Key Points

The innovation tackles tumour heterogeneity in oncology, where varied cell populations cause treatment resistance and recurrence, Xinhua news agency reported.

The AAnet AI tool, developed by the Sydney-based Garvan Institute of Medical Research in collaboration with the Yale School of Medicine in the US, uses deep learning to study gene activity in single cancer cells.

It finds five different cell types within tumours, each with its own behaviour and risk of spreading. This helps doctors understand cancer better than older methods, which treated all tumour cells the same, said the multinational research team.

"Heterogeneity is a problem because currently, we treat tumors as if they are made up of the same cell. This means we give one therapy that kills most cells in the tumor by targeting a particular mechanism. But not all cancer cells may share that mechanism," said the study's co-senior author, Associate Professor Christine Chaffer from the Garvan Institute.

As a result, some cancer cells survive, and the disease can return, Chaffer said. She added that AAnet provides a way to biologically characterise tumour diversity, enabling the design of combination therapies that target all cell groups at once.

Associate Professor Smita Krishnaswamy of Yale University, a co-developer of the AI, indicated that this is the first method to distill cellular complexity into practical archetypes, potentially transforming precision oncology.

The technology is ready for clinical use, with plans to combine AI analysis and traditional diagnostics to create treatments tailored to each tumour's cell type.

Validated in breast cancer, it also shows promise for other cancers and autoimmune diseases, marking a shift toward personalised medicine, revealed the study published in the journal Cancer Discovery.

- IANS