Cancer evolution

Our research focuses on the question of how alterations of the DNA transform a healthy into a malignant cell. 10,000s of human genomes and epigenomes have been sequenced over the last years. These efforts uncovered a large number of genetic and epigenetic alterations in cancer patients. Given this large number of alterations it is difficult to tell which of those contribute to disease progression. In particular, we are interested to understand how to detect selection for alteration types other than point mutations such as epigenetic changes or structural alterations of the genome. Another question we address is which regions of the genome are under negative selection, ie. are depleted in alterations.

Cancer: a complex system

We aim to understand how the environment impacts selection by comparing the selective pressure in cancers under different environmental conditions. For example: Why are certain genes only found mutated in one type of tissue but not in others? How does selection change from tissue to tissue, after treatment, in metastasis and as a consequence of microbial infection? A tumor consists of many different parts but the whole tumor is greater than the sum of its parts. We aim to understand the composition of a tumor beyond heterogeneity of malignant cells (for example, how do immune cells, microbes and fibroblasts integrate into this ecosystem) and how do system level properties of the cancer (such as resistance against therapy or migratory behaviour) emerge through interactions of its parts.

Tools for the age of precision medicine

Traditionally, genes have been studied in isolation. However, genes and proteins act together to create complex phenotypes. We develop resources and methods using tools from network and systems biology to better understand the interplay between genes. One particular interest of us is to better the understanding of how the rewiring of cellular signaling pathways leads to diseases such as cancer. We do so by developing bias reduced networks with high physiological relevance and appyling subnetwork finding algorithms to multi-omics cancer networks.