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.

高级别浆液性卵巢癌（HGSOC）亚型已在多项研究中确定;然而， 这些亚型的生物学基础仍然知之甚少。该提案的中心假设是， HGSOC肿瘤细胞组成的差异驱动了表达模式， 至少一些先前描述的HGSOC亚型。新技术，条码抗体和 在测序之前来自单个细胞的转录物可以以单细胞分辨率表征基因表达 并检测细胞类型，这使得中心假设可以直接测试。这些综合进步奠定了 在细胞组成和通路表达方面，确定HGSOC亚型的基础 通过两个目标。 目标1：以单细胞分辨率表征转录组和选定蛋白质，并进行去卷积 现有的肿瘤基因表达数据。单细胞RNA和表面蛋白丰度将在 对于高级别浆液性卵巢癌的单细胞水平，将使用无监督分析来鉴定细胞 群体，将分析细胞表面蛋白以表征免疫区室、细胞类型标志物 基因将被定义，并且标记基因将被用于对匹配的批量RNA-seq样品进行去卷积。这将 允许对来自较大队列的现有数据进行去卷积，从而允许对肿瘤进行生存分析 按细胞组成分层。 目的2：表征HGSOC肿瘤中癌细胞的转录组学谱，以鉴定 在癌细胞内不稳定表达的途径。癌细胞内的基因表达将是 使用两种互补方法测量：（i）原位患者来源的异种移植物（PDX）和（ii） 单细胞RNAseq.对于每种途径，将生成富集分数，并且将具有表达的途径 将识别在整个群组中显著变化的水平。结合基因的表达水平 在可变途径内，根据现有数据集的癌细胞分数估计值将能够推断 在控制癌细胞后，这些可变途径在报告的亚型之间的差异程度 丰度。 该提案预计将产生两个主要成果：1）了解细胞在多大程度上 组成和途径表达有助于HGSOC基因表达亚型;和2）HGSOC基因表达亚型的估计值。 在现有的研究中，细胞类型的比例与公共基因表达数据。预计短期影响 通过基于从细胞组成中鉴定的变异改进HGSOC亚型的存活预测 和途径表达，这项工作预计将在长期内产生影响，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。