Small molecule inhibitors of Late SV40 Factor (LSF) abrogate hepatocellular carcinoma (HCC): Evaluation using an endogenous HCC model.

Hepatocellular carcinoma (HCC) is a lethal malignancy with high mortality and poor prognosis. Oncogenic transcription factor Late SV40 Factor (LSF) plays an important role in promoting HCC. A small molecule inhibitor of LSF, Factor Quinolinone Inhibitor 1 (FQI1), significantly inhibited human HCC xenografts in nude mice without harming normal cells. Here we evaluated the efficacy of FQI1 and another inhibitor, FQI2, in inhibiting endogenous hepatocarcinogenesis. HCC was induced in a transgenic mouse with hepatocyte-specific overexpression of c-myc (Alb/c-myc) by injecting N-nitrosodiethylamine (DEN) followed by FQI1 or FQI2 treatment after tumor development. LSF inhibitors markedly decreased tumor burden in Alb/c-myc mice with a corresponding decrease in proliferation and angiogenesis. Interestingly, in vitro treatment of human HCC cells with LSF inhibitors resulted in mitotic arrest with an accompanying increase in CyclinB1. Inhibition of CyclinB1 induction by Cycloheximide or CDK1 activity by Roscovitine significantly prevented FQI-induced mitotic arrest. A significant induction of apoptosis was also observed upon treatment with FQI. These effects of LSF inhibition, mitotic arrest and induction of apoptosis by FQI1s provide multiple avenues by which these inhibitors eliminate HCC cells. LSF inhibitors might be highly potent and effective therapeutics for HCC either alone or in combination with currently existing therapies.

Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets.

BACKGROUND: Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. RESULTS: S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug's transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions--exposure time and concentration and (ii) Network training conditions--training compendium modifications. Two analyses of SSEM-Lasso output--gene set and single gene--were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. CONCLUSIONS: This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.

Antiproliferative small-molecule inhibitors of transcription factor LSF reveal oncogene addiction to LSF in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. Despite the prevalence of HCC, there is no effective, systemic treatment. The transcription factor LSF is a promising protein target for chemotherapy; it is highly expressed in HCC patient samples and cell lines, and promotes oncogenesis in rodent xenograft models of HCC. Here, we identify small molecules that effectively inhibit LSF cellular activity. The lead compound, factor quinolinone inhibitor 1 (FQI1), inhibits LSF DNA-binding activity both in vitro, as determined by electrophoretic mobility shift assays, and in cells, as determined by ChIP. Consistent with such inhibition, FQI1 eliminates transcriptional stimulation of LSF-dependent reporter constructs. FQI1 also exhibits antiproliferative activity in multiple cell lines. In LSF-overexpressing cells, including HCC cells, cell death is rapidly induced; however, primary or immortalized hepatocytes are unaffected by treatment with FQI1. The highly concordant structure-activity relationship of a panel of 23 quinolinones strongly suggests that the growth inhibitory activity is due to a single biological target or family. Coupled with the striking agreement between the concentrations required for antiproliferative activity (GI(50)s) and for inhibition of LSF transactivation (IC(50)s), we conclude that LSF is the specific biological target of FQIs. Based on these in vitro results, we tested the efficacy of FQI1 in inhibiting HCC tumor growth in a mouse xenograft model. As a single agent, tumor growth was dramatically inhibited with no observable general tissue cytotoxicity. These findings support the further development of LSF inhibitors for cancer chemotherapy.