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Study of transcriptional effects of Vorinostat, Sorafenib and Resveratrol on SNU-387 and HepG2/C3A 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: GSE85257 | GEO | 2016/11/15

SECONDARY ACCESSION(S): PRJNA337958

REPOSITORIES: GEO

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

Project description: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.

2016-11-15 | GSE79246 | GEO

Transcriptomics-based drug repurposing approach identifies novel drugs against sorafenib-resistant hepatocellular carcinoma

Project description:Hepatocellular carcinoma (HCC) is frequently diagnosed in patients with late-stage disease who are ineligible for curative surgical therapies. Furthermore, the majority of patients become resistant to sorafenib. Recently, computational methods for drug repurposing have shown great promise to accelerate the discovery of new uses for existing drugs. In order to identify novel drugs for use against sorafenib resistant (SR)-HCC, we employed a transcriptomics-based drug repurposing method termed connectivity mapping. We conducted a comprehensive analysis of available in vitro and in vivo gene signatures of (SR)-HCC, and generated our own in vitro model using the Huh7 HCC cell line. We compared coverage of SR-HCC gene signatures across seven patient-derived HCC gene expression datasets, and observed that patients harboring the Huh7 SR-HCC gene signature had significantly reduced survival. Utilizing the Huh7 SR-HCC gene signature, we applied connectivity mapping to drug-induced gene expression profiles (n= 3,740 drugs) in the HepG2 HCC cell line from the LINCS database in order to find drugs that could oppose sorafenib resistance. We validated the use of two non-receptor tyrosine kinase inhibitors, dasatinib and fostamatinib, to reduce viability of sorafenib-resistant HCC cells and confirmed up-regulated activity of Src family kinases, the targets of dasatinib, in our SR-HCC models. We prospectively validated predicted gene expression changes in fostamatinib treated Huh7-SR via RNA-seq analysis.

2019-09-01 | GSE113005 | GEO

Identification of Oncogenic Potential of SIRT7 and Its Regulatory MicroRNAs in Liver Tumorigenesis

Project description:To investigate the specific roles of SIRT7 in the development of HCC, we employed large-scale gene expression analysis to identify the molecular signature that may affect enabling characteristics of cancer cells. Differentially expressed genes were analyzed on the Hep3B cells transfected with SIRT7 shRNAs, and recapitulated molecular signatures that related to hallmarks of cancer.

2012-03-01 | GSE33234 | GEO

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Pharmacoproteomics identifies kinase pathways that drive the epithelial-mesenchymal transition and drug resistance in hepatocellular carcinoma

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.

2018-12-15 | MSV000083236 | MassIVE

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2016-10-18 | GSE88812 | GEO

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Project description:Background & Aims: Hepatocellular carcinoma (HCC) is the second most lethal cancer due to lack of effective therapies. Although promising, HCC molecular classification, which enriches potential responders to specific therapies, has not yet been assessed in clinical trials of anti-HCC drugs. We aimed to overcome these challenges by developing clinicopathological surrogate indices of HCC molecular classification. Methods: HCC classification defined in our previous transcriptome meta-analysis (S1, S2, and S3 subclasses) was implemented in an FDA-approved diagnostic platform (Elements assay, NanoString). Ninety-six HCC tumors (training set) were assayed to develop molecular subclass-predictive indices based on clinicopathological features, which were independently validated in 99 HCC tumors (validation set). Molecular deregulations associated with the histopathological features were determined by pathway analysis. Sample sizes for HCC clinical trials enriched with specific molecular subclasses were determined. Results: HCC subclass-predictive indices were: steatohepatitic (SH)-HCC variant and immune cell infiltrate for S1 subclass, macrotrabecular/compact pattern, lack of pseudoglandular pattern, and high serum alpha-fetoprotein (>400 ng/mL) for S2 subclass, and microtrabecular pattern, lack of SH-HCC and clear cell variants, and lower histological grade for S3 subclass. Macrotrabecular/compact pattern, a predictor of S2 subclass, was associated with activation of therapeutically targetable oncogene YAP and stemness markers EPCAM/KRT19. BMP4 was associated with pseudoglandular pattern. Subclass-predictive indices-based patient enrichment reduced clinical trial sample sizes from 121, 184, and 53 to 30, 43, and 22 for S1, S2, and S3 subclass-targeting therapies, respectively. Conclusions: HCC molecular subclasses can be enriched by clinicopathological indices tightly associated with deregulation of therapeutically targetable molecular pathways.

2015-06-17 | GSE59548 | GEO

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2017-06-29 | GSE92589 | GEO

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