Proteogenomics of glioblastoma associates molecular patterns with survival

Gali Yanovich-Arad, Paula Ofek, Eilam Yeini, Mariya Mardamshina, Artem Danilevsky, Noam Shomron, Rachel Grossman, Ronit Satchi-Fainaro, Tamar Geiger

Research output: Contribution to journalArticlepeer-review

Abstract

Glioblastoma (GBM) is the most aggressive form of glioma, with poor prognosis exhibited by most patients, and a median survival time of less than 2 years. We assemble a cohort of 87 GBM patients whose survival ranges from less than 3 months and up to 10 years and perform both high-resolution mass spectrometry proteomics and RNA sequencing (RNA-seq). Integrative analysis of protein expression, RNA expression, and patient clinical information enables us to identify specific immune, metabolic, and developmental processes associated with survival as well as determine whether they are shared between expression layers or are layer specific. Our analyses reveal a stronger association between proteomic profiles and survival and identify unique protein-based classification, distinct from the established RNA-based classification. By integrating published single-cell RNA-seq data, we find a connection between subpopulations of GBM tumors and survival. Overall, our findings establish proteomic heterogeneity in GBM as a gateway to understanding poor survival. Yanovich-Arad et al. perform a proteogenomic analysis of IDH-WT GBM tumors in which they combine proteomics, transcriptomics, and patient clinical information. Integrative analysis generates proteomic tumor subtypes different from transcriptomic subtypes and identifies biological processes associated with survival that are either common to RNA and protein or are expression-level specific.

Original languageEnglish
Article number108787
JournalCell Reports
Volume34
Issue number9
DOIs
StatePublished - 2 Mar 2021

Keywords

  • RNA-sequencing
  • cancer heterogeneity
  • glioblastoma
  • mass spectrometry
  • proteogenomics
  • proteomics

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