bioRxivSystems BiologyPreprint
Scale-free structure of cancer networks and their vulnerability to hub-directed combination therapy
Andrew CheniD, Christopher J. Zopf, Jerome Thomas MettetaliD, Wen Chyi Shyu, Joseph B. Bolen, Arijit ChakravartyiD, Santhosh Palani
Abstract
Background The effectiveness of many targeted therapies is limited by toxicity and the rise of drug resistance. A growing appreciation of the inherent redundancies of cancer signaling has led to a rise in the number of combination therapies under development, but a better understanding of the overall cancer network topology would provide a conceptual framework for choosing effective combination partners. In this work, we explore the scale-free nature of cancer protein-protein interaction networks in 14 indications. Scale-free networks, characterized by a power-law degree distribution, are known to be resilient to random attack on their nodes, yet vulnerable to directed attacks on their hubs (their most highly connected nodes).Results Consistent with the properties of scale-free networks, we find that lethal genes are associated with ∼5-fold higher protein connectivity partners than non-lethal genes. This provides a biological rationale for a hub-centered combination attack. Our simulations show that combinations targeting hubs can efficiently disrupt 50% of network integrity by inhibiting less than 1% of the connected proteins, whereas a random attack can require inhibition of more than 30% of the connected proteins.Conclusions We find that the scale-free nature of cancer networks makes them vulnerable to focused attack on their highly connected protein hubs. Thus, we propose a new strategy for designing combination therapies by targeting hubs in cancer networks that are not associated with relevant toxicity networks.Competing Interest StatementThe authors have declared no competing interest.View Full Text
- organism: human
- assay: protein sequence
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- research focus: perturbation response prediction
- approach: statistical model
- Citations
- 3
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