LXR Agonism and Sorafenib Treatment As Novel Combination Therapy for Hepatocellular Carcinoma

˜The œFASEB journal(2019)

引用 1|浏览19
暂无评分
摘要
Hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths worldwide. Sorafenib is the only first‐line pharmaceutical approved for the treatment of HCC, and has limited efficacy. In order to identify new therapeutic options for HCC, we sought to elucidate novel druggable targets that may work in combination with sorafenib. We previously performed a genetic screen of 44 candidate genes known to contribute to hepatic regeneration or tumorigenesis. We used a mouse model of hereditary tyrosinemia as a clinically‐relevant model of liver injury and repopulation in order to sensitize mice to develop HCC. After hydrodynamic injection of 44 unique transposon plasmids, we observed HCC development with plasmid enrichment for MYC, TGFa, FOXA3, and Liver X Receptor (LXR). When mice were treated with sorafenib, tumor burden was unsurprisingly decreased. Upon analysis of sorafenib‐treated tumors, we observed all tumors contained MYC plasmids, and a majority had TGFα plasmids. However, none of the sorafenib‐resistant tumors contained LXR‐expressing plasmids, suggesting that expression of this gene may block tumorigenesis in the context of sorafenib treatment. Indeed, addition of an LXR agonist and sorafenib to various HCC cell lines decreased cell proliferation in vitro, and transcriptomic analyses identified several genes differentially expressed by combination treatment. Ongoing in vivo studies are confirming the efficacy of combinatorial therapy for potential future clinical use. Overall, our innovative paradigm allows for prospective testing of genetic combinations to determine interactions of oncogenes and tumor suppressors during carcinogenesis. We successfully employed this approach to identify LXR agonism and sorafenib as a novel combination therapy for HCCThis abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要