Using AI to understand how triple negative breast cancer responds to treatment

Triple negative breast cancer is a more aggressive form of breast cancer that’s harder to treat than other forms of the disease. Currently, for many people diagnosed with triple negative breast cancer, treatment includes chemotherapy and immunotherapy given before surgery. It’s often very effective, but in some people, this treatment doesn’t kill all the cancer cells by the time of surgery. When this happens, breast cancer is more likely to return.

If we can understand why this treatment doesn’t always work, the researchers will be able to design better, more effective treatments that can be tailored to the needs of each individual.

This research uses cutting-edge science to study breast cancer in new ways. By combining different technologies with AI models, the team aims to build a fuller picture of how triple negative breast cancer behaves – and how that might change with treatment.

1. Using AI to analyse tumours and predict treatment response
   
   The researchers are using AI models to study tissue samples donated by people who had treatment for triple negative breast cancer before surgery. They’re looking for features linked to treatment success or resistance in samples of breast tumours and lymph nodes. 
   
   They hope to train AI models to spot important patterns in how breast cancer and immune cells are arranged. These patterns could help to predict whose cancer is more likely to respond well to chemotherapy and immunotherapy. 
   
   
2. Understanding how the immune system responds to treatment
   
   This project focuses on how immune cells act in response to chemotherapy and immunotherapy. Immunotherapy treatment aims to turn the immune system on so that it can recognise breast cancer. The researchers are studying tissue samples in detail, looking at specific areas where treatment worked or didn’t. 
   
   By comparing treated and untreated tumour samples, they hope to understand what a successful immune response looks like, and how to spot it early. The insights could guide better use of immunotherapy in the future.
   
   
3. Combining data to build a full picture of treatment response
   
   Finally, the researchers are developing ways to combine information from a range of different sources to build a full picture of how breast tumours respond to treatment.
   
   They're tracking changes over time and across different areas of the tumour and lymph nodes. This approach will help them understand how breast cancer cells interact with nearby immune cells, and how these relationships affect cancer’s response to treatment. By combining all this information, the team aims to create AI tools that better predict how each person’s triple negative breast cancer is likely to behave.

This research could help doctors predict how someone’s triple negative breast cancer will respond to treatment. That means more people could get the right treatment at the right time.
It may also lead to better use of immunotherapy by making sure it’s given in the most effective way and to people most likely to benefit.

Over time, these insights and tools could help researchers design better and more personalised treatments for people with triple negative breast cancer.

Every year in the UK, over 8000 people are diagnosed with triple negative breast cancer. So this project has the potential to help thousands of people diagnosed with this type of breast cancer.