Literature DB >> 24097821

Novel modeling of cancer cell signaling pathways enables systematic drug repositioning for distinct breast cancer metastases.

Hong Zhao1, Guangxu Jin, Kemi Cui, Ding Ren, Timothy Liu, Peikai Chen, Solomon Wong, Fuhai Li, Yubo Fan, Angel Rodriguez, Jenny Chang, Stephen T C Wong.   

Abstract

A new type of signaling network element, called cancer signaling bridges (CSB), has been shown to have the potential for systematic and fast-tracked drug repositioning. On the basis of CSBs, we developed a computational model to derive specific downstream signaling pathways that reveal previously unknown target-disease connections and new mechanisms for specific cancer subtypes. The model enables us to reposition drugs based on available patient gene expression data. We applied this model to repurpose known or shelved drugs for brain, lung, and bone metastases of breast cancer with the hypothesis that cancer subtypes have their own specific signaling mechanisms. To test the hypothesis, we addressed specific CSBs for each metastasis that satisfy (i) CSB proteins are activated by the maximal number of enriched signaling pathways specific to a given metastasis, and (ii) CSB proteins are involved in the most differential expressed coding genes specific to each breast cancer metastasis. The identified signaling networks for the three types of breast cancer metastases contain 31, 15, and 18 proteins and are used to reposition 15, 9, and 2 drug candidates for the brain, lung, and bone metastases. We conducted both in vitro and in vivo preclinical experiments as well as analysis on patient tumor specimens to evaluate the targets and repositioned drugs. Of special note, we found that the Food and Drug Administration-approved drugs, sunitinib and dasatinib, prohibit brain metastases derived from breast cancer, addressing one particularly challenging aspect of this disease. ©2013 AACR.

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Year:  2013        PMID: 24097821      PMCID: PMC4005386          DOI: 10.1158/0008-5472.CAN-12-4617

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  46 in total

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