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Study of transcriptional effects of drugs on SNU-423 hepatocellular carcinoma cell lines

ABSTRACT: Hepatocellular carcinoma (HCC) is the second most common cause of cancer-related death worldwide. Like in many cancers, tumor heterogeneity in HCC hampers the development of personalized therapies. Integrative genomics contributed to characterize HCC subtypes by identifying specific genetic alterations and molecular signatures, leading to targeted drug candidates. However, no consensus was achieved for genes and pathways recurrently altered in HCC. Here, a meta-analysis of 15 independent HCC datasets identifies a comprehensive signature consisting of 935 genes commonly deregulated in HCC as compared to the surrounding non-tumor tissue (P<0.01). The 935-gene HCC signature covers well-established cancer hallmarks (e.g. proliferation, metabolic reprogramming, microenvironment remodeling) together with specific hallmarks associated with protein turnover and epigenetics. Accordingly, the 935-gene HCC signature highlights relevant drugs for systemic therapies, including including two histone deacetylase (HDAC) inhibitors (trichostatin A and vorinostat), PI3K inhibitor LY294002, mTOR inhibitor sirolimus (also known as rapamycin), alpha-estradiol and resveratrol. The impact of these drugs as compared to sorafenib that is currently used for the treatment of advanced HCC was evaluated on the viability of 6 HCC-derived cell lines. We concluded that combined therapies targeting common and subtype-specific cancer networks may represent a relevant strategy to efficiently treat liver cancer.

ORGANISM(S): Homo sapiens

PROVIDER: GSE79246 | GEO | 2016/11/15

SECONDARY ACCESSION(S): PRJNA315265

REPOSITORIES: GEO

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Study of transcriptional effects of Vorinostat, Sorafenib and Resveratrol on SNU-387 and HepG2/C3A hepatocellular carcinoma cell lines

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Project description:Hepatocellular carcinoma (HCC) is a complex and deadly disease lacking druggable genetic mutations. The limited efficacy of systemic treatments for advanced HCC implies that novel predictive biomarkers and drug targets are urgently needed. Most HCC drugs target protein kinases and their kinase-dependent signaling networks to drive HCC progression. To identify HCC signaling networks that determine responses to kinase inhibitors (KIs), we apply a pharmacoproteomics approach integrating kinome activity in 17 HCC cell lines with their responses to 299 KIs, resulting in a comprehensive dataset of pathway-based drug response signatures. By profiling patient HCC samples, we identify signatures of clinical HCC drug responses in individual tumors. Our analyses reveal kinase networks promoting the epithelial-mesenchymal transition (EMT) and drug resistance, including a FZD2-AXL-NUAK1/2 signaling module, whose inhibition reverses the EMT and sensitizes HCC cells to drugs. Our approach identifies cancer drug targets and molecular signatures of drug response for personalized oncology.

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