Metastatic hepatocellular carcinoma. Image by Yale Rosen via Flickr
Hepatocellular carcinoma (HCC), a cancer associated with underlying liver disease and cirrhosis that often only becomes symptomatic when it is very advanced, is the second leading cause of cancer deaths around the world, and yet it has no effective treatment.
As with other conditions without treatments, the data that scientists need to understand and treat the disease may be sitting in plain view in databases that have barely been analyzed, says Atul Butte, MD, PhD, director of the Institute for Computational Health Sciences at UC San Francisco.
Bin Chen, PhD, a former postdoctoral scholar in Butte’s lab and now a faculty member in Pediatrics in the Institute for Computational Health Sciences, recently published a paper in Gastroenterology about using data-mining computational tools to identify a treatment for HCC.
Gene Expression and Drug Targets
Taking advantage of publicly available gene expression data, he first derived a molecular disease signature for HCC – looking at 274 genes that are either up or down regulated in cancerous liver tissues, but not in normal liver tissues.
Then, he looked for drugs that were known to target those genes and found, to his surprise, that a close cousin of a deworming pill, when used in combination with the standard care drug, was highly effective at killing cancerous liver tissue that had been engrafted into experimental mice.