Publication: Functional stratification of cancer drugs through integrated network similarity
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KU-Authors
KU Authors
Co-Authors
Beyge, Şeyma Ünsal
Advisor
Publication Date
2022
Language
English
Type
Journal Article
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Volume Title
Abstract
Drugs not only perturb their immediate protein targets but also modulate multiple signaling pathways. In this study, we explored networks modulated by several drugs across multiple cancer cell lines by integrating their targets with transcriptomic and phosphoproteomic data. As a result, we obtained 236 reconstructed networks covering five cell lines and 70 drugs. A rigorous topological and pathway analysis showed that chemically and functionally different drugs may modulate overlapping networks. Additionally, we revealed a set of tumor-specific hidden pathways with the help of drug network models that are not detectable from the initial data. The difference in the target selectivity of the drugs leads to disjoint networks despite sharing a similar mechanism of action, e.g., HDAC inhibitors. We also used the reconstructed network models to study potential drug combinations based on the topological separation and found literature evidence for a set of drug pairs. Overall, network-level exploration of drug-modulated pathways and their deep comparison may potentially help optimize treatment strategies and suggest new drug combinations.
Description
Source:
NPJ Systems Biology and Applications
Publisher:
Nature Publishing Group (NPG)
Keywords:
Subject
Mathematical and computational biology