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单细胞 RNA 测序揭示了前列腺癌细胞生态系统中克隆进化之上的发育层次。

Single-Cell RNA-seq Reveals a Developmental Hierarchy Super-Imposed Over Subclonal Evolution in the Cellular Ecosystem of Prostate Cancer.

机构信息

Key Laboratory of Genomics and Precision Medicine, Beijing Institute of Genomics, China National Center for Bioinformation, Chinese Academy of Sciences, Beijing, 100101, China.

University of Chinese Academy of Sciences, Beijing, 100049, China.

出版信息

Adv Sci (Weinh). 2022 May;9(15):e2105530. doi: 10.1002/advs.202105530. Epub 2022 Mar 24.


DOI:10.1002/advs.202105530
PMID:35322584
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9131431/
Abstract

Prostate cancer (PCa) is a complex disease. An ongoing accumulation of mutations results in increased genetic diversity, with the tumor acquiring distinct subclones. However, non-genetic intra-tumoral heterogeneity, the cellular differentiation state and the interplay between subclonal evolution and transcriptional heterogeneity are poorly understood. Here, the authors perform single-cell RNA sequencing from 14 untreated PCa patients. They create an extensive cell atlas of the PCa patients and mapped developmental states onto tumor subclonal evolution. They identify distinct subclones across PCa patients and then stratify tumor cells into four transcriptional subtypes, EMT-like (subtype 0), luminal A-like (subtype 1), luminal B/C-like (subtype 2), and basal-like (subtype 3). These subtypes are hierarchically organized into stem cell-like and differentiated status. Strikingly, multiple subclones within a single primary tumor present with distinct combinations of preferential subtypes. In addition, subclones show different communication strengths with other cell types within the tumor ecosystem, which may modulate the distinct transcriptional subtypes of the subclones. Notably, by integrating TCGA data, they discover that both tumor cell transcriptional heterogeneity and cellular ecosystem diversity correlate with features of a poor prognosis. Collectively, their study provides the analysis of subclonal and transcriptional heterogeneity and its implication for patient prognosis.

摘要

前列腺癌(PCa)是一种复杂的疾病。不断积累的突变导致遗传多样性增加,肿瘤获得独特的亚克隆。然而,非遗传的肿瘤内异质性、细胞分化状态以及亚克隆进化和转录异质性之间的相互作用还了解甚少。在这里,作者对 14 名未经治疗的 PCa 患者进行了单细胞 RNA 测序。他们创建了一个 PCa 患者的广泛细胞图谱,并将发育状态映射到肿瘤亚克隆进化上。他们在 PCa 患者中识别出不同的亚克隆,然后将肿瘤细胞分为四个转录亚型,EMT 样(亚型 0)、管腔 A 样(亚型 1)、管腔 B/C 样(亚型 2)和基底样(亚型 3)。这些亚型按干细胞样和分化状态进行分层组织。引人注目的是,单个原发性肿瘤内的多个亚克隆呈现出不同的优势亚型组合。此外,亚克隆与肿瘤生态系统内的其他细胞类型之间显示出不同的通讯强度,这可能调节亚克隆的不同转录亚型。值得注意的是,通过整合 TCGA 数据,他们发现肿瘤细胞转录异质性和细胞生态系统多样性与预后不良的特征相关。总的来说,他们的研究提供了对亚克隆和转录异质性的分析及其对患者预后的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/333712d1b10e/ADVS-9-2105530-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/91a5df742af6/ADVS-9-2105530-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/a445dea6d175/ADVS-9-2105530-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/7a775067f276/ADVS-9-2105530-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/8c39d7c3d699/ADVS-9-2105530-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/e3c3e39216b4/ADVS-9-2105530-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/3968333040cc/ADVS-9-2105530-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/333712d1b10e/ADVS-9-2105530-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/91a5df742af6/ADVS-9-2105530-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/a445dea6d175/ADVS-9-2105530-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/7a775067f276/ADVS-9-2105530-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/8c39d7c3d699/ADVS-9-2105530-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/e3c3e39216b4/ADVS-9-2105530-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/3968333040cc/ADVS-9-2105530-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3360/9131431/333712d1b10e/ADVS-9-2105530-g006.jpg

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[3]
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[4]
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[5]
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[6]
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J Transl Med. 2024-11-12

[7]
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[8]
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[9]
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[10]
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本文引用的文献

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J Clin Med. 2021-5-29

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Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing.

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