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Functional stratification of cancer drugs through integrated network similarity
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Date
2022-04-01
Author
Beyge, Seyma Unsal
Tunçbağ, Nurcan
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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.
URI
https://hdl.handle.net/11511/97289
Journal
NPJ SYSTEMS BIOLOGY AND APPLICATIONS
DOI
https://doi.org/10.1038/s41540-022-00219-8
Collections
Graduate School of Informatics, Article
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S. U. Beyge and N. Tunçbağ, “Functional stratification of cancer drugs through integrated network similarity,”
NPJ SYSTEMS BIOLOGY AND APPLICATIONS
, vol. 8, no. 1, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/97289.