Network Modeling Identifies Patient-specific Pathways in Glioblastoma
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Network modeling.pdf
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1.28 MB
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Checksum (MD5)
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Author(s)
Pokorny, Jenny L.
Sio, Terence T.
Iyekegbe, Dennis O.
Sarkaria, Jann N.
Tuncbag, Nurcan
Milani, Pamela
Johnson, Hannah
Dalin, Simona
Fraenkel, Ernest
White, Forest M.
Date Issued
June 2016
Journal
Scientific Reports
Publisher
Nature Publishing Group
Citation
Tuncbag, Nurcan et al. “Network Modeling Identifies Patient-Specific Pathways in Glioblastoma.” Scientific Reports 6 (2016): 28668.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Biology
Koch Institute for Integrative Cancer Research at MIT
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/srep28668
