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单细胞 RNA 测序揭示人类乳腺癌转移中的转录多样性和生物能量转变。

Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing.

机构信息

Department of Physiology and Biophysics, University of California, Irvine, Irvine, CA, USA.

Center for Complex Biological Systems, University of California, Irvine, Irvine, CA, USA.

出版信息

Nat Cell Biol. 2020 Mar;22(3):310-320. doi: 10.1038/s41556-020-0477-0. Epub 2020 Mar 6.

DOI:10.1038/s41556-020-0477-0
PMID:32144411
Abstract

Although metastasis remains the cause of most cancer-related mortality, mechanisms governing seeding in distal tissues are poorly understood. Here, we establish a robust method for the identification of global transcriptomic changes in rare metastatic cells during seeding using single-cell RNA sequencing and patient-derived-xenograft models of breast cancer. We find that both primary tumours and micrometastases display transcriptional heterogeneity but micrometastases harbour a distinct transcriptome program conserved across patient-derived-xenograft models that is highly predictive of poor survival of patients. Pathway analysis revealed mitochondrial oxidative phosphorylation as the top pathway upregulated in micrometastases, in contrast to higher levels of glycolytic enzymes in primary tumour cells, which we corroborated by flow cytometric and metabolomic analyses. Pharmacological inhibition of oxidative phosphorylation dramatically attenuated metastatic seeding in the lungs, which demonstrates the functional importance of oxidative phosphorylation in metastasis and highlights its potential as a therapeutic target to prevent metastatic spread in patients with breast cancer.

摘要

尽管转移仍然是大多数癌症相关死亡的原因,但控制种子在远端组织中播种的机制还了解甚少。在这里,我们使用单细胞 RNA 测序和乳腺癌患者来源异种移植模型,建立了一种可靠的方法来鉴定播种过程中稀有转移性细胞中的全局转录组变化。我们发现,原发肿瘤和微转移均显示出转录异质性,但微转移具有跨越患者来源异种移植模型保守的独特转录组程序,该程序高度预测患者的生存不良。通路分析显示,在微转移中,线粒体氧化磷酸化是上调最多的通路,与原发肿瘤细胞中糖酵解酶水平较高形成对比,我们通过流式细胞术和代谢组学分析证实了这一点。氧化磷酸化的药理学抑制显著减弱了肺部的转移播种,这证明了氧化磷酸化在转移中的功能重要性,并强调了其作为预防乳腺癌患者转移扩散的治疗靶点的潜力。

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Tumour heterogeneity and metastasis at single-cell resolution.单细胞分辨率下的肿瘤异质性和转移。
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