title: Network Modeling Identifies Patient-specific Pathways in Glioblastoma.
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.
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA.
authors.forename: Nurcan
authors.initials: N
authors.lastname: Tuncbag
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA.
authors.forename: Pamela
authors.initials: P
authors.lastname: Milani
authors.affiliation: Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota 55905, USA.
authors.forename: Jenny L
authors.initials: JL
authors.lastname: Pokorny
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA.|Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA.
authors.forename: Hannah
authors.initials: H
authors.lastname: Johnson
authors.affiliation: Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota 55905, USA.
authors.forename: Terence T
authors.initials: TT
authors.lastname: Sio
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA.
authors.forename: Simona
authors.initials: S
authors.lastname: Dalin
authors.affiliation: Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota 55905, USA.
authors.forename: Dennis O
authors.initials: DO
authors.lastname: Iyekegbe
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA.|Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA.
authors.forename: Forest M
authors.initials: FM
authors.lastname: White
authors.affiliation: Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota 55905, USA.
authors.forename: Jann N
authors.initials: JN
authors.lastname: Sarkaria
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, USA.
authors.forename: Ernest
authors.initials: E
authors.lastname: Fraenkel
mesh_terms: D000818:Animals; D005909:Glioblastoma / Q000378:metabolism* / Q000473:pathology / Q000628:therapy; D006801:Humans; D051379:Mice; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000378:metabolism*; D009374:Neoplasms, Experimental / Q000378:metabolism* / Q000473:pathology / Q000628:therapy; D057285:Precision Medicine / Q000379:methods*; D015398:Signal Transduction
chemical_list: D009363:Neoplasm Proteins