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Updated: Jan 10, 2026
Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
Published on: January 9, 2019
Integrated pathway analysis identifies prognostically relevant subtypes of glioblastoma characterized by
Pei Zhang1, Dan Liu1, Tonghui Yu2
1Advanced Technology Research Institute, State Key Laboratory of Molecular Medicine and Biological Diagnosis and Treatment (Ministry of Industry and Information Technology), School of Life Science, Beijing Institute of Technology, Beijing, China.
Background:
Gene expression-based molecular subtypes in glioblastoma from The Cancer Genome Atlas Network (TCGA-GBM) unraveled the pathological origins by identifying tumour cell driver genes. However, the causal inference between molecular subtype origins and their therapeutic efficacy remains obscure.
Methods:
We integrated TCGA-GBM multi-omics (DNA, mRNA, and protein profiles) using correlation analysis to identify cis-regulation. We analyzed the exposure-mediated base substitution-level mutations and their potential triggers. Importantly, we performed Consensus Clustering based on the MSigDB database with Silhouette-correction to identify prognostically relevant pathway-based MSig subtypes. The tumour driver mutations (co-occurrence mutation pattern), aberrant pathways (tumour hallmarks), immune microenvironment (xCell), and pseudo-time analysis (dyno) were used to characterize the MSig subtype landscape. Furthermore, we evaluated potential drug sensitivities across MSig subtypes using the Genomics of Drug Sensitivity in Cancer database.
Results:
We classified five MSig subtypes, characterized by neural-like, tumour-driving, low tumour evolution, immune-inflamed, and classical tumour features. We observed several key features in 'tumour-driving' GBM patients: (1) mutual exclusivity between prognostic factors TP53 and EGFR; and (2) IDH1 mutations co-occurring with TP53, which account for the protective role of IDH1 in TP53 mutant patients. The immune-inflamed GBM, characterized as a 'hot' tumour, exhibited upregulation of immune-related pathways, including PD-1 and IFN-γ signalling responses. DNA methylation landscape revealed 14 MGMT CpG-rich regions regulating expression. Evolutionary trajectories revealed progression from a primary tumour state (close to normal tissue) to two distinct endpoints (tumour-driving and immune-inflamed subtypes).
Conclusions:
Our findings reveal interactions between tumour cells and their surrounding immune environment, classifying GBM into two newly identified subtypes: (1) the tumour-driving subtype is characterized by multiple oncogenic mutations, while (2) the immune-blockade subtype is marked by a high presence of immune cells. We highlight the importance of integrating multi-type data (somatic mutations, DNA methylation, and RNA transcripts, etc.) to decipher GBM biology and potential therapeutic implications.
Highlights:
We report the interaction between tumor cells and environmental immune cells, classifying GBM into two main subtypes: 1) The tumor-driving subtype is characterized by multiple oncogenic mutations, while 2) the immune-blockage subtype is marked by a high presence of immune cells. We used integrated multidimensional analyses of somatic mutations, DNA methylation, and RNA transcripts to gain a deeper understanding of GBM biology and potential therapeutic implications.
Insights
Glioblastoma (GBM) can be classified into two new subtypes: tumor-driving, with many mutations, and immune-blockade, with many immune cells. This research integrates multi-omics data to understand GBM biology and guide therapy.
Area of Science:
- Oncology
- Genomics
- Immunology
Background:
- Glioblastoma (GBM) molecular subtypes identified by The Cancer Genome Atlas Network (TCGA-GBM) revealed tumor cell driver genes but lacked causal links to therapeutic efficacy.
- Understanding the interplay between molecular origins and clinical outcomes is crucial for developing targeted GBM therapies.
Purpose of the Study:
- To integrate multi-omics data for a deeper understanding of Glioblastoma (GBM) biology.
- To identify novel GBM subtypes based on pathway analysis and their prognostic relevance.
- To explore potential drug sensitivities across different GBM subtypes.
Main Methods:
- Integrated multi-omics data (DNA, mRNA, protein) from TCGA-GBM using correlation analysis for cis-regulation.
- Performed Consensus Clustering using the MSigDB database to identify prognostically relevant pathway-based MSig subtypes.
- Analyzed tumor driver mutations, aberrant pathways, immune microenvironment, and evolutionary trajectories; evaluated drug sensitivities using the Genomics of Drug Sensitivity in Cancer database.
Main Results:
- Classified five MSig subtypes: neural-like, tumor-driving, low tumor evolution, immune-inflamed, and classical.
- Identified distinct features in 'tumor-driving' GBM, including TP53/EGFR mutual exclusivity and IDH1/TP53 co-occurrence.
- The 'immune-inflamed' subtype showed 'hot' tumor characteristics with upregulated immune-related pathways (e.g., PD-1, IFN-γ); evolutionary analysis revealed progression to tumor-driving and immune-inflamed endpoints.
Conclusions:
- Classified GBM into two novel subtypes: 'tumor-driving' (multiple oncogenic mutations) and 'immune-blockade' (high immune cell presence), highlighting tumor-immune microenvironment interactions.
- Emphasized the importance of integrating multi-type data (somatic mutations, DNA methylation, RNA transcripts) for deciphering GBM biology.
- Findings provide potential therapeutic implications for distinct GBM subtypes.
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