101 liver cancer drug candidates pave way to personalized medicine

"This is the first time personalized models have been used to find and evaluate new potential drugs," says Professor Jens Nielsen at Chalmers University of Technology.

Our most common liver cancer, Hepatocellular carcinoma, HCC, causes more than half a million deaths worldwide every year. If the cancer cannot be surgically removed the disease is usually deadly within 3-6 months. Only 30 percent of the patients respond to the best existing drug, sorenafib. This new research now identifies 101 drug candidates predicted to prevent the cancer growth in all six studied patients, which raises hope of developing a drug helping all HCC-patients.

Cancer cells modify their metabolism in order to breed. To understand these diverse mechanisms, what metabolic enzymes are involved, when and how, has been a major focus in medicine in order to identify novel drug targets. However, this is quite a challenging task, since HCC not only involves a large number of interplays between different biological pathways, but also significant individual variations.

Finding the candidates Average models don't give sufficiently good answers. To take the individual variations into account the researchers generated personalized proteomics data for HCC patients using the antibodies produced in the Human Protein Atlas project (read more below). The researchers then generated individual computer models for six HCC patients based on their entire, personal set of proteins and a generic map of human metabolism, which had been produced in an earlier project.

"I am excited to see how we have managed to successfully transfer our modelling approaches on yeast to study cancer metabolism," says Dr Rasmus Ågren, shared first author of the paper.

The six personal models were then used to find potential new anticancer drugs. One of the most common types of anticancer drugs is so called antimetabolites. Antimetabolites prevent the use of one or more metabolites (the small molecules that act and are produced, and whose interplay together constitute a full metabolism) by stopping the catalyzing enzymes. By simulating the effect of all possible antimetabolites -- more than 3000 compounds -- the computer generated potential anti-cancer drugs which could be effective in inhibiting tumor growth.

Furthermore, the researchers simulated the effect of these antimetabolites on 83 major healthy cell types in human body to predict their toxic effects. This led to the identification of 101 antimetabolites which were predicted to prevent cancer growth in all six studied HCC patients, whereas 46 antimetabolites inhibited tumor growth only in one or a few of the patients. All 147 were predicted to not be overly toxic to healthy cells.

Clinical tests The general validity of the approach can be extended by running personalized models for more patients.

"With this approach we can find and evaluate new potential drugs, some that could be used for general treatment of HCC, and others that are highly specific for each HCC patient. We can also predict false positive drug targets that would not be effective in all patients. This would lead to more targeted and efficient cancer treatment," says Dr Adil Mardinoglu, shared first author of the paper.

One of the antimetabolites was tested in vitro on a liver cancer cell line. The tested compound, perhexiline, had an effect on viability comparable to sorafenib, demonstrating the predictive power of the computer models. Perhexiline is a drug approved for heart disease, and the potential to use it for HCC is of course interesting. As a continuation of this study, perhexiline will be tested on other cancer cell lines, since the researchers believe it may inhibit growth in other types of cancer as well. The initial experiments look very promising.

This work was supported by grants from the Knut and Alice Wallenberg Foundation.

Read the article in Molecular Systems Biology: Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling