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SINGAPORE The development of personalised cancer treatments will get a boost from an artificial intelligence-based method that can identify cancer mutations in DNA fragments inside tumour samples.

The method, called Variant Network (VarNet), uses deep learning to detect cancer mutations. It is developed by scientists from the Genome Institute of Singapore(GIS), a research institute under the Agency for Science, Technology and Research (A*Star).

Cancer is generally thought to be caused by mutations in our genomes, and its essential to identify these mutations to tailor the most effective treatment for the individual patients, said Dr Anders Skanderup, group leader of GIS Laboratory of Computational Cancer Genomics.

In line with the precision medicine approach where medical treatment is tailored to the individual based on factors such as variations in genetics and environment drugs prescribed for cancer treatment increasingly work only when certain mutations are present, he said.

A high level of accuracy is needed when identifying cancer mutations, he added.

VarNet is a mutation caller, which identifies mutations by sifting through raw DNA sequencing data.

Using artificial intelligence (AI), VarNet is trained to identify mutations through exposure to millions of real cancer mutations as well as to examples of false cancer mutations.

This enables VarNet to detect real mutations while ignoring false ones, Dr Skanderup told The Straits Times.

A paper published in the peer-reviewed scientific journal Nature Communications in July 2022 found VarNet often exceeded existing mutation identification algorithms in terms of accuracy.

While other AI-based methods of detecting cancer mutations exist, these rely heavily on human experts providing vast amounts of detailed training data to the models to train them to identify mutations, he said.

Deep learning an AI method where computers are taught to process data in a way that mimics the human brain allows VarNet to distinguish between real and false mutations, essentially teaching itself the rules of doing so, with minimal human intervention.

The papers first author Kiran Krishnamachari an A*Star Computing and Information Science scholar affiliated with GIS noted VarNet is able to learn to detect mutations from the raw data in a manner that a human expert would do when manually inspecting potential mutations.

This gives us the confidence that the system can learn relevant mutational features when trained on vast sequencing datasets, using our weak-supervision strategy that does not require excessive manual labelling, he said.

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Artificial intelligence developed to identify cancer mutations - The Straits Times