Glioblastoma multiforme (GBM) is the most common and aggressive type of malignant human brain tumor. Molecular profiling experiments using gene expression and proteomics have revealed that these tumors are extremely heterogeneous, and this heterogeneity is one of the principal challenges for developing targeted therapies. We hypothesized 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 reconstructed the altered signaling pathways from tumor-specific proteomic data and known protein-protein interactions. We then developed a network-based strategy for selecting therapeutic targets that were predicted by the models but not directly observed in the experiments. Overall, our results demonstrate that despite the heterogeneity of the proteomic data, network models can identify common pathway-level changes. These results represent an important proof of principle that can improve the target selection process for personalized medicine.