In-silico personalized protein-protein interaction networks prioritize candidate compounds for glioblastoma.

The incidence of cancer continues to rise globally, with many tumor types remaining difficult to treat effectively. While existing drugs can alleviate symptoms or slow disease progression, their efficacy varies considerably between patients. To explore this challenge, we present a computational approach for identifying patient-specific candidate compounds. Our approach was developed for glioblastoma, but it can be applied to other types of cancer. Our study involves identifying possible disease-relevant proteins specific to each patient that underlie the generated networks, together with drug targets and proteins associated with cancer dependency. These networks are analyzed using centrality measures and Louvain community detection method. Then, further investigating the individual networks, network controllability analysis is applied to identify key regulatory targets within the network. These targets are filtered and ranked to prioritize candidate drugs for individualized treatment. By comparing results across patients and against networks based on generic data, we show substantial differences between patient-specific and generic network representations. Additionally, we track changes in patient-specific networks over time in five glioblastoma cases, exploring how molecular differences between primary and recurrent tumors may influence network structure and computational drug prioritization.