Despite decades of genomics studies aimed at identifying new therapeutic targets to improve clinical outcomes, the five-year survival rate for patients diagnosed with HGGs remains below 5%. The recent multi-omics study published in Cancer Cell has provided groundbreaking insights into the evolution of high-grade gliomas (HGGs): IDH-wildtype glioblastoma and IDH-mutated astrocytoma.
By integrating data from proteomics, metabolomics, lipidomics, and post-translational modifications with genomic and transcriptomic analyses, researchers have painted a detailed picture of the molecular landscape of HGGs. Here are the key takeaways from the study:
- Molecular Features of HGGs: The study uncovered specific molecular features in HGGs, such as tumors with distinct alterations (TERTp/PTEN and TERTp/EGFR) produced similar metabolomic, glycoproteomic and PTM profiles. The study also emphasized the pivotal role of PTPN11 signaling, which connects EGFR, PDGFR, and IDH1 to their downstream effectors. Notably, IDH-mutant HGGs showed a low hypoxia signature and reduced AMPKA activities, distinguishing them from other forms.
- Tumor Microenvironment (TME) Remodeling: Utilizing the Akoya PhenoCycler platform, the study provided crucial spatial information on the TME’s role in the evolution of these tumors. Recurrence was linked to significant changes in the TME, including altered proliferation rates and the enrichment of specific cell types in recurrent HGGs.
- Dysregulated Hypoxia in HGG: The research identified dysregulated hypoxia signaling in IDH-mutant HGGs. Using the PhenoCycler platform, researchers observed a higher expression of SERPINE1 in IDH-WT but not in IDH-mutant tumors, with macrophages and malignant cells being the primary expressors of SERPINE1. This suggests a more active HIF1A pathway in IDH-WT tumors.
| Antibody | Expected cell type | |
|---|---|---|
| Immune lymphoid | DAPI | Nuclei |
| CD45 | Leukocyte | |
| CD3e | T cell | |
| CD4 | CD4+ T cell | |
| CD8 | CD8+ T cell | |
| FOXP3 | Treg cell | |
| CD20 | B cell | |
| Immune myeloid | CD68 | Macrophage |
| CD163 | M2 Macrophage | |
| IBA1 | Microglia | |
| CD11b | Pan-monocyte, pan-granulocyte | |
| HLA-DR | APC | |
| Malignant / Stroma | PTPRZ1 | GBM tumor |
| OLIG2 | GBM tumor | |
| PanCytokeratin | Epithelium (met tumor) | |
| GFAP | Astrocyte / GBM tumor | |
| CD31 | Endothelial (this cytoplasmic) | |
| Functional | Vimentin | mesenchymal |
| Ki67 | Proliferation | |
| GLUT1 | Hypoxia | |
| VEGFA | Hypoxia | |
| PAI1/SERPINE1 | Hypoxia | |
| HIF1A | Hypoxia | |
| FN1 | Hypoxia | |
| PTPN11-Y546 | Phosphorylated PTPN11 |
Table 1. PhenoCycler antibodies used in the multiplexed analysis of brain tissue.
A New View of HGG
Overall, this comprehensive integration of multi-omic data not only reveals the dynamic changes in HGGs as they evolve under treatment but also highlights potential new targets for therapeutic intervention, which is vital for improving patient outcomes in this challenging disease area.
To learn more:
- View the publication, “Multi-scale signaling and tumor evolution in high-grade gliomas”
- See how the PhenoCycler-Fusion 2.0 solution is helping provide insights into complex biological systems and gain a better understanding of cellular microenvironments
- Join us at an upcoming event near you or connect with us to discuss how Akoya’s spatial biology solutions can help you advance your research.
