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.

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.

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.

Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis.

Understanding the systemic biological pathways and the key cellular mechanisms that dictate disease states, drug response, and altered cellular function poses a significant challenge. Although high-throughput measurement techniques, such as transcriptional profiling, give some insight into the altered state of a cell, they fall far short of providing by themselves a complete picture. Some improvement can be made by using enrichment-based methods to, for example, organize biological data of this sort into collections of dysregulated pathways.