High-throughput Epigenomic Mapping of Regulatory Elements in Ovarian Cancer at Basepair Resolution
卵巢癌调控元件碱基对分辨率的高通量表观基因组图谱
基本信息
- 批准号:10201528
- 负责人:
- 金额:$ 33.4万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-07-01 至 2023-06-30
- 项目状态:已结题
- 来源:
- 关键词:ARID1A geneAddressAreaBRCA1 geneBehaviorBinding SitesBioinformaticsBiologicalBiological AssayCategoriesClear CellClinicalClinical ManagementDNADNA MethylationDNA Repair PathwayDNA Sequence AlterationDataData SetDependenceDiagnosisDiseaseElementsEnhancersEnzymesEpigenetic ProcessEpithelial ovarian cancerFreezingGenesGenetic TranscriptionGenomeHistologicHumanHybridsKnowledgeLasersLightLiteratureMalignant - descriptorMalignant Female Reproductive System NeoplasmMalignant NeoplasmsMalignant neoplasm of ovaryMapsMethodsMethylationMolecularMutationNeoplasm MetastasisNucleosomesOncogenicOvarian Clear Cell TumorOvarian Endometrioid AdenocarcinomaOvarian Serous TumorPIK3CA genePathway interactionsPatientsPhenotypePlatinumProcessPrognosisPublishingRAD51C geneRNARecurrenceRegulatory ElementResearchResearch PriorityResistance developmentResolutionRoleSamplingSerousSignal TransductionSpecimenStandardizationStratificationTP53 geneTechnologyThe Cancer Genome AtlasTherapeuticTissuesTranscriptional RegulationTumor Suppressor GenesUnited StatesUntranslated RNAbasebioinformatics pipelinebioinformatics toolbisulfitechemotherapychromatin remodelingcost effectiveendometriosisepigenetic profilingepigenetic regulationepigenomeepigenomicsgenome-wideinnovationinterestmethylomeneoplastic cellpromoterresponsesubclonal heterogeneitytherapeutically effectivetooltranscription factortranscriptometranscriptome sequencingtranscriptomicstumor
项目摘要
PROJECT SUMMARY / ABSTRACT
Epithelial ovarian cancer (OC) is the deadliest gynecological cancer in the US. It consists of several
histotypes, each biologically distinct with different clinical challenges. Clear cell OC (CCOC) represents a much
understudied histotype marked by low response rates to standard chemotherapies and a lack of effective
therapeutic options. Endometrioid OC (ENOC) is a closely related histotype. CCOC and ENOC share a common
tissue of origin (endometriosis) and key genetic mutations but differ markedly in their clinical behavior. Our
recently published study showed that most of these mutations were detectable in untransformed endometriosis,
suggesting further distinct mechanisms of malignant transformation. We seek to delineate the contributions of
altered epigenetics and transcriptional control in this process. The most prevalent histotype, high grade serous
OC (HGSOC) harbors epigenetic inactivation of key tumor suppressor genes BRCA1 and RAD51C in the
homologous DNA repair (HR) pathway, a pathway highlighted for its therapeutic relevance. These tumors often
recur within five years despite initial good response to platinum therapy. Past epigenetic studies of primary OC
have usually been conducted without stratification of histotypes, or heavily biased towards the most common
histotype HGSOC. Even for HGSOC, epigenetic profiling has been performed with only limited genome coverage,
focused primarily on gene promoters. Enhancers have incurred much interest in recent literature as the most
dynamically used compartment of the genome, but enhancer studies of primary human OC samples, especially
those of distinct histotypes, are generally lacking. This is attributable in part to limitations of existing technology.
To address this knowledge gap, we propose to implement an innovative cost-effective tool, compatible with
primary human samples, to profile enhancers and other regulatory elements using a targeted technology that
jointly profiles DNA methylation and nucleosome occupancy (Target-NOMe Seq). We will also develop the
associated bioinformatic pipeline needed to apply this technology (Aim 1). We will use Target-NOMe Seq and
transcriptome profiling to analyze 300 bulk OC tumor samples, as well as microdissected tumor and supportive
stromal compartments on a subset of samples (Aim 2). With this rich dataset we hope to address the research
and clinical questions described above (Aim 3). We will use enhancer and promoter epigenetic states, and a
new category of non-coding RNA - enhancer RNA (eRNA), as well as the expression levels of transcription
factors and candidate target genes to define transcriptional regulatory networks. By analyzing which networks
are altered in the different histotypes, we will gain a better understanding of the distinct molecular makeup of
each of these histotypes. The relatively high sequencing depth of our focused Target-NOMe Seq technology will
also allow us to assess intertumor and subclonal heterogeneity to shed light on potential mechanisms of tumor
recurrence.
项目总结/摘要
卵巢上皮癌(OC)是美国最致命的妇科癌症。它由几个
组织型,每种组织型在生物学上都不同,具有不同的临床挑战。透明细胞OC(CCOC)代表了
未充分研究的组织型对标准化疗的反应率低,缺乏有效的
治疗选择子宫内膜样癌(ENOC)是一种密切相关的组织型。CCOC和ENOC有一个共同点,
组织起源(子宫内膜异位症)和关键的基因突变,但在临床行为上有显着差异。我们
最近发表的研究表明,这些突变中的大多数在未转化的子宫内膜异位症中是可检测的,
这进一步表明了恶性转化的不同机制。我们试图描述的贡献,
在这个过程中改变了表观遗传学和转录控制。最常见的组织型,高级别浆液性
OC(HGSOC)在细胞中携带关键肿瘤抑制基因BRCA 1和RAD 51 C的表观遗传失活。
同源DNA修复(HR)途径,这是一种因其治疗相关性而突出的途径。这些肿瘤通常
5年内复发,尽管最初对铂类治疗反应良好。原发性骨肉瘤的表观遗传学研究
通常没有进行组织型分层,或严重偏向于最常见的
组织型HGSOC。即使对于HGSOC,表观遗传分析也仅在有限的基因组覆盖率下进行,
主要研究基因启动子。增强剂在最近的文献中引起了很大的兴趣,因为最重要的是增强剂。
动态使用的基因组区室,但增强子研究的主要人类OC样本,特别是
不同组织型的人通常缺乏。部分原因是现有技术的局限性。
为了弥补这一知识差距,我们建议采用一种创新的、符合成本效益的工具,
使用靶向技术,
联合分析DNA甲基化和核小体占据(Target-NOMe Seq)。我们亦会发展
相关的生物信息学管道需要应用这项技术(目标1)。我们将使用Target-NOMe Seq,
转录组谱分析,以分析300块OC肿瘤样本,以及显微切割的肿瘤和支持性
样品子集上的基质区室(目的2)。有了这个丰富的数据集,我们希望能够解决研究
和上述临床问题(目标3)。我们将使用增强子和启动子表观遗传状态,
一类新的非编码RNA -增强子RNA(eRNA),以及转录的表达水平
因子和候选靶基因来定义转录调控网络。通过分析哪些网络
在不同的组织型中发生了改变,我们将更好地了解不同的分子组成,
每一种组织类型我们专注的Target-NOMe Seq技术的相对较高的测序深度将
也使我们能够评估肿瘤间和亚克隆异质性,以阐明肿瘤的潜在机制。
复发
项目成果
期刊论文数量(0)
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{{ truncateString('Hui Shen', 18)}}的其他基金
High-throughput Epigenomic Mapping of Regulatory Elements in Ovarian Cancer at Basepair Resolution
卵巢癌调控元件碱基对分辨率的高通量表观基因组图谱
- 批准号:
10582199 - 财政年份:2018
- 资助金额:
$ 33.4万 - 项目类别:
High-throughput Epigenomic Mapping of Regulatory Elements in Ovarian Cancer at Basepair Resolution
卵巢癌调控元件碱基对分辨率的高通量表观基因组图谱
- 批准号:
10436903 - 财政年份:2018
- 资助金额:
$ 33.4万 - 项目类别:
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