Division of Molecular Genome Analysis
Prof. Dr. Stefan Wiemann
Cancer and other human diseases arise from genetic aberrations that are either inherited or occur - as in most cancers – spontaneously in somatic cells. These defects cause abnormal activities of gene products that lead to malfunctioning of molecular and cellular interactions which, in consequence, may induce tumors and cause cancer progression.
The central objective of our division is to understand the complex molecular mechanisms that have evolved in the regulation of signaling networks and how these impact on cancer development, metastasis, and drug resistance. To this end, we generate and maintain resources for large-scale experimentation, apply high-throughput functional genomics and proteomics technologies, and analyze candidate genes using in vitro as well as in vivo systems. Effects of perturbations (gene gain- and loss-of-function, miRNA, drugs) imposed on the signaling processes are experimentally tested and then computationally modeled. This generates mechanistic knowledge that is exploited to identify new diagnostic and prognostic markers as well as to develop novel strategies for therapeutic intervention. Along these lines our major focus is on breast cancer, where we investigate protein and non-protein factors that are involved in the progression of different subtypes via their activities in interrelated signaling networks.
We have already seen from our current data that signaling is not regulated in isolated pathways but rather in complex networks. Therefore, in the future we will investigate the impact individual perturbations have in a variety of cellular pathways and at different levels (DNA, RNA, protein, metabolite, …, phenotype). This should provide us with a better understanding of the connectivity in multi-layer interaction systems. Such information will be inevitable, for example, to identify strategies that should help to overcome drug resistance.
While much of our current knowledge is based on in vitro experiments we need to validate findings in vivo in order to prove their relevance. To this end, we will generate and test animal models and challenge our hypotheses with patient samples. Collaborations to this end have already been established and first promising results have already been obtained.
Ward, A. et al. (2012) Re-expression of microRNA-375 reverses both tamoxifen resistance and accompanying EMT-like properties in breast cancer. Oncogene, doi:10.1038/onc.2012.128
Uhlmann, S. et al. (2012) Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer. Mol Syst Biol 8, 570
Henjes, F. et al. (2012) Strong EGFR signaling in cell line models of ERBB2-amplified breast cancer attenuates response towards ERBB2-targeting drugs. Oncogenesis 1, e16
Keklikoglou, I. et al. (2011) MicroRNA-520/373 family functions as a tumor suppressor in estrogen receptor negative breast cancer by targeting NF-kappaB and TGF-beta signaling pathways. Oncogene, doi:10.1038/onc.2012.128