Work

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

Year: 2026

Type: article

Abstract: Background and ObjectiveThe 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.MethodsTo address this challenge, we present a personalized pipeline for identifying patient specific treatment options. Our approach begins by identifying disease-relevant proteins unique to each patient. These proteins are integrated into a personalized molecular network, which also incorporates known drug–target interactions and proteins implicated in inhibiting disease progression. We then apply network controllability analysis to identify key regulatory targets within the network. These targets are filtered and ranked to prioritize candidate drugs for individualized treatment. After this, we compared the individual networks with a general network to highlight the differences and the need for personalized treatment.ResultsThe glioblastoma protein-specific networks showed similar dimensions and, after applying different centrality metrics, we obtained for each centrality measure approximately the same top five nodes for all networks. However, differences between the individual cases and the general network were observed. Moreover, based on our study, drugs identified as potential treatments in general networks may not be effective for specific cases and vice versa. Additionally, we track changes in patient-specific networks over time in five glioblastoma cases, illustrating how disease progression affects network structure and therapeutic recommendations.ConclusionsWe demonstrate the utility of this method using glioblastoma as a case study, showing that the protocol is generalizable to other cancer types. By comparing results across patients and against networks based on generic data, we highlight the added value of personalization.

Source: PLoS Computational Biology

DOI: 10.1371/journal.pcbi.1014785

Authors: Nicoleta Siminea, Victor-Bogdan Popescu, Mihaela Păun, Ion Petre, Andrei Păun

Institutions: University of Bucharest, National Institute of Research and Development for Biological Sciences, Revvity (United States), University of Turku, Romanian Academy

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,043