Network Modeling Identifies Patient-specific Pathways in Glioblastoma

Nurcan Tuncbag, Pamela Milani, Jenny L. Pokorny, Hannah Johnson, Terence T. Sio, Simona Dalin, Dennis O. Iyekegbe, Forest M. White, Jann N Sarkaria, Ernest Fraenkel

Research output: Contribution to journalArticle

13 Citations (Scopus)

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.

Original languageEnglish (US)
Article number28668
JournalScientific Reports
Volume6
DOIs
StatePublished - Jun 29 2016
Externally publishedYes

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Glioblastoma
Neoplasms
Proteins
Patient-Specific Modeling
Brain Neoplasms
Proteomics
Phosphorylation

ASJC Scopus subject areas

  • General

Cite this

Tuncbag, N., Milani, P., Pokorny, J. L., Johnson, H., Sio, T. T., Dalin, S., ... Fraenkel, E. (2016). Network Modeling Identifies Patient-specific Pathways in Glioblastoma. Scientific Reports, 6, [28668]. https://doi.org/10.1038/srep28668

Network Modeling Identifies Patient-specific Pathways in Glioblastoma. / Tuncbag, Nurcan; Milani, Pamela; Pokorny, Jenny L.; Johnson, Hannah; Sio, Terence T.; Dalin, Simona; Iyekegbe, Dennis O.; White, Forest M.; Sarkaria, Jann N; Fraenkel, Ernest.

In: Scientific Reports, Vol. 6, 28668, 29.06.2016.

Research output: Contribution to journalArticle

Tuncbag, N, Milani, P, Pokorny, JL, Johnson, H, Sio, TT, Dalin, S, Iyekegbe, DO, White, FM, Sarkaria, JN & Fraenkel, E 2016, 'Network Modeling Identifies Patient-specific Pathways in Glioblastoma', Scientific Reports, vol. 6, 28668. https://doi.org/10.1038/srep28668
Tuncbag N, Milani P, Pokorny JL, Johnson H, Sio TT, Dalin S et al. Network Modeling Identifies Patient-specific Pathways in Glioblastoma. Scientific Reports. 2016 Jun 29;6. 28668. https://doi.org/10.1038/srep28668
Tuncbag, Nurcan ; Milani, Pamela ; Pokorny, Jenny L. ; Johnson, Hannah ; Sio, Terence T. ; Dalin, Simona ; Iyekegbe, Dennis O. ; White, Forest M. ; Sarkaria, Jann N ; Fraenkel, Ernest. / Network Modeling Identifies Patient-specific Pathways in Glioblastoma. In: Scientific Reports. 2016 ; Vol. 6.
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