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Improved prognostication of glioblastoma beyond molecular subtyping by transcriptional profiling of the tumor microenvironment

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  • Marine Jeanmougin
  • Annette B Håvik
  • Lina Cekaite
  • Petter Brandal
  • Anita Sveen
  • Torstein R Meling
  • Trude H Ågesen
  • David Scheie
  • Sverre Heim
  • Ragnhild A Lothe
  • Guro E Lind
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Glioblastoma (GBM), the most aggressive form of brain cancer, is characterized by a high level of molecular heterogeneity, and infiltration by various immune and stromal cell populations. Important advances have been made in deciphering the microenvironment of GBMs, but its association with existing molecular subtypes and its potential prognostic role remain elusive. We have investigated the abundance of infiltrating immune and stromal cells in silico, from gene expression profiles. Two cohorts, including in-house normal brain and glioma samples (n = 70) and a large sample set from TCGA (n = 393), were combined into a single exploratory dataset. A third independent cohort (n = 124) was used for validation. Tumors were clustered based on their microenvironment infiltration profiles, and associations with known GBM molecular subtypes and patient outcome were tested a posteriori in a multivariable setting. We identified a subset of GBM samples with significantly higher abundances of most immune and stromal cell populations. This subset showed increased expression of both immune suppressor and immune effector genes compared to other GBMs and was enriched for the mesenchymal molecular subtype. Survival analyses suggested that tumor microenvironment infiltration pattern was an independent prognostic factor for GBM patients. Among all, patients with the mesenchymal subtype with low immune and stromal infiltration had the poorest survival. By combining molecular subtyping with gene expression measures of tumor infiltration, the present work contributes with improving prognostic models in GBM.

Original languageEnglish
JournalMolecular Oncology
Volume14
Issue number5
Pages (from-to)1016-1027
Number of pages12
ISSN1574-7891
DOIs
Publication statusPublished - May 2020

ID: 62326539