The multilayered transcriptional architecture of glioblastoma ecosystems.

in Nature genetics by Masashi Nomura, Avishay Spitzer, Kevin C Johnson, Luciano Garofano, Djamel Nehar-Belaid, Noam Galili Darnell, Alissa C Greenwald, Lillian Bussema, Young Taek Oh, Frederick S Varn, Fulvio D'Angelo, Simon Gritsch, Kevin J Anderson, Simona Migliozzi, L Nicolas Gonzalez Castro, Tamrin ChowdhFury, Nicolas Robine, Catherine Reeves, Jong Bae Park, Anuja Lipsa, Frank Hertel, Anna Golebiewska, Simone P Niclou, Labeeba Nusrat, Sorcha Kellet, Sunit Das, Hyo Eun Moon, Sun Ha Paek, Franck Bielle, Alice Laurenge, Anna Luisa Di Stefano, Bertrand Mathon, Alberto Picca, Marc Sanson, Shota Tanaka, Nobuhito Saito, David M Ashley, Stephen T Keir, Keith L Ligon, Jason T Huse, W K Alfred Yung, Anna Lasorella, Roel G W Verhaak, Antonio Iavarone, Mario L Suvà, Itay Tirosh

TLDR

  • Researchers analyzed 121 GBM samples to understand the complex transcriptional landscape of the disease and identified three stereotypic ecosystems based on cellular composition, cellular states, and gene expression programs.

Abstract

In isocitrate dehydrogenase wildtype glioblastoma (GBM), cellular heterogeneity across and within tumors may drive therapeutic resistance. Here we analyzed 121 primary and recurrent GBM samples from 59 patients using single-nucleus RNA sequencing and bulk tumor DNA sequencing to characterize GBM transcriptional heterogeneity. First, GBMs can be classified by their broad cellular composition, encompassing malignant and nonmalignant cell types. Second, in each cell type we describe the diversity of cellular states and their pathway activation, particularly an expanded set of malignant cell states, including glial progenitor cell-like, neuronal-like and cilia-like. Third, the remaining variation between GBMs highlights three baseline gene expression programs. These three layers of heterogeneity are interrelated and partially associated with specific genetic aberrations, thereby defining three stereotypic GBM ecosystems. This work provides an unparalleled view of the multilayered transcriptional architecture of GBM. How this architecture evolves during disease progression is addressed in the companion manuscript by Spitzer et al.

Overview

  • The study analyzed 121 primary and recurrent GBM samples from 59 patients using single-nucleus RNA sequencing and bulk tumor DNA sequencing to characterize GBM transcriptional heterogeneity.
  • The study aimed to explore the multilayered transcriptional architecture of GBM and identify the regulatory mechanisms underlying its evolution during disease progression.
  • The study divided GBMs into three stereotypic ecosystems based on their broad cellular composition, diversity of cellular states, and baseline gene expression programs, highlighting the importance of understanding the complex transcriptional landscape of GBM.

Comparative Analysis & Findings

  • The study identified three layers of heterogeneity in GBMs: broad cellular composition, diversity of cellular states and their pathway activation, and baseline gene expression programs.
  • The results showed that GBMs can be classified into three stereotypic ecosystems based on their cellular composition, which was partially associated with specific genetic aberrations.
  • The study identified an expanded set of malignant cell states, including glial progenitor cell-like, neuronal-like, and cilia-like states, which were not previously characterized in GBM.

Implications and Future Directions

  • The study provides a comprehensive understanding of the transcriptional heterogeneity in GBM, which can inform the development of more targeted and effective therapeutic strategies.
  • The study highlights the importance of considering the complex transcriptional landscape of GBM in the development of novel diagnostic and therapeutic approaches.
  • Future studies should focus on identifying the regulatory mechanisms underlying the evolution of GBM transcriptional heterogeneity during disease progression and developing strategies to target specific transcriptional programs.
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