AI Tool Maps Tumor Diversity, Paving Way for Breakthroughs in Cancer Treatment
In a major advancement for precision medicine, an international team of researchers has developed an artificial intelligence (AI) tool that could dramatically enhance cancer treatment by decoding the complex cellular landscape within tumors.
The tool, named AAnet, was created by scientists from the Garvan Institute of Medical Research in Sydney in partnership with the Yale School of Medicine in the United States. It leverages deep learning to analyze gene activity in individual cancer cells, offering unprecedented insight into tumor heterogeneity—a key factor behind treatment resistance and cancer recurrence.
Unlike conventional approaches that treat tumors as uniform masses, AAnet identifies five distinct cell types within a tumor, each with different behaviors and potential to spread. This enables oncologists to understand the biological diversity of cancer more precisely, helping guide multi-pronged treatment strategies.
“Tumor heterogeneity is a major challenge in oncology,” said Associate Professor Christine Chaffer, co-senior author from the Garvan Institute. “Currently, therapies are designed to target a dominant mechanism shared by most tumor cells. But if a subset of cells doesn’t respond to that mechanism, it survives—and cancer can return.” AAnet aims to solve this problem by profiling all major cancer cell types present in a tumor, allowing for combination therapies that target each subset simultaneously.
Associate Professor Smita Krishnaswamy of Yale University, the AI’s co-developer, said AAnet is the first system capable of translating cellular complexity into actionable treatment archetypes, potentially changing the future of oncology.
The technology is now clinically ready, with plans to integrate AI analysis into existing diagnostic workflows to create personalized treatment plans based on the unique cellular makeup of each tumor.
Initially validated in breast cancer, the tool also holds promise for a range of other cancers and even autoimmune diseases, signaling a major step forward in the shift toward personalized medicine. The findings were recently published in the journal Cancer Discovery.
