Suppr超能文献

通过空间和单细胞转录组学鉴定和表征雌激素受体阳性转移性乳腺癌中的增殖细胞群。

Identification and characterization of a proliferative cell population in estrogen receptor-positive metastatic breast cancer through spatial and single-cell transcriptomics.

作者信息

Yoshitake Ryohei, Mori Hitomi, Ha Desiree, Wu Xiwei, Wang Jinhui, Wang Xiaoqiang, Saeki Kohei, Chang Gregory, Shim Hyun Jeong, Chan Yin, Chen Shiuan

出版信息

bioRxiv. 2023 Feb 3:2023.01.31.526403. doi: 10.1101/2023.01.31.526403.

Abstract

BACKGROUND

Intratumor heterogeneity is a hallmark of most solid tumors, including breast cancers. We applied spatial transcriptomics and single-cell RNA-sequencing technologies to profile spatially resolved cell populations within estrogen receptor-positive (ER ) metastatic breast cancers and elucidate their importance in estrogen-dependent tumor growth.

METHODS

Spatial transcriptomics and single-cell RNA-sequencing were performed on two patient-derived xenografts (PDXs) of "ER-high" metastatic breast cancers with opposite estrogen-mediated growth responses: estrogen-suppressed GS3 (80-100% ER) and estrogen-stimulated SC31 (30-75% ER) models. The analyses included samples treated with and without 17β-estradiol. The findings were validated via scRNA-seq analyses on "ER-low" estrogen-accelerating PDX, GS1 (5% ER). The results from our spatial and single-cell analyses were further supported by the analysis of a publicly available single cell dataset and a protein-based dual immunohistochemical (IHC) evaluation using three important clinical markers [i.e., ER, progesterone receptor (PR), and Ki67]. The translational implication of these results was assessed by clinical outcome analyses on public breast cancer cohorts.

RESULTS

Our novel space-gene-function study revealed a "proliferative" cell population in addition to three major spatially distinct compartments within ER metastatic breast cancers. These compartments showed functional diversity (i.e., estrogen-responsive, proliferative, hypoxia-induced, and inflammation-related). The "proliferative ( )" population, not "estrogen-responsive" compartment, was crucial for estrogen-dependent tumor growth, leading to the acquisition of luminal B features. The cells with induction of typical estrogen-responsive genes such as were not directly linked to estrogen-dependent proliferation. Additionally, the dual IHC analyses demonstrated the distinct contribution of the Ki67 proliferative cells toward estrogen-mediated growth and their response to palbociclib, a CDK4/6 inhibitor. The gene signatures developed from the proliferative, hypoxia-induced, and inflammation-related compartments were significantly correlated with worse clinical outcomes, while patients with the high estrogen-responsive scores showed better prognosis, confirming that the estrogen-responsive compartment would not be directly associated with estrogen-dependent tumor progression.

CONCLUSIONS

For the first time, our study elucidated a "proliferative" cell population distinctly distributed in ER metastatic breast cancers. They contribute differently toward progression of these cancers, and the gene signature in the "proliferative" compartment is an important determinant of luminal cancer subtypes.

摘要

背景

肿瘤内异质性是包括乳腺癌在内的大多数实体瘤的一个标志。我们应用空间转录组学和单细胞RNA测序技术来描绘雌激素受体阳性(ER )转移性乳腺癌内空间分辨的细胞群体,并阐明它们在雌激素依赖性肿瘤生长中的重要性。

方法

对两个具有相反雌激素介导生长反应的“ER高”转移性乳腺癌患者来源异种移植模型(PDXs)进行空间转录组学和单细胞RNA测序:雌激素抑制的GS3(80 - 100% ER)和雌激素刺激的SC31(30 - 75% ER)模型。分析包括用和不用17β-雌二醇处理的样本。通过对“ER低”雌激素促进的PDX,GS1(5% ER)进行scRNA-seq分析来验证这些发现。我们的空间和单细胞分析结果通过对一个公开可用的单细胞数据集的分析以及使用三种重要临床标志物[即ER、孕激素受体(PR)和Ki67]的基于蛋白质的双重免疫组织化学(IHC)评估得到进一步支持。通过对公共乳腺癌队列的临床结果分析来评估这些结果的转化意义。

结果

我们新颖的空间-基因-功能研究揭示,除了ER 转移性乳腺癌内三个主要的空间不同区室之外,还有一个“增殖性”细胞群体。这些区室表现出功能多样性(即雌激素反应性、增殖性、缺氧诱导性和炎症相关性)。对于雌激素依赖性肿瘤生长至关重要并导致获得腔面B特征的是“增殖性( )”群体,而非“雌激素反应性”区室。诱导典型雌激素反应性基因如 的细胞与雌激素依赖性增殖没有直接联系。此外,双重IHC分析证明Ki67 增殖性细胞对雌激素介导生长的独特贡献及其对CDK4/6抑制剂帕博西尼的反应。从增殖性、缺氧诱导性和炎症相关性区室开发的基因特征与更差的临床结果显著相关,而雌激素反应性评分高的患者预后较好,证实雌激素反应性区室与雌激素依赖性肿瘤进展没有直接关联。

结论

我们的研究首次阐明了在ER 转移性乳腺癌中明显分布的一个“增殖性”细胞群体。它们对这些癌症的进展有不同贡献,并且“增殖性”区室中的基因特征是腔面癌亚型的一个重要决定因素。

相似文献

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验