心假设可以直接得到检验。这些结合在一起的进步 根据细胞组成和途径表达确定HGSOC亚型基础的基础工作 通过两个目标。 目标1：在单细胞分辨率下表征转录本和选定的蛋白质，并去卷积 现有的肿瘤基因表达数据。单细胞RNA和表面蛋白丰度将在 对于单细胞水平的高级别浆液性卵巢癌，将使用非监督分析来鉴定细胞 群体、细胞表面蛋白的分析将表征免疫隔间、细胞类型的标志物 基因将被定义，标记基因将被用于去卷积匹配的批量RNA-SEQ样本。这将是 允许对来自更大队列的现有数据进行去卷积，从而允许对肿瘤执行生存分析 按细胞组成分层。 目的2：确定HGSOC肿瘤内癌细胞的转录图谱以确定 在癌细胞内可变表达的通路。癌细胞内的基因表达将是 使用两种互补的方法测量：(I)原位患者来源的异种移植物(PDX)和(Ii) 单细胞RNAseq.对于每条途径，都会生成一个富集度分数，并且这些途径都有表达 将确定在队列中差异很大的水平。结合基因的表达水平 在可变路径内，根据现有数据集估计的癌细胞比例将能够推断 在控制癌细胞后，这些可变通路在已报道的亚型之间的差异程度 富足。 该提案预计将产生两个主要结果：1)了解细胞 组成和途径表达有助于HGSOC基因表达亚型；以及2)估计 现有研究中公开的基因表达数据中细胞类型的比例。预计会产生短期影响 通过改进基于细胞组成变异的HGSOC亚型生存预测因子 和途径的表达，这项工作预计将在较长期内产生影响，因为确定 亚型的生物学基础是开发针对其特定脆弱性的治疗方法的关键一步。