Cancer is a disease of the genome. It is caused by the successive accumulation of DNA errors (somatic driver mutations). This carcinogenic process starts in normal cells and is an example of Darwinian evolution, where each driver event results in a fitness advantage, positive selection and clonal expansion of the affected cells.

The Computational Cancer Genomics and Tumor Evolution (CCGG) lab aims to better understand this tumor evolution using state-of-the-art as well as newly developed wet-lab and computational approaches. By gaining new insights in the key mechanisms underlying tumor evolution, our ultimate goals is to identify novel diagnostic and therapeutic strategies for cancer patients.

The lab's core expertise is in the analysis of somatic mutation patterns, spatial omics applications, machine learning and interactive data visualization. Analyses are mostly performed on next generation sequencing data. These data are obtained from public repositories or from newly sequenced tissues from cancer patients, whole-body donors (post-mortem tissues) or experimental model systems.