Abstract A137: Combinatorial therapy design in cancer as a directed attack on the hubs of a scale-free network.

Abstract

Abstract Scale-free networks, characterized by a power-law degree distribution, are known to be resilient to random failures, yet vulnerable to directed attacks on their hubs (their most connected nodes). Here, we show that protein-protein interaction networks in cancer are scale-free, validate the effectiveness of hub attacks using published RNAi lethality screens conducted on human cancer cells, and propose a novel approach for combinatorial hub attacks. Methods: Proteins involved in 14 different cancer types were extracted from the KEGG database. For each cancer type, a network was constructed by incorporating the experimentally-validated physical interactions of the pertinent KEGG proteins from the BioGRID interaction network. To validate the presence of scale-free cancer networks, we fit the degree distribution of the full network to a power-law. Then, we quantified the degree distribution of the lethality proteins discovered from RNAi screens (conducted on human multiple myeloma and epithelial ovarian cancer cells) and compared it to the rest of the network. A previously-derived mathematical relationship between the destruction of the giant component (a metric of network integrity) and the fraction of hubs removed was applied to the cancer networks to investigate the utility of combination therapy. Results: In agreement with previous studies of many biological networks, we found that the 14 cancer types listed in KEGG all demonstrated scale-free behavior. We analyzed previously published, genome-wide RNAi lethality screens in two cancer cells, and found a significant enrichment (≈ 5-fold) of median degree-connectivity among lethal proteins compared to all the proteins in the network. As expected, for a scale-free network, the removal of < 5% of the proteins completely destroyed network integrity. In the case of basal-cell carcinoma, as few as 9 proteins were responsible for the integrity of the entire network. Also, the marginal utility of each successive hub knockout for network destruction remained relatively constant, consistent with a combinatorial approach in tackling cancer. Conclusion: In this work, we demonstrate that cancer networks are scale-free. The correlation between connectivity and lethality in the investigated cancer networks suggests that the number of neighbor-interactions of a protein may determine its potency as a drug target. We demonstrate a greedy algorithm for combinatorial therapy design based on the principle of simultaneous targeting of high-connectivity nodes. As the network of proteins responsible for toxicity in response to anticancer agents is also likely to be scale-free (and may be uncorrelated with cancer protein networks) the targeted destruction of cancer network hubs may in effect be a random attack on the toxicity nodes. This suggests that a combinatorial approach of targeting multiple proteins with high connectivity in cancer networks may be effective in creating an optimal therapeutic window. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A137. Citation Format: Andrew Chen, Arijit Chakravarty, Jerome Mettetal, Wen Chyi Shyu, Joseph Bolen, Santhosh Palani. Combinatorial therapy design in cancer as a directed attack on the hubs of a scale-free network. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A137.