ESZTER LAKATOS

The growth of any cancer is affected by how it interacts with the immune system. Cancer cells have many genetic changes (mutations) making them different from healthy cells. Some of these changes are presented on the surface of each cell. If immune cells come near a cancer cells, they can detect this ’unhealthy’ material and attack the cancer cells. Understanding this interplay is crucial for developing better treatment, but there are still many unanswered questions, like: which mutations matter? Are immune cells actively searching for cancer? Is chemotherapy helping the immune system?

In this project, we will develop computational tools that can analyse large cancer datasets in their entirety to answer these questions. The key to our approach is that we do not focus on a single mutation or a single type of immune cell. Instead, we consider all possible molecules (both cancer-specific and healthy ones) that could be presented on the surface of the cancer cells, and check if some molecules ’trump’ others, so that only a few are actually visible to the immune cells.

Then, we will look at the different types of cells present in, and around, the cancer, and use mathematical methods to unravel how strongly they interact – e.g. whether immune cells get attracted to cancer cells and move towards them. Combining these two aspects, we will create a tool that can compute a cancer immunogenicity score from data that is commonly available in the clinic.

Finally, we will create ’digital twins’, personalised computational representations of real cancers, on which we test how different therapies could be combined to have the best treatment with help from the immune system.