Suppr超能文献

人类肿瘤图谱网络:以单细胞分辨率绘制肿瘤在空间和时间上的转变图谱。

The Human Tumor Atlas Network: Charting Tumor Transitions across Space and Time at Single-Cell Resolution.

机构信息

Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.

Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA; Koch Institute for Integrative Cancer Research, Department of Biology, MIT, Cambridge, MA 02139, USA.

出版信息

Cell. 2020 Apr 16;181(2):236-249. doi: 10.1016/j.cell.2020.03.053.

Abstract

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

摘要

癌症中的关键转变——包括肿瘤起始、局部扩张、转移和治疗抵抗——涉及动态肿瘤生态系统中细胞之间的复杂相互作用。变革性的单细胞基因组学技术和空间多重原位方法现在提供了一个前所未有的分辨率来研究这种复杂性的机会。作为国家癌症研究所(NCI)癌症登月计划的一部分,人类肿瘤图谱网络(HTAN)将建立一个临床、实验、计算和组织框架,为一系列不同的肿瘤类型生成信息丰富且易于访问的癌症转变三维图谱。这项工作既是对正在进行的健康器官图谱绘制工作的补充,也是对之前集中在单一时间点进行批量测序的大型癌症基因组学方法的补充。生成单细胞、多参数、纵向图谱,并将其与临床结果相结合,应该有助于识别新的预测性生物标志物和特征,以及治疗相关的细胞类型、细胞状态和细胞间相互作用,贯穿整个转变过程。由此产生的肿瘤图谱将对我们对癌症生物学的理解产生深远的影响,并有可能改善癌症检测、预防和治疗发现,为癌症患者和癌症高危人群提供更好的精准医疗治疗。

相似文献

2
Clinical Perspectives of Single-Cell RNA Sequencing.单细胞 RNA 测序的临床视角。
Biomolecules. 2021 Aug 6;11(8):1161. doi: 10.3390/biom11081161.
4
The Case for a Pre-Cancer Genome Atlas (PCGA).癌症前基因组图谱(PCGA)的理由。
Cancer Prev Res (Phila). 2016 Feb;9(2):119-24. doi: 10.1158/1940-6207.CAPR-16-0024. Epub 2016 Feb 1.
9
10
Applications of Single-Cell Omics in Tumor Immunology.单细胞组学在肿瘤免疫学中的应用。
Front Immunol. 2021 Jun 9;12:697412. doi: 10.3389/fimmu.2021.697412. eCollection 2021.

引用本文的文献

9
Informatics at the Frontier of Cancer Research.癌症研究前沿的信息学
Cancer Res. 2025 Aug 15;85(16):2967-2986. doi: 10.1158/0008-5472.CAN-24-2829.
10
Single-cell RNA sequencing: new insights for pulmonary endothelial cells.单细胞RNA测序:肺内皮细胞的新见解
Front Cell Dev Biol. 2025 Jun 17;13:1576067. doi: 10.3389/fcell.2025.1576067. eCollection 2025.

本文引用的文献

1
Jointly Embedding Multiple Single-Cell Omics Measurements.联合嵌入多个单细胞组学测量值
Algorithms Bioinform. 2019 Sep 3;143. doi: 10.4230/LIPIcs.WABI.2019.10.
8
Cancer biology as revealed by the research autopsy.研究尸检揭示的癌症生物学
Nat Rev Cancer. 2019 Dec;19(12):686-697. doi: 10.1038/s41568-019-0199-4. Epub 2019 Sep 13.

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