News
ERC Advanced Grant for Prof. Stiewe, Co-Spokesperson of the former LOEWE Research Cluster iCANx: Research opens up new avenues in precision oncology
Prof. Dr. Thorsten Stiewe – a scientist associated with the former LOEWE Research Cluster "iCANx – Cancer - Lung (Disease) Crosstalk: Tumor and Organ Microenvironment" (2021–2025) and a molecular oncologist at the University of Marburg – has been awarded an ERC Advanced Grant by the European Research Council (ERC). The project titled "TP53 Variant Complexity in Context: Multidimensional Functional Profiling for Precision Oncology" will receive approximately 2.5 million euros in funding over the next five years.
For Stiewe, this funding represents both a validation of his work and the start of a new phase. His research was previously supported by an ERC grant from 2010 to 2016; the Advanced Grant now enables the next major step in development. The project pursues a vision that extends far beyond a single cancer gene: "We aim to create a scalable approach to systematically decipher the functional significance of genetic alterations in the future," says Stiewe.
The award also ties in thematically with key questions addressed by the LOEWE Research Cluster: How do biological properties emerge from complex interactions, and how can this complexity be systematically captured? The ERC project focuses on the tumor suppressor gene TP53, which is mutated more frequently than any other known cancer gene. Using newly developed experimental methods, the research team intends to investigate how genetic alterations have varying effects depending on cell type, genetic background, and the tumor microenvironment.
The project employs a multidimensional approach that examines mutations across several biological dimensions simultaneously for the first time. The goal is to bridge the gap between detecting genetic alterations and understanding them, thereby laying the groundwork for a more precise interpretation of genetic findings in cancer medicine.
In the long term, the results could help better support individual treatment decisions in precision oncology and lead to the development of new methodological tools and datasets applicable to other disease-relevant genes.