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单细胞质谱流式细胞术鉴定高级别浆液性卵巢肿瘤中的常见细胞亚群。

Commonly Occurring Cell Subsets in High-Grade Serous Ovarian Tumors Identified by Single-Cell Mass Cytometry.

机构信息

Baxter Laboratory for Stem Cell Biology, Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA.

Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, 1450 Third Street, San Francisco, CA 94158, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, 1450 Third Street, San Francisco, CA 94158, USA.

出版信息

Cell Rep. 2018 Feb 13;22(7):1875-1888. doi: 10.1016/j.celrep.2018.01.053.

Abstract

We have performed an in-depth single-cell phenotypic characterization of high-grade serous ovarian cancer (HGSOC) by multiparametric mass cytometry (CyTOF). Using a CyTOF antibody panel to interrogate features of HGSOC biology, combined with unsupervised computational analysis, we identified noteworthy cell types co-occurring across the tumors. In addition to a dominant cell subset, each tumor harbored rarer cell phenotypes. One such group co-expressed E-cadherin and vimentin (EV), suggesting their potential role in epithelial mesenchymal transition, which was substantiated by pairwise correlation analyses. Furthermore, tumors from patients with poorer outcome had an increased frequency of another rare cell type that co-expressed vimentin, HE4, and cMyc. These poorer-outcome tumors also populated more cell phenotypes, as quantified by Simpson's diversity index. Thus, despite the recognized genomic complexity of the disease, the specific cell phenotypes uncovered here offer a focus for therapeutic intervention and disease monitoring.

摘要

我们通过多参数质谱流式细胞术(CyTOF)对高级别浆液性卵巢癌(HGSOC)进行了深入的单细胞表型特征分析。使用 CyTOF 抗体面板来探究 HGSOC 生物学的特征,结合无监督计算分析,我们确定了在肿瘤中共同存在的值得注意的细胞类型。除了占主导地位的细胞亚群外,每个肿瘤还存在更罕见的细胞表型。其中一组共同表达 E-钙黏蛋白和波形蛋白(EV),表明它们在上皮间质转化中的潜在作用,这通过成对相关分析得到了证实。此外,预后较差的患者的肿瘤中另一种罕见的细胞类型——同时表达波形蛋白、HE4 和 cMyc 的细胞类型的频率增加。这些预后较差的肿瘤还存在更多的细胞表型,这可以通过辛普森多样性指数来量化。因此,尽管该疾病的基因组复杂性已得到公认,但这里揭示的特定细胞表型为治疗干预和疾病监测提供了重点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95d2/8556706/b9b8f5a9877a/nihms-1747794-f0001.jpg

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