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In a new paper published in Cell Reports Medicine, the UCL team outlines how they, in collaboration with a University of Cambridge team, developed a new type of treatment-predicting computer program that uses an individual's detailed genetic fingerprint to explore which treatments, if any, may kill their cancer—especially when standard treatments have failed.

Computer models have long been used in cancer medicine, and more recently artificial intelligence has opened up a new avenue in this regard.

But the "black box" nature of "traditional" AI associated with large language models—where how they arrive at their conclusions is unclear—has prompted a degree of skepticism among the medical profession, the researchers said.

In developing their model, specifically to recommend treatment options for triple-negative breast cancer (TNBC), a hard-to-treat cancer, the UCL team instead used a form of AI called "symbolic AI" which relies on logic rather than prediction and where its workings can be clearly seen.

TNBC is an aggressive, fast-growing form of breast cancer that accounts for about 15% of all breast cancer cases.

To test the effectiveness of their approach, the team built computer-generated tumors—or "digital twins"—that aped the real-life tumors of 31 patients involved in the Platinum and PolyAdenosine 5'-Diphosphoribose Polymerisation Inhibitor for Research and Treatment of Neo-Adjuvant Early and Relapse-Risk Breast Cancer (PARTNER) clinical trial using their genetic information.

They then copied the process the researchers had carried out in the trial, but as a computer simulation: on each digital twin, they saw what happened when they treated the tumor with either chemotherapy alone or with chemotherapy and a drug called olaparib , known to be effective in treating TNBC in some cases.

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