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Network Modeling Identifies Patient-specific Pathways in Glioblastoma
Nurcan Tuncbag1, Pamela Milani1, Jenny L Pokorny2
1Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA.
Abstract:
Glioblastoma is the most aggressive type of malignant human brain tumor. Molecular profiling experiments have revealed that these tumors are extremely heterogeneous. This heterogeneity is one of the principal challenges for developing targeted therapies. We hypothesize that despite the diverse molecular profiles, it might still be possible to identify common signaling changes that could be targeted in some or all tumors. Using a network modeling approach, we reconstruct the altered signaling pathways from tumor-specific phosphoproteomic data and known protein-protein interactions. We then develop a network-based strategy for identifying tumor specific proteins and pathways that were predicted by the models but not directly observed in the experiments. Among these hidden targets, we show that the ERK activator kinase1 (MEK1) displays increased phosphorylation in all tumors. By contrast, protein numb homolog (NUMB) is present only in the subset of the tumors that are the most invasive. Additionally, increased S100A4 is associated with only one of the tumors. Overall, our results demonstrate that despite the heterogeneity of the proteomic data, network models can identify common or tumor specific pathway-level changes. These results represent an important proof of principle that can improve the target selection process for tumor specific treatments.
Insights
This study used network modeling to find common and specific signaling targets in glioblastoma, a complex brain tumor. The findings help identify potential therapeutic strategies despite tumor heterogeneity.
Area of Science:
- Oncology
- Systems Biology
- Bioinformatics
Background:
- Glioblastoma is a highly aggressive brain tumor characterized by significant molecular heterogeneity.
- This heterogeneity poses a major challenge for developing effective targeted therapies.
- Identifying common or tumor-specific signaling alterations is crucial for treatment development.
Purpose of the Study:
- To investigate if common signaling pathways can be identified in heterogeneous glioblastoma tumors.
- To develop a network-based strategy for identifying hidden therapeutic targets.
- To improve target selection for glioblastoma treatment.
Main Methods:
- Reconstruction of altered signaling pathways using network modeling from phosphoproteomic data and protein-protein interactions.
- Development of a network-based strategy to identify unobserved, predicted targets.
- Analysis of tumor-specific proteins and pathways.
Main Results:
- Network models successfully identified common and tumor-specific pathway-level changes despite proteomic heterogeneity.
- ERK activator kinase1 (MEK1) showed increased phosphorylation in all analyzed glioblastoma tumors.
- Protein numb homolog (NUMB) was found in a subset of invasive tumors, and S100A4 was elevated in only one tumor type.
Conclusions:
- Network modeling is a viable approach to uncover conserved and specific molecular alterations in glioblastoma.
- This strategy can identify potential therapeutic targets, such as MEK1, applicable across diverse glioblastoma subtypes.
- The findings provide a proof of principle for enhancing targeted therapy selection in complex cancers.
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