Interaction network highlighting the distribution of targets of approved cancer drugs (pink); targets of approved drugs from non-cancer therapeutic areas (blue); and targets predicted to be druggable by different druggability prediction methodologies(light and dark green). Druggable proteins are spread widely across the network while targets of current approved drugs tend to cluster into few areas. [© 2015 Mitsopoulos]
Interaction network highlighting the distribution of targets of approved cancer drugs (pink); targets of approved drugs from non-cancer therapeutic areas (blue); and targets predicted to be druggable by different druggability prediction methodologies(light and dark green). Druggable proteins are spread widely across the network while targets of current approved drugs tend to cluster into few areas. [© 2015 Mitsopoulos]

Cancer Research UK-funded researchers say they designed a computer model that applies methods used to analyze social networks to identify new ways of treating cancer. They published their study (“Distinctive Behaviors of Druggable Proteins in Cellular Networks”) in PLOS Computational Biology.

The model analyses the unique behaviors of oncogenic proteins and spots what makes them different from normal proteins. The program also maps out molecular targets for new potential drugs that could be developed to treat cancer.

Scientists at the Institute of Cancer Research in London compared proteins inside cells to members of an enormous social network, mapping the ways they interact. This allowed them to predict which proteins will be most effectively targeted with drugs. The researchers made the map publicly available in their journal article. They believe it could provide drug discovery scientists with a shortcut to finding new drugs for many different types of cancer.

The team found that there are many molecular pathways that interact to affect the development of cancer. Oncogenic proteins that have already been successfully targeted with drugs tended to have particular “social” characteristics that differ from non-cancer proteins, suggesting that previously unexplored cancer proteins with similar characteristics could also make good drug targets. 'Hub-like' proteins which communicate with lots of other proteins, like a super-Facebook user with thousands of friends, were more likely to cause cancer.

“Our study is the first to identify the rules of social behavior of cancer proteins and use it to predict new targets for potential cancer drugs. It shows that cancer drug targets behave very differently from normal proteins and often have a complex web of social interactions, like a Facebook super-user,” said Bissan Al-Lazikani, Ph.D., team leader in computational biology and cancer research at the Institute of Cancer Research. “Finding new targets is one of the most important steps in drug discovery. But it can be a lengthy, expensive process. The map that we've made will help researchers design better new drugs, more quickly, saving time and money. It also sheds light on how resistance to treatments may occur, and in just a few years could help doctors choose the best drug combinations to suit individual patients.” 

Previous articleCan Lab-Grown Super Corals Save The Ocean?
Next articleAgenus Acquires PhosImmune for Up to $44.9M