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Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. ​

文献信息 ​

DOI10.1126/science.aad0501
PMID27124452
期刊Science (New York, N.Y.)
影响因子45.8
JCR 分区Q1
发表年份2016
被引次数2595
关键词黑色素瘤, 单细胞RNA测序, 肿瘤微环境, 转录组异质性, T细胞耗竭
文献类型Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.
ISSN0036-8075
页码189-96
期号352(6282)
作者Itay Tirosh, Benjamin Izar, Sanjay M Prakadan, Marc H Wadsworth, Daniel Treacy, John J Trombetta, Asaf Rotem, Christopher Rodman, Christine Lian, George Murphy, Mohammad Fallahi-Sichani, Ken Dutton-Regester, Jia-Ren Lin, Ofir Cohen, Parin Shah, Diana Lu, Alex S Genshaft, Travis K Hughes, Carly G K Ziegler, Samuel W Kazer, Aleth Gaillard, Kellie E Kolb, Alexandra-Chloé Villani, Cory M Johannessen, Aleksandr Y Andreev, Eliezer M Van Allen, Monica Bertagnolli, Peter K Sorger, Ryan J Sullivan, Keith T Flaherty, Dennie T Frederick, Judit Jané-Valbuena, Charles H Yoon, Orit Rozenblatt-Rosen, Alex K Shalek, Aviv Regev, Levi A Garraway

一句话小结 ​

本研究通过对19名黑色素瘤患者的4645个单细胞进行单细胞RNA测序，揭示了肿瘤内恶性细胞的转录异质性及其与细胞周期、空间环境和耐药性程序的关联，发现不同细胞状态和肿瘤微环境模式。同时，分析肿瘤浸润T细胞的疲惫程序和激活关系，为靶向治疗和免疫治疗提供了新的见解，推动了对黑色素瘤细胞生态系统的理解。

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黑色素瘤 · 单细胞RNA测序 · 肿瘤微环境 · 转录组异质性 · T细胞耗竭

摘要 ​

为了探讨黑色素瘤肿瘤的不同基因型和表型状态，我们对来自19名患者的4645个单细胞进行了单细胞RNA测序（RNA-seq），分析了恶性细胞、免疫细胞、间质细胞和内皮细胞。同一肿瘤内的恶性细胞表现出与细胞周期、空间环境以及耐药性程序相关的转录异质性。特别是，所有肿瘤中都存在来自两种不同转录细胞状态的恶性细胞，MITF转录因子水平较高的肿瘤中也包含了MITF水平较低且AXL激酶水平升高的细胞。单细胞分析表明存在不同的肿瘤微环境模式，包括细胞间相互作用。对肿瘤浸润T细胞的分析揭示了疲惫程序、它们与T细胞激活和克隆扩增的关系，以及不同患者间的变异性。总体而言，我们开始揭示肿瘤的细胞生态系统，以及单细胞基因组学如何提供对靶向治疗和免疫治疗的深刻见解。

英文摘要 ​

To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

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主要研究问题 ​

  1. 在不同转录状态的恶性细胞中，是否存在特定的基因表达模式与药物抵抗性相关？
  2. 单细胞RNA测序如何帮助识别肿瘤微环境中不同细胞类型的相互作用？
  3. 针对肿瘤浸润性T细胞的耗竭程序，是否有潜在的干预策略可以提高其活性？
  4. 不同患者的肿瘤微环境差异如何影响免疫治疗的效果？
  5. MITF转录因子与AXL激酶在黑色素瘤中的作用机制是什么？

核心洞察 ​

研究背景和目的 ​

黑色素瘤是一种复杂的肿瘤生态系统，由多种细胞类型（恶性细胞、免疫细胞、基质细胞等）组成。尽管靶向疗法和免疫疗法在黑色素瘤的治疗中取得了一定成效，但肿瘤的异质性和耐药性问题仍未得到充分理解。因此，本研究旨在利用单细胞RNA测序技术，深入探讨转移性黑色素瘤的细胞组成及其相互作用，以揭示其对治疗反应的影响。

主要方法/材料/实验设计 ​

本研究对19名患者的4645个单细胞进行了单细胞RNA测序，涵盖恶性细胞、免疫细胞、基质细胞和内皮细胞。以下是研究的技术路线：

Mermaid diagram
  1. 样本收集：从19名患者的新鲜黑色素瘤组织中提取细胞。
  2. 单细胞分离：使用流式细胞术分离出活细胞，分为免疫细胞（CD45+）和非免疫细胞（CD45-）。
  3. 单细胞RNA测序：对分离的单细胞进行cDNA合成和高通量测序。
  4. 数据分析和细胞分类：利用非线性降维和密度聚类分析，识别不同细胞类型及其转录状态。
  5. 功能分析和临床相关性：分析细胞周期、耐药性及其在肿瘤微环境中的作用。

关键结果和发现 ​

  1. 细胞异质性：恶性细胞展示了显著的转录异质性，特别是在细胞周期和耐药程序上。
  2. 细胞状态：每个肿瘤中都存在两种不同的转录细胞状态：高MITF状态和低MITF、AXL高状态。AXL高状态与耐药性相关。
  3. 微环境分析：肿瘤微环境中存在明显的细胞间相互作用，特别是肿瘤浸润T细胞的耗竭程序。
  4. T细胞异质性：不同患者的T细胞表现出不同的激活状态和耗竭标志，影响其抗肿瘤功能。

主要结论/意义/创新性 ​

本研究揭示了转移性黑色素瘤的复杂细胞生态系统及其在治疗中的重要性。通过单细胞RNA测序技术，研究者能够识别出与耐药性相关的细胞状态，进而为个性化治疗提供了新的生物标志物。此外，研究结果强调了肿瘤微环境中细胞间相互作用的关键作用，可能为未来的治疗策略提供新的思路。

研究局限性和未来方向 ​

  1. 局限性：样本量相对较小，可能无法全面代表所有黑色素瘤患者的情况。
  2. 未来方向：建议进行更大规模的多中心研究，以验证发现的细胞状态与临床反应的相关性。同时，探索如何通过调节微环境来改善治疗效果，尤其是在耐药性黑色素瘤的管理中。

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引用本文的文献 ​

  1. Molecular characterisation of cutaneous melanoma: creating a framework for targeted and immune therapies. - Shivshankari Rajkumar;Ian R Watson - British journal of cancer (2016)
  2. High-throughput genomic profiling of tumor-infiltrating leukocytes. - Aaron M Newman;Ash A Alizadeh - Current opinion in immunology (2016)
  3. A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells. - Meromit Singer;Chao Wang;Le Cong;Nemanja D Marjanovic;Monika S Kowalczyk;Huiyuan Zhang;Jackson Nyman;Kaori Sakuishi;Sema Kurtulus;David Gennert;Junrong Xia;John Y H Kwon;James Nevin;Rebecca H Herbst;Itai Yanai;Orit Rozenblatt-Rosen;Vijay K Kuchroo;Aviv Regev;Ana C Anderson - Cell (2016)
  4. Overcome tumor heterogeneity-imposed therapeutic barriers through convergent genomic biomarker discovery: A braided cancer river model of kidney cancer. - James J Hsieh;Brandon J Manley;Nabeela Khan;JianJiong Gao;Maria I Carlo;Emily H Cheng - Seminars in cell & developmental biology (2017)
  5. Divide or Conquer: Cell Cycle Regulation of Invasive Behavior. - Abraham Q Kohrman;David Q Matus - Trends in cell biology (2017)
  6. Genomic Approaches to Understanding Response and Resistance to Immunotherapy. - David A Braun;Kelly P Burke;Eliezer M Van Allen - Clinical cancer research : an official journal of the American Association for Cancer Research (2016)
  7. Single-Cell Transcriptomics Bioinformatics and Computational Challenges. - Olivier B Poirion;Xun Zhu;Travers Ching;Lana Garmire - Frontiers in genetics (2016)
  8. Increased gene expression noise in human cancers is correlated with low p53 and immune activities as well as late stage cancer. - Rongfei Han;Guanqun Huang;Yejun Wang;Yafei Xu;Yueming Hu;Wenqi Jiang;Tianfu Wang;Tian Xiao;Duo Zheng - Oncotarget (2016)
  9. Tissue-resident macrophages - how to humanize our knowledge. - Andreas Schlitzer;Joachim L Schultze - Immunology and cell biology (2017)
  10. The Role of TAM Family Receptors in Immune Cell Function: Implications for Cancer Therapy. - Magdalena Paolino;Josef M Penninger - Cancers (2016)

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