4.8 Article

Single-cell landscapes of primary glioblastomas and matched explants and cell lines show variable retention of inter- and intratumor heterogeneity

期刊

CANCER CELL
卷 40, 期 4, 页码 379-+

出版社

CELL PRESS
DOI: 10.1016/j.ccell.2022.02.016

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资金

  1. Terry Fox Research Institute [2009-20]
  2. Canadian Institutes of Health Research (CIHR) [FDN-143288]
  3. BC Cancer
  4. BC Cancer Foundation
  5. Canada Research Chairs program
  6. Canada Foundation for Innovation [20070, 30981, 33408]
  7. Genome BC
  8. Genome Canada
  9. CIHR Vanier Canada Graduate Scholarship
  10. Killam Doctoral Scholarship
  11. UBC 4-year fellowship
  12. University of British Columbia (UBC)

向作者/读者索取更多资源

Glioblastomas (GBMs) are highly heterogeneous brain tumors, and patient-derived models have been used to study this heterogeneity. However, the extent to which these models recapitulate GBM genomic features is still unclear. In this study, the researchers analyzed the genomes and transcriptomes of GBM tumors and patient-derived models, and found that the models exhibit genetic similarities to the tumors and retain varying gene expression characteristics. These findings provide valuable insights for using GBM-derived models to study cellular heterogeneity.
Glioblastomas (GBMs) are aggressive brain tumors characterized by extensive inter- and intratumor heterogeneity. Patient-derived models, such as organoids and explants, have recently emerged as useful models to study such heterogeneity, although the extent to which they can recapitulate GBM genomic features remains unclear. Here, we analyze bulk exome and single-cell genome and transcriptome profiles of 12 IDH wild-type GBMs, including two recurrent tumors, and of patient-derived explants (PDEs) and gliomasphere (GS) lines derived from these tumors. We find that PDEs are genetically similar to, and variably retain gene expression characteristics of, their parent tumors. Notably, PDEs appear to exhibit similar levels of transcriptional heterogeneity compared with their parent tumors, whereas GS lines tend to be enriched for cells in a more uniform transcriptional state. The approaches and datasets introduced here will provide a valuable resource to help guide experiments using GBM-derived models, especially in the context of studying cellular heterogeneity.

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