Lipophilic statins limit cancer cell growth and survival, via involvement of Akt signaling.
Colin H BeckwittKeisuke ShirahaAlan WellsPublished in: PloS one (2018)
The HMG-CoA reductase inhibitors, statins, have been used as lipid lowering drugs for decades and several epidemiological studies suggest statin usage correlates with a decreased incidence of cancer specific mortality in patients. However, the mechanism of this mortality benefit remains unclear. Here, we demonstrate that statin drug lipophilicity and affinity for its target enzyme, HMGCR, determine their growth suppressive potency against various tumor cell lines. The lipophilic atorvastatin decreases cancer cell proliferation and survival in vitro. Statin sensitivity coincided with Ras localization to the cytosol instead of the membrane, consistent with a decrement in prenylation. To investigate signaling pathways that may be involved with sensitivity to statin therapy, we employed inhibitors of the PI3K-Akt and Mek-Erk signaling cascades. We found that inhibition of PI3K signaling through Akt potentiated statin sensitivity of breast cancer cells in vitro and in co-culture with primary human hepatocytes. The same effect was not observed with inhibition of Mek signaling through Erk. Moreover, the sensitivity of breast cancer cells to atorvastatin-mediated growth suppression correlated with a decrease in EGF-mediated phosphorylation of Akt. As an increase in Akt activity has been shown to be involved in the metastasis and metastatic outgrowth of many cancer types (including breast), these data suggest a mechanism by which statins may reduce cancer specific mortality in patients.
Keyphrases
- cell proliferation
- signaling pathway
- papillary thyroid
- cardiovascular disease
- coronary artery disease
- pi k akt
- end stage renal disease
- squamous cell
- breast cancer cells
- cardiovascular events
- squamous cell carcinoma
- risk factors
- newly diagnosed
- chronic kidney disease
- endothelial cells
- prognostic factors
- lymph node metastasis
- type diabetes
- machine learning
- peritoneal dialysis
- fatty acid
- childhood cancer
- mesenchymal stem cells
- young adults
- induced apoptosis
- bone marrow
- deep learning
- protein kinase
- big data
- artificial intelligence
- capillary electrophoresis
- pluripotent stem cells