AI used to forecast cancer progression

pharmafile | September 3, 2018 | News story | Sales and Marketing AI, Cancer, ICR, London, ai drug research and development, university of edinburgh 

A team of scientists at the Institute of Cancer Research (ICR) in London and the University of Edinburgh have developed an artificial intelligence that is able to predict the ways in which cancers will progress and evolve. The technique may help doctors design more effective personalised cancer treatments in the future.

The technique, called REVOLVER (Repeated Evolution of Cancer) identifies patterns in DNA mutation within cancers and uses that information to forecast future genetic changes.

While the ever changing nature of tumours is one of the biggest challenges in treating cancer, the new tool has allowed researchers to predict the disease’s progression. It is thus hoped the tool will allow doctors to stay one step ahead in the fight against cancer.

Dr Andrea Sottoriva, Team Leader in Evolutionary Genomics and Modelling at The Institute of Cancer Research, London, who led the study commented: “We’ve developed a powerful artificial intelligence tool which can make predictions about the future steps in the evolution of tumours based on certain patterns of mutation that have so far remained hidden within complex data sets. With this tool we hope to remove one of cancer’s trump cards – the fact that it evolves unpredictably, without us knowing what is going to happen next. By giving us a peek into the future, we could potentially use this AI tool to intervene at an earlier stage, predicting cancer’s next move.”

The research, which was funded by the Wellcome Trust, the European Research Council and Cancer Research UK, was outlined in the journal Nature Methods. The researchers from teh University of Edinburgh and the ICR worked collaboratively with colleagues from the University of Birmingham, Stanford University and Queen Mary University London, who also found that there was a link between certain sequences of repeated tumour mutations and survival outcome.

Joint study leader Professor Guido Sanguinetti, from the School of Informatics at the University of Edinburgh, said: “This study shows how collaboration across disciplines adds value to research. By solving a statistical machine learning problem, we were able to shed light on cancer evolution. It is an example of how the power of AI to detect complex patterns in data can be harnessed to further our scientific understanding to improve human health.”

Louis Goss

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