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.
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
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
The general validity of the approach can be extended by running personalized models for more patients.
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.
Text: Christian Borg and Martin Markström
Read the article in Molecular Systems Biology: Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling
Mass production of personalized models with new algorithm
a new algorithm for automatic reconstruction of personalized models,
was recently developed at Chalmers University of Technology, branded as
tINIT. Compared to previous algorithms it
allows reconstruction of functional and simulation ready models.
All the tools for have been made publically available in the so called Raven toolbox
Proteomics data in Human Protein Atlas
All of the models were generated based on the proteomics data generated in Human Protein Atlas, HPA.
The Human Protein Atlas project is a world leading effort to
systematically explore the human proteome in 46 different types of
normal tissue and 216 different cancer tissues representing the 20 most
common forms of human cancer. The atlas was recently used to generate a
virtual map of human metabolism, in a collaborative project between
Chalmers University of Technology and researchers at SciLifeLab.
This virtual map of human cell metabolism has the most comprehensive
list of the entire set of known biochemical reactions which occurs in
human body. The map contains more than 8,000 reactions and 3,765
associated genes, and it represents a great resource for systems
biologists working in human disorders related to metabolism.
Read the article in Molecular & Cellular Proteomics: Analysis of the Human Tissue-specific Expression by Genome-wide Integration of Transcriptomics and Antibody-based Proteomics
Drug targets and biomarkers also for other liver conditions
same type of modelling (however using an average model) has previously
been used to investigate the most common liver disease, non-alcoholic
fatty liver disease. This resulted in predictions of new drug targets,
new biomarkers for diagnosis, as well as suggestions for dietary
Read more about that study in the Chalmers' news item Computer model takes us closer to treatment of our most common liver disease, or the Nature Communications article Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease.