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
基本信息
- 批准号:9883762
- 负责人:
- 金额:$ 59.57万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-04-01 至 2020-12-31
- 项目状态:已结题
- 来源:
- 关键词:AntibodiesBar CodesBiologicalCancer EtiologyCell FractionCell Surface ProteinsCell surfaceCellsCessation of lifeDataData SetDiseaseEpithelial ovarian cancerGene ExpressionGene Expression ProfilingGenesImmuneImmunohistochemistryImmunotherapyIndividualKnowledgeLettersLinkMalignant NeoplasmsMalignant neoplasm of ovaryMeasuresMembrane ProteinsMissionMolecularPathway interactionsPatientsPatternPopulationPopulation AnalysisProteinsPublic HealthPublishingRNAReportingResearchResearch PersonnelResolutionSamplingSerousSolid NeoplasmStandardizationStromal CellsSurvival AnalysisTechniquesTestingTimeTranscriptTumor BiologyVariantWomanWorkXenograft procedurebasecancer cellcancer gene expressioncancer subtypescell typecohortdisorder subtypeexperimental studygenome-wideimprovedinclusion criteriamolecular subtypesnew technologyprimary outcomesingle-cell RNA sequencingsurvival predictiontranscriptometranscriptome sequencingtranscriptomicstranslational impacttreatment strategytumor
项目摘要
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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Jennifer A. Doherty其他文献
Role of neighborhood context in ovarian cancer survival disparities: current research and future directions
邻里环境在卵巢癌生存差异中的作用:当前研究与未来方向
- DOI:
10.1016/j.ajog.2023.04.026 - 发表时间:
2023-10-01 - 期刊:
- 影响因子:8.400
- 作者:
Scarlett L. Gomez;Ekaterina Chirikova;Valerie McGuire;Lindsay J. Collin;Lauren Dempsey;Pushkar P. Inamdar;Katherine Lawson-Michod;Edward S. Peters;Lawrence H. Kushi;Juraj Kavecansky;Salma Shariff-Marco;Lauren C. Peres;Paul Terry;Elisa V. Bandera;Joellen M. Schildkraut;Jennifer A. Doherty;Andrew Lawson - 通讯作者:
Andrew Lawson
Factors associated with cancer-related pain among Utah cancer survivors
犹他州癌症幸存者中与癌症相关疼痛相关的因素
- DOI:
10.1007/s11764-025-01840-2 - 发表时间:
2025-06-05 - 期刊:
- 影响因子:2.900
- 作者:
Rachel R. Codden;Blessing S. Ofori-Atta;Marjorie E. Carter;Kimberly A. Herget;Jennifer A. Doherty;Anne C. Kirchhoff;Morgan M. Millar - 通讯作者:
Morgan M. Millar
Jennifer A. Doherty的其他文献
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{{ truncateString('Jennifer A. Doherty', 18)}}的其他基金
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 - 财政年份:2019
- 资助金额:
$ 59.57万 - 项目类别:
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:
10589920 - 财政年份:2019
- 资助金额:
$ 59.57万 - 项目类别:
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:
10438939 - 财政年份:2019
- 资助金额:
$ 59.57万 - 项目类别:
Characterizing Molecular Subtypes of Ovarian Cancer in African-American Women
非裔美国女性卵巢癌分子亚型特征
- 批准号:
9386358 - 财政年份:2016
- 资助金额:
$ 59.57万 - 项目类别:
Epidemiologic factors and survival by molecular subtypes of ovarian cancer
卵巢癌分子亚型的流行病学因素和生存率
- 批准号:
8631078 - 财政年份:2013
- 资助金额:
$ 59.57万 - 项目类别:
Epidemiologic factors and survival by molecular subtypes of ovarian cancer
卵巢癌分子亚型的流行病学因素和生存率
- 批准号:
9022436 - 财政年份:2013
- 资助金额:
$ 59.57万 - 项目类别:
Epidemiologic factors and survival by molecular subtypes of ovarian cancer
卵巢癌分子亚型的流行病学因素和生存率
- 批准号:
8504516 - 财政年份:2013
- 资助金额:
$ 59.57万 - 项目类别:
Telomeres and lung cancer incidence and survival
端粒与肺癌的发病率和生存率
- 批准号:
8107313 - 财政年份:2011
- 资助金额:
$ 59.57万 - 项目类别:
Telomeres and lung cancer incidence and survival
端粒与肺癌的发病率和生存率
- 批准号:
8316272 - 财政年份:2011
- 资助金额:
$ 59.57万 - 项目类别:
Telomeres and lung cancer incidence and survival
端粒与肺癌的发病率和生存率
- 批准号:
8538889 - 财政年份:2011
- 资助金额:
$ 59.57万 - 项目类别: