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

Literature Information ​

DOI10.1126/science.aad0501
PMID27124452
JournalScience (New York, N.Y.)
Impact Factor45.8
JCR QuartileQ1
Publication Year2016
Times Cited2595
Keywordsmelanoma, single-cell RNA sequencing, tumor microenvironment, transcriptional heterogeneity, immune cells
Literature TypeJournal 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
Pages189-96
Issue352(6282)
AuthorsItay 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

TL;DR ​

This study utilized single-cell RNA sequencing to analyze 4,645 cells from melanoma tumors, revealing significant transcriptional heterogeneity among malignant cells related to cell cycle, spatial context, and drug resistance, alongside distinct tumor microenvironments and T cell exhaustion patterns. These findings enhance our understanding of the tumor cellular ecosystem and highlight the potential of single-cell genomics to inform targeted and immune therapies for melanoma.

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melanoma · single-cell RNA sequencing · tumor microenvironment · transcriptional heterogeneity · immune cells

Abstract ​

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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Primary Questions Addressed ​

  1. How do the transcriptional states of malignant cells influence their response to different therapeutic strategies in metastatic melanoma?
  2. What are the implications of the observed spatial context on the efficacy of immune checkpoint inhibitors in melanoma treatment?
  3. How might the heterogeneity in T cell exhaustion programs affect the design of personalized immunotherapies for melanoma patients?
  4. In what ways can single-cell RNA-seq data be utilized to identify novel biomarkers for early detection of metastatic melanoma?
  5. How does the interplay between stromal and endothelial cells contribute to the overall tumor microenvironment and metastasis in melanoma?

Key Findings ​

Research Background and Purpose ​

The study investigates the complex cellular ecosystem of metastatic melanoma using single-cell RNA sequencing (scRNA-seq). The goal is to characterize the genotypic and phenotypic diversity of melanoma tumors, understand the cellular interactions within the tumor microenvironment, and identify factors influencing treatment resistance and response to therapies.

Main Methods/Materials/Experimental Design ​

The research involved several key methodologies:

  1. Sample Collection: Single cells were isolated from 19 patients with melanoma, resulting in the profiling of 4,645 cells from malignant, immune, stromal, and endothelial lineages.

  2. Single-Cell RNA Sequencing: High-quality RNA was extracted from isolated cells for sequencing, enabling detailed transcriptomic profiling.

  3. Data Analysis:

    • Copy Number Variation (CNV) Analysis: CNVs were inferred from expression profiles to classify cells as malignant or non-malignant.
    • Dimensionality Reduction: t-distributed stochastic neighbor embedding (t-SNE) was used for visualizing cell populations.
    • Clustering: Density clustering methods were applied to identify distinct cell types and states based on gene expression profiles.

  4. Validation Techniques: Immunohistochemistry and flow cytometry were used to validate findings from the RNA-seq data.

Mermaid diagram

Key Results and Findings ​

  • Transcriptional Heterogeneity: Malignant cells exhibited diverse transcriptional states associated with the cell cycle and drug resistance. Tumors contained cells with both high MITF and low MITF/AXL expressions.
  • Microenvironmental Patterns: The study identified distinct patterns of tumor microenvironment interactions, particularly among immune cells, cancer-associated fibroblasts (CAFs), and endothelial cells.
  • T Cell Exhaustion: Analysis of tumor-infiltrating T cells revealed exhaustion programs and variability across patients, indicating that T cell states may influence treatment responses.

Main Conclusions/Significance/Innovation ​

The research highlights the complexity of the melanoma cellular ecosystem, emphasizing the significance of cellular heterogeneity in treatment response and resistance. By leveraging single-cell genomics, the study provides insights into the interactions between malignant cells and their microenvironment, which could inform future therapeutic strategies and the development of biomarkers for predicting responses to immunotherapy.

Research Limitations and Future Directions ​

  • Limitations: The study is limited by the small number of patient samples and the inherent challenges in capturing the full diversity of tumor microenvironments. Additionally, the findings are primarily correlative and require further validation in larger cohorts.
  • Future Directions: Future research should focus on longitudinal studies to track changes in the tumor ecosystem over time, especially in response to treatment. Exploring therapeutic strategies targeting specific cellular states and interactions identified in this study may enhance treatment efficacy for melanoma patients.

References ​

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Literatures Citing This Work ​

  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)

... (2585 more literatures)

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