Division of Applied Functional Genomics

Our laboratory is studying rare, poorly understood tumor diseases with the goal of finding new entry points for therapies through a better understanding of their biology and pathogenesis.

Rare cancers and ultra-rare sarcomas pose a major problem for affected patients as they are associated with several challenges. Unlike common cancers, only a few patients are available to conduct randomized clinical trials, discover recurrent cancer driver genes, and study the biology of the disease. This leads to lack of scientific knowledge, misdiagnosis and delays in diagnosis, and deficits in clinical expertise and appropriate therapies, which ultimately results in poorer overall survival when comparing rare to common cancers. The relevance of studying these ill-defined cancers also becomes clear when considering the number of patients affected, which overall are not that rare. 22% of all cancers diagnosed each year in Europe are rare, that is one out of five cancers. In addition, approximately a quarter of all people living with cancer in Europe have a rare cancer, which are 4.3 Million people.

Our strategy to gain insights into the biology of rare cancers is to combine omics analyses of patient samples with the functional characterization of tumor-specific alterations. We have a close cooperation with the prospective precision oncology program MASTER (Molecularly Aided Stratification for Tumor Eradication Research), in which patient tumors, in particular rare cancers, are investigated by whole-genome and RNA sequencing and DNA methylation. Genetic alterations discovered in this program are functionally characterized in our laboratory by using different model systems including cell lines and mouse models. In addition, we uncover secondary gene dependencies by using large-scale CRISPR screening that is followed by their in-depth mechanistic characterization. Through these efforts, we provide new mechanistic insights into critical cellular signaling pathways and help translate genomic information into clinical applications.