Artificial intelligence ‘can accurately predict cancer patients’ survival chances’

ARTIFICIAL intelligence can accurately predict cancer patients’ survival chances, according to a new study.

American scientists have developed an AI model that is able to predict if and when patients with various types of cancer will die.

The researchers found that by examining the gene expression patterns of “epigenetic” factors those that influence how genes are turned on or off - in tumours, they could categorise them into distinct groups to predict patient outcomes better than traditional measures.

The UCLA research team say their findings, published in the journal Communications Biology, also lay the groundwork for developing targeted therapies aimed at regulating epigenetic factors in cancer therapy.

Co-senior author Professor Hilary Coller, of UCLA Health Jonsson Comprehensive Cancer Centre, said: “Traditionally, cancer has been viewed as primarily a result of genetic mutations within oncogenes or tumour suppressors.

“However, the emergence of advanced next-generation sequencing technologies has made more people realise that the state of the chromatin and the levels of epigenetic factors that maintain this state are important for cancer and cancer progression.

“There are different aspects of the state of the chromatin - like whether the histone proteins are modified, or whether the nucleic acid bases of the DNA contain extra methyl groups - that can affect cancer outcomes.

“Understanding these differences between tumours could help us learn more about why some patients respond differently to treatments and why their outcomes vary.”

While previous studies have shown that mutations in the genes that encode epigenetic factors can affect a person’s susceptibility to cancer, little is known about how the levels of these factors impact cancer progression.

Prof Coller says that knowledge gap is “crucial” in fully understanding how epigenetics affect the chances of a patient surviving.

Study first author Michael Cheng said: “Our research helps provide a roadmap for similar AI models that can be generated through publiclyavailable lists of prognostic epigenetic factors.”