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'Virtual experiments' aid scientists to find missing cancer targets

Scientists have identified 46 previously overlooked but potentially ‘druggable’ cancer targets, using a powerful new online approach that allows researchers to carry out ‘virtual experiments’ to quickly prioritise which are the best targets for drug discovery.

The research, funded by Cancer Research UK and conducted at The Institute of Cancer Research, London, is published in the journal Nature Reviews Drug Discovery.

The new approach - created by researchers at Cancer Research UK’s Cancer Therapeutics Unit at The Institute of Cancer Research (ICR) - combines the use of a unique online database called canSAR with a new tool that allows researchers to compare up to 500 potential drug targets at the same time in minutes.

This will enable scientists all over the world to systematically analyse unprecedented volumes and varieties of data, to uncover new or previously overlooked drug targets with the potential to lead to innovative cancer drugs.

The researchers demonstrated the power of their new strategy by analysing the Sanger Institute’s existing list of 479 cancer genes, revealing a total of 46 potentially druggable cancer proteins that have previously been overlooked for drug discovery, despite their known biological relevance to cancer.

Dr Bissan Al-Lazikani said: “To find so many overlooked potential new targets for cancer treatments in one project is very surprising. These results show that, using this new approach, we can find targets for cancer drugs in a smarter and faster way than ever before.

“This new way of harnessing genomic data is a key step towards the discovery of the next generation of cancer treatments. It is a stepping stone between research into the fundamental causes of cancer and new drugs delivering benefits to patients.”

Study co-author Professor Paul Workman, director of the Cancer Research UK Cancer Therapeutics Unit and deputy chief executive of The Institute of Cancer Research, said: “Our new approach will help researchers worldwide to address three major issues that we face today in developing new cancer drugs for personalised medicine. Firstly, it will empower scientists to select the very best targets that are most likely to lead to successful drugs, thereby increasing the success rate in the clinic. Secondly, it will allow researchers to discover the best new drugs much more quickly and at a lower cost. Thirdly, it will enhance innovation, by helping shift the focus away from the tried and tested drug targets while managing the inevitable risk associated with moving into new and exciting areas. Both patients and the pharmaceutical industry will benefit from these advances.”  

Dr Nigel Blackburn, director of drug development at Cancer Research UK’s Drug Development Office, said: “A key problem in cancer research at the moment is how to make sense of the wealth of information coming out of cancer genome studies. This exciting new resource provides a strategy by which scientists can combine this information with both structural and chemical data, to select the very best gene targets for future development. Not only will this save time and money, but it also paves the way for research into promising new drug targets that until recently may have been overlooked due to a lack of information.”


For media enquiries, please contact Ailsa Stevens in the Cancer Research UK press office on 020 3469 8309 or, out of hours, the duty press officer on 07050 264 059. 

Notes to editors

canSAR is an integrated database and workbench that brings together biological, chemical, pharmacological (and eventually clinical) data. Its goal is to integrate this information and make it accessible to cancer research scientists from multiple disciplines worldwide, in order to help with hypothesis generation in cancer research and support drug discovery decisions.

Users can search canSAR using text queries, protein/gene name searches, any keyword searches, chemical structure searches and sequence similarity searches.
Additionally, users can explore and filter chemical compound sets, view experimental data and produce summary plots. For more information visit:


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