Functional stratification of cancer drugs through integrated network similarity

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.

Synergistic Antitumor Effect of Naproxen and Sorafenib in Hepatocellular Carcinoma

“…With the aid of network-based analysis of omics data, it is possible to discover hidden targets that are commonly affected by drugs having different mechanisms of action. In combinatorial treatment strategies, it is suggested that drug-modulated networks should have commonalities on the altered proteins, while they should also affect different molecular pathways to be more effective against the disease (15). Thus, it is essential to investigate the network-level alterations of drugs to understand their potential synergistic effects.…”

Cancer Models on Chip: Paving the Way to Large‐Scale Trial Applications

“…The genomic stratification of cancer by integrative multi‐omic approaches provides a realistic perspective on network‐level alterations for the development of more effective therapeutic strategies to address cancer heterogeneity. [ 5 ] Classifying tumors into subtypes based on their biological hallmarks has contributed to a certain extent to a more accurate prediction of their evolution and the implementation of proper therapeutic plans. [ 6 ] The Pan‐Cancer Analysis of Whole Genomes Consortium of the International Cancer Genome Consortium and The Cancer Genome Atlas permitted the reconstruction of the life history and evolution of mutational processes and driver mutation sequences of 38 cancer types.…”

Unveiling Hidden Connections in Omics DataviapyPARAGON: an Integrative Hybrid Approach for Disease Network Construction

“…Network-based methods can uncover the most relevant interactions between a given set of proteins/genes by either inferring from a reference protein-protein interaction (PPI) network or reconstructing them 1,22,23 . These methods eventually obtain a network model which may represent the alterations in disease models or drug treatments with the help of topological and statistical features [24][25][26][27][28][29] .…”