AI tool set to transform characterization and treatment of cancers

Tumors aren't made of just one cell type – they're a mix of different cells that grow and respond to treatment in different ways. This diversity, or heterogeneity, makes cancer harder to treat and can in turn lead to worse outcomes, especially in triple negative breast cancer.

“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. As a result, while the patient may have an initial response, the remaining cells can grow and the cancer may come back,” says associate professor Christine Chaffer, co-senior author of the study and co-director of the Cancer Plasticity and Dormancy Program at the Garvan Institute of Medical Research.

But while heterogeneity is a problem, researchers don’t know enough to characterize it: “So far researchers haven’t been able to clearly explain how adjacent cells in a tumor differ from each other, and how to classify those differences into meaningful ways to better treat tumors. But this is exactly what we need to know so we can kill all cells within that tumor with the right therapies,” Chaffer adds.​​​​​​

The researchers say the use of AAnet to characterize the different groups of cells in a tumour according to their biology opens doors for a paradigm shift in how we treat cancer.

“Currently the choice of cancer treatment for a patient is largely based on the organ that the cancer came from such as breast, lung or prostate and any molecular markers it may exhibit. But this assumes that all cells in that cancer are the same. Instead, now we have a tool to characterize the heterogeneity of a patient’s tumour and really understand what each group of cells is doing at a biological level. With AAnet, we now hope to improve the rational design of combination therapies that we know will target each of those different groups through their biological pathways. This has the potential to vastly improve outcomes for that patient,” says Chaffer.

On the application of AAnet, co-senior author of the study and Chief Scientific Officer of Garvan Professor Sarah Kummerfeld states: “We envision a future where doctors combine this AI analysis with traditional cancer diagnoses to develop more personalised treatments that target all cell types within a person’s unique tumour. These results represent a true melding of cutting-edge technology and biology that can improve patient care. Our study focused on breast cancer, but it could be applied to other cancers and illnesses such as autoimmune disorders. The technology is already there.”