Skip to main content
Mouse lymph nodes: - The colors represent fluorescent markers that bind to specific molecules on the surface or inside individual immune cells. In the project, analyses of similar images from patients are planned, which will provide information about the type and positioning of immune cells in the vicinity of tumor cells.
© Image: AG Hoelzel / UKB

News categories: Honors & Funding

Artificial intelligence to help tumor immunology

Researchers want to study the environment of cancer cells in more detail. Federal Ministry of Education and Research funds project with 800,000 euros.

The success of cancer treatment depends not only on the type of tumor, but also on the surrounding tissue. Tumors influence it to their advantage, promoting the growth of blood vessels or fooling incoming immune cells. Developing methods to predict the nature of the resulting tumor microenvironment is the goal of researchers from the Clusters of Excellence ImmunoSensation2 and the Hausdorff Center for Mathematics (HCM) led by Prof. Kevin Thurley at the University of Bonn. The German Federal Ministry of Education and Research (BMBF) is funding the "InterpretTME" project with around 800,000 euros over the next three years.

Cancer treatment has been revolutionized in the past decade by new methods of immunotherapy. This involves not attacking a tumor directly, but rather using the existing cells of the immune system. These are usually able to recognize and eliminate malignant tumor cells. However, many tumors have the ability to prevent or severely limit an effective immune response. Immunotherapy aims to restore the misguided immune system's ability to recognize and destroy tumor cells.

The role of the tumor microenvironment

Immunotherapy against cancer is not successful in all patients. Resistance to cancer immunotherapies has been shown to be frequently associated with tumor microenvironment (TME) composition. In oncology, the properties of the TME are already being used as biomarkers to make predictions about how a cancer will develop. This is done using imaging techniques that map the type and location of individual cells within the TME. Patterns of gigantic cell assemblies emerge, which in their totality and structure influence the success or failure of cancer immunotherapy. How exactly this works, however, remains elusive.

"New high-resolution imaging techniques have shown that disease mechanisms are indeed related to details of the spatial arrangement of specific cell types in tissues," notes Prof. Kevin Thurley of the Institute of Experimental Oncology at the University Hospital Bonn. "Using a combination of mathematical modeling and artificial intelligence methods, we will investigate these phenomena in detail, in direct collaboration with experimental and clinical research at our University Hospital."

Artificial intelligence for tissue analysis

Artificial intelligence (AI)-based methods for image analysis are already well advanced today. The situation is different when simulating complex systems, due to the large number of interacting cells within a tissue. Given the multitude of cell types involved, the different cellular processes taking place there, and the complex tissue architecture, such a simulation requires extremely high computational resources. However, it can help to simulate the TME of a tumor and thus draw conclusions about tumor development.

Insights into immunotherapy through machine learning

The overall goal of "InterpretTME" is to develop interpretable machine learning (ML) methods for studying complex cellular systems. These are to be used to gain insights into the nature of TMEs. "Machine learning is already used in many places in the hospital to process imagedata," explains Prof. Jan Hasenauer, of the Life & Medical Sciences Institute (LIMES) at the University of Bonn. "We will go one step further and investigate the extent to which information about mechanisms can also be obtained." One aim is to investigate the role that individual immune cell types present in the TME play in the development
of different tumor types. In addition, the researchers want to determine what effect chemotherapeutic agents and biological drugs have on the TME of different tumor types. Prof. Michael Hölzel and Prof. Marieta Toma from the University Hospital Bonn and Prof. Alexander Effland from the University of Bonn are also involved in the project.

Jan Hasenauer, Kevin Thurley, and Alexander Effland are the group leaders of the Interdisciplinary Research Unit (IRU) Mathematics and Life Sciences at the Unversity of Bonn.


Dr. David Fußhöller

Cluster of Excellence ImmunoSensation2

University of Bonn

Tel. (+49) 228 287 512 83


Related news

Christina Weisheit

News categories: Honors & Funding

Finding novel therapies for aortic valve diseases

The DFG funds the project of Christina Weisheit and collaborators with 600,000 euros.
View entry
GTH Preis

News categories: Honors & Funding

The Becker-Gotot group is awarded at the GTH 2024 in Vienna

Janine Becker-Gotot and Amina Abdelmageed receive awards for their intriguing studies at the GTH 2024 in Vienna.
View entry
Radosław P. Nowak is Professor of Immune Engineering and Drug Discovery at the University Hospital Bonn

News categories: Honors & Funding

Expert in the targeted degradation of proteins

Radosław P. Nowak is Professor of Immune Engineering and Drug Discovery at the University Hospital Bonn
View entry

Back to the news overview