Abstract 4370: Network-based signatures for drug repositioning and combination for the breast tumor initiating cells

Abstract

Abstract Understanding the signaling mechanisms of breast tumor initiating cells (TIC) is important to design efficient therapeutic and management strategies of breast cancer. We present a network-based signature and a comprehensive signaling map for identification of candidates of drug repositioning and combinations for breast TIC. The network-based signature is based on an extended concept of network motifs, known as cancer-signaling bridges (CSBs), which can be used to expand the cancer drug-targets of known signaling pathways. We use the profiles of TIC derived from CD44+/CD24-/low breast cancer cells and mammospheres (MS) cells to establish network-based signatures. Facilitated by the signaling pathways that are highly connected with CSBs, e.g., MAPK, NOTCH, ECM-receptor, Jak-STAT, and Wnt, we first identify the high-confidence signaling paths automatically chosen out of CSBs by two scoring systems, namely, Differential Expression Score (DES) and Signaling Pathway Score (SPS). The high-confidence signaling paths are used to build the network-based signature that characterizes the breast TIC. The network-based signature for CD44+/CD24-/low cells is composed by 140 proteins and 132 protein-protein interactions and that for mammospheres contains 153 proteins and 119 protein-protein interactions. The FDA-approved drugs whose targets are included in the network-based signatures are repositioned to breast TIC as the candidates for the repositioning. Furthermore, we curate a comprehensive signaling map for breast TIC by using the available signaling transductions in BioCarta, KEGG, and IPA and include seven signaling pathways, PI3K/AKT, JAK/STAT, Notch, HH, Wnt, P53, and ECM. The signaling map enables us to further refine the repositioning candidates and eventually propose combination candidates by using the cross-talks of signaling pathways in the map. Using this mapping approach, the drugs targeting on TNF, KDR, and IKBKB, such as sunitinib, Arsenic trioxide, and Atorvastatin, are repositioned for treating breast TIC, while the crosstalks between pathways such as Notch+hedgehog and Hedgehog+wnt+PI3K are used to identify drug combination candidates from the target combinations, TNF+KDR, and TNF+IKBKB. Acknowledgements: This research is partially funded by NIH ICBP U54CA149169. The authors would also appreciate the discussion and advice of Jeff Rosen and David Tweardy from Baylor College of Medicine during the generation of comprehensive signaling map for breast TICs. Guangxu Jin and Hong Zhao contribute to this work equally. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4370. doi:10.1158/1538-7445.AM2011-4370