Atlas of clinically distinct cell states and ecosystems across human solid tumors.
Atlas of clinically distinct cell states and ecosystems across human solid tumors.
复制标题
DOI：
10.1016/j.cell.2021.09.014
复制
发表时间：
2021-10-14
期刊：
影响因子：
64.5
通讯作者：
Newman AM
中科院分区：
文献类型：
--
作者：
Luca BA;Steen CB;Matusiak M;Azizi A;Varma S;Zhu C;Przybyl J;Espín-Pérez A;Diehn M;Alizadeh AA;van de Rijn M;Gentles AJ;Newman AM
确定细胞如何随其局部信号环境而变化并组织成不同的细胞群落对于理解发育，衰老和癌症等不同过程至关重要。在这里，我们介绍EcoTyper，这是一个机器学习框架，用于从批量，单细胞和空间分辨的基因表达数据中大规模识别和验证细胞状态和多细胞群落。当应用于16种人类癌症的12种主要细胞系时，EcoTyper确定了69种转录定义的细胞状态。大多数状态是肿瘤组织特有的，在肿瘤类型中普遍存在，并且具有显著的预后性。通过分析细胞状态共现模式，我们发现了10个具有出乎意料的强保守性的临床上不同的多细胞群落，其中三个具有与不良生存相关的骨髓和基质成分，一个富含正常组织，两个与早期癌症发展相关。这项研究阐明了人类癌症细胞组织的基本单位，并为任何组织中细胞生态系统的大规模分析提供了框架。EcoTyper是一种用于从基因表达数据中识别和表征细胞状态和生态系统的机器学习框架，可以深入了解人类癌症的细胞景观和群落结构，这是癌症相关死亡率的主要原因。
Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially-resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally-defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell state co-occurrence patterns, we discovered 10 clinically-distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue. EcoTyper, a machine learning framework for identifying and characterizing cell states and ecosystems from gene expression data, yields insights into the cellular landscape and community structure of human carcinoma, the leading cause of cancer-related mortality.
登录
查看更多内容
EMT亚型影响上皮可塑性和细胞迁移模式。
影响因子：
11.8
作者：
Aiello NM;Maddipati R;Norgard RJ;Balli D;Li J;Yuan S;Yamazoe T;Black T;Sahmoud A;Furth EE;Bar-Sagi D;Stanger BZ
通讯作者：
Stanger BZ
影响因子：
16.6
作者：
Aran D;Camarda R;Odegaard J;Paik H;Oskotsky B;Krings G;Goga A;Sirota M;Butte AJ
通讯作者：
Butte AJ
影响因子：
7
作者：
Foley, Joseph W.;Zhu, Chunfang;West, Robert B.
通讯作者：
West, Robert B.
影响因子：
12.3
作者：
Abdelaal, Tamim;Michielsen, Lieke;Mahfouz, Ahmed
通讯作者：
Mahfouz, Ahmed
影响因子：
82.9
作者：
Binnewies M;Roberts EW;Kersten K;Chan V;Fearon DF;Merad M;Coussens LM;Gabrilovich DI;Ostrand-Rosenberg S;Hedrick CC;Vonderheide RH;Pittet MJ;Jain RK;Zou W;Howcroft TK;Woodhouse EC;Weinberg RA;Krummel MF
通讯作者：
Krummel MF