Histone deacetylase inhibitor resminostat in combination with sorafenib counteracts platelet-mediated pro-tumoral effects in hepatocellular carcinoma.

In hepatocellular carcinoma (HCC), blood platelets have been linked to tumor growth, epithelial-to-mesenchymal transition (EMT), extrahepatic metastasis and a limited therapeutic response to the multikinase inhibitor (MKi) sorafenib, the standard of care in advanced HCC for the last decade. Recent clinical data indicated an improved overall survival for sorafenib in combination with the HDAC inhibitor resminostat in a platelet count dependent manner. Here, the impact of platelets on the sorafenib and resminostat drug effects in HCC cells was explored. In contrast to sorafenib, resminostat triggered an anti-proliferative response in HCC cell lines independent of platelets. As previously described, platelets induced invasive capabilities of HCC cells, a prerequisite for extravasation and metastasis. Importantly, the resminostat/sorafenib drug combination, but not the individual drugs, effectively blocked platelet-induced HCC cell invasion. Exploration of the molecular mechanism revealed that the combined drug action led to a reduction of platelet-induced CD44 expression and to the deregulation of several other epithelial and mesenchymal genes, suggesting interference with cell invasion via EMT. In addition, the drug combination decreased phosphorylated ERK level, indicating inhibition of the mitogenic signaling pathway MEK/ERK. Taken together, the resminostat plus sorafenib combination counteracts platelet-mediated cancer promoting effects in HCC cells.

Prediction of mechanisms of action of antibacterial compounds by gene expression profiling.

We have generated a database of expression profiles carrying the transcriptional responses of the model organism Bacillus subtilis following treatment with 37 well-characterized antibacterial compounds of different classes. The database was used to build a predictor for the assignment of the mechanisms of action (MoAs) of antibacterial compounds by the use of support vector machines. This predictor was able to correctly classify the MoA class for most compounds tested. Furthermore, we provide evidence that the in vivo MoA of hexachlorophene does not match the MoA predicted from in vitro data, a situation frequently faced in drug discovery. A database of this kind may facilitate the prioritization of novel antibacterial entities in drug discovery programs. Potential applications and limitations are discussed.