Artificial Intelligence Predicts Cancer Growth

The ever-changing nature of tumors is one of the biggest challenges in treating cancer. Scientists have used artificial intelligence to predict how cancers will progress and evolve. This could help doctors design the most effective treatment for each patient.

Artificial Intelligence Predicts Cancer Growth

A team led by the Institute of Cancer Research London (ICR) and the University of Edinburgh developed a new technique known as Revolver (Repeated Evolution of Cancer). This picks out patterns in DNA mutation within cancers and uses the information to forecast future genetic changes. The researchers said the ever-changing nature of tumors was one of the biggest challenges in treating cancer – with cancers often evolving to a drug-resistant form.

Breast Tumors:

However, if doctors can predict how a tumor will evolve, they could intervene earlier to stop cancer in its tracks before it has had a chance to evolve or develop resistance, increasing the patient’s chances of survival. The team also found a link between certain sequences of repeated tumor mutations and survival outcome. This suggests that repeating patterns of DNA mutations could be used as an indicator of prognosis, helping to shape future treatment.

“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”, said Dr. Andrea Sottoriva, Institute of Cancer Research London. For example, researchers found that breast tumors which had a sequence of errors in the genetic material that codes for the tumor-suppressing protein p53, followed by mutations in chromosome 8, survived less time than those with other similar trajectories of genetic changes. You may like Bitcoin Price Drops 

The research team developed a new machine learning technique which transfers knowledge about tumors across similar patients. This method identifies patterns in the order that genetic mutations occur in tumors that are repeated both within and between patients’ tumors, applying one tumor’s pattern of mutations to predict another’s.

Researchers used 768 tumor samples from 178 patients reported in previous studies for lung, breast, kidney and bowel cancer, and analyzed the data within each cancer type respectively to accurately detect and compare changes in each tumor. By identifying repeating patterns and combining this with current knowledge of cancer biology and evolution, scientists could predict the future trajectory of tumor development.

Personalised treatment:

If tumors with certain patterns are found to develop resistance to a particular treatment, this novel methodology could be used to predict if patients will develop resistance in the future. The research is published in the journal Nature Methods.

Dr Andrea Sottoriva, who led the study and is team leader in evolutionary genomics and modelling at the ICR, said: “We’ve developed a powerful artificial intelligence tool which can make predictions about the future steps in the evolution of tumors 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.”

ICR chief executive Professor Paul Workman said: “Cancer evolution is the biggest challenge we face in creating treatments that will work more effectively for patients. “If we are able to predict how a tumor will evolve, the treatment could be altered before adaptation and drug resistance ever occur, putting us one step ahead of cancer. “This new approach using AI could allow treatment to be personalized in a more detailed way and at an earlier stage than is currently possible, tailoring it to the characteristics of each an individual tumor and to predictions of
what that tumor will look like in the future.”

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