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Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. ​

Literature Information ​

DOI10.1126/science.1254257
PMID24925914
JournalScience (New York, N.Y.)
Impact Factor45.8
JCR QuartileQ1
Publication Year2014
Times Cited2707
Keywordssingle-cell RNA sequencing, glioblastoma, intratumoral heterogeneity, transcriptional programs, prognosis
Literature TypeJournal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't
ISSN0036-8075
Pages1396-401
Issue344(6190)
AuthorsAnoop P Patel, Itay Tirosh, John J Trombetta, Alex K Shalek, Shawn M Gillespie, Hiroaki Wakimoto, Daniel P Cahill, Brian V Nahed, William T Curry, Robert L Martuza, David N Louis, Orit Rozenblatt-Rosen, Mario L Suvà, Aviv Regev, Bradley E Bernstein

TL;DR ​

This study utilized single-cell RNA sequencing to analyze 430 cells from five primary glioblastomas, revealing significant intratumoral heterogeneity in transcriptional programs related to oncogenesis, immune response, and stemness. The findings highlight the complexity of glioblastoma biology and suggest that current subtype classifiers may not accurately reflect the diverse cellular landscapes within tumors, which has important implications for prognosis and targeted therapies.

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single-cell RNA sequencing · glioblastoma · intratumoral heterogeneity · transcriptional programs · prognosis

Abstract ​

Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single-cell RNA sequencing (RNA-seq) to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy.

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Primary Questions Addressed ​

  1. How does intratumoral heterogeneity influence the effectiveness of targeted therapies in glioblastoma?
  2. What are the implications of the identified stemness-related expression states for the development of new treatment strategies?
  3. In what ways could single-cell RNA-seq technology be applied to study other types of cancers with similar heterogeneity?
  4. How do the diverse transcriptional programs identified in glioblastoma correlate with patient outcomes and survival rates?
  5. What are the potential challenges in integrating single-cell RNA-seq findings into clinical practice for glioblastoma management?

Key Findings ​

Research Background and Objectives ​

Glioblastoma is a highly heterogeneous and lethal brain tumor characterized by diverse cellular phenotypes and genotypes. This study aims to investigate the intratumoral heterogeneity of glioblastoma using single-cell RNA sequencing (scRNA-seq) to provide insights into its complex biology, regulatory programs, and potential implications for prognosis and therapy.

Main Methods/Materials/Experimental Design ​

The researchers employed single-cell RNA sequencing to analyze 430 individual cells from five freshly resected human glioblastoma tumors. The workflow involved:

  1. Sample Collection: Tumor samples were obtained from patients, and cells were dissociated and sorted.
  2. CD45+ Cell Depletion: To minimize contamination from inflammatory cells, CD45+ cells were removed.
  3. Single-Cell RNA Sequencing: The SMART-seq protocol was used to generate full-length RNA profiles from individual cells.
  4. Data Analysis: Hierarchical clustering and principal component analysis were utilized to assess gene expression profiles and identify patterns of intratumoral heterogeneity.
Mermaid diagram

Key Results and Findings ​

  • Intratumoral Heterogeneity: Significant variability was observed in the expression of genes related to oncogenic signaling, immune response, and hypoxia across individual tumor cells.
  • Stemness Gradients: A continuum of stemness-related expression states was identified, with potential regulators of stemness revealed in vivo.
  • Mosaic Expression of Receptor Tyrosine Kinases (RTKs): Variability in expression of RTKs (e.g., EGFR, PDGFRA) was noted, which may contribute to resistance against targeted therapies.
  • Subtypes Distribution: Each tumor contained a mixture of glioblastoma subtypes (proneural, classical, mesenchymal, neural), indicating that population-level classifications may not capture the true diversity present within individual tumors.
  • Prognostic Implications: Increased intratumoral heterogeneity was associated with poorer survival outcomes, particularly in proneural tumors.

Main Conclusions/Significance/Innovation ​

This study highlights the complexity of glioblastoma by revealing substantial intratumoral heterogeneity in gene expression and regulatory programs. The findings suggest that individual glioblastoma tumors contain a spectrum of cellular states that can influence therapeutic responses and clinical outcomes. This underscores the need for more refined models of glioblastoma that consider this heterogeneity to develop effective treatment strategies.

Research Limitations and Future Directions ​

  • Limitations: The study primarily focuses on transcriptional heterogeneity and does not fully address genetic mutations and focal alterations that contribute to tumor diversity. Additionally, the single-cell approach may miss interactions within the tumor microenvironment.
  • Future Directions: Further research is needed to explore the spatial organization of heterogeneous cell populations within tumors and to investigate how these dynamics affect treatment resistance and tumor evolution. Additionally, integrating genetic data with transcriptomic profiles could provide a more comprehensive understanding of glioblastoma biology.

References ​

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Literatures Citing This Work ​

  1. In vivo models of brain tumors: roles of genetically engineered mouse models in understanding tumor biology and use in preclinical studies. - Iva Simeonova;Emmanuelle Huillard - Cellular and molecular life sciences : CMLS (2014)
  2. MethylPurify: tumor purity deconvolution and differential methylation detection from single tumor DNA methylomes. - Xiaoqi Zheng;Qian Zhao;Hua-Jun Wu;Wei Li;Haiyun Wang;Clifford A Meyer;Qian Alvin Qin;Han Xu;Chongzhi Zang;Peng Jiang;Fuqiang Li;Yong Hou;Jianxing He;Jun Wang;Jun Wang;Peng Zhang;Yong Zhang;Xiaole Shirley Liu - Genome biology (2014)
  3. Are all cancer stem cells created equal? - Xiujie Xie;Theodoros N Teknos;Quintin Pan - Stem cells translational medicine (2014)
  4. Brain tumor cells in circulation are enriched for mesenchymal gene expression. - James P Sullivan;Brian V Nahed;Marissa W Madden;Samantha M Oliveira;Simeon Springer;Deepak Bhere;Andrew S Chi;Hiroaki Wakimoto;S Michael Rothenberg;Lecia V Sequist;Ravi Kapur;Khalid Shah;A John Iafrate;William T Curry;Jay S Loeffler;Tracy T Batchelor;David N Louis;Mehmet Toner;Shyamala Maheswaran;Daniel A Haber - Cancer discovery (2014)
  5. Abrogation of radioresistance in glioblastoma stem-like cells by inhibition of ATM kinase. - Ross Carruthers;Shafiq U Ahmed;Karen Strathdee;Natividad Gomez-Roman;Evelyn Amoah-Buahin;Colin Watts;Anthony J Chalmers - Molecular oncology (2015)
  6. Cancer genomics: one cell at a time. - Nicholas E Navin - Genome biology (2014)
  7. Contemporary murine models in preclinical astrocytoma drug development. - Robert S McNeill;Mark Vitucci;Jing Wu;C Ryan Miller - Neuro-oncology (2015)
  8. NF-κB and STAT3 in glioblastoma: therapeutic targets coming of age. - G Kenneth Gray;Braden C McFarland;Susan E Nozell;Etty N Benveniste - Expert review of neurotherapeutics (2014)
  9. Modularity and predictability in cell signaling and decision making. - Oguzhan Atay;Jan M Skotheim - Molecular biology of the cell (2014)
  10. Cancer genomic research at the crossroads: realizing the changing genetic landscape as intratumoral spatial and temporal heterogeneity becomes a confounding factor. - Shengwen Calvin Li;Lisa May Ling Tachiki;Mustafa H Kabeer;Brent A Dethlefs;Michael J Anthony;William G Loudon - Cancer cell international (2014)

... (2697 more literatures)

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