Grant: Cat Gr: Develop./validation of new biomedical techniques/technolo.- General Pool

Immunotherapy is a promising approach to fighting cancer by using the human body's own immune system. One type of immunotherapy, called CAR-T therapy, involves modifying a cancer patient's immune cells, so they can better target and kill cancer cells. This is done by giving these cells special molecules that can recognize and attack cancer cells while leaving healthy cells alone. Interactions between these special molecules and cancer cells is crucial for killing the cancer. These set off a series of signals inside the cells that depend on various other molecules. However, there is still a lot we do not know about how exactly these special molecules trigger these mechanisms when they contact cancer cells. Understanding this process better is essential for making immunotherapies more effective in treating cancer. Mass spectrometry is a technique that allows us to identify molecules and their interacting partners. With this technology, we can map out how molecules interact with themselves and other molecules. This approach can help us understand how the immune system targets cancer cells. However cutting-edge mass spectrometry is not sensitive enough to detect all such interactions. In this project, we propose to develop software solutions based on artificial intelligence that will change how mass spectrometry acquires data from interacting molecules. Our novel approaches focus their efforts on data that allows the detection of such interactions and improve our ability to identify them with confidence. The improved detection of molecular interactions in the context of immunotherapy will shed light on how this clinical approach can be used to fight cancers.