The German Cancer Research Center is the largest biomedical research institution in Germany. With more than 3,000 employees, we operate an extensive scientific program in the field of cancer research.
The Division of Signal Transduction and Growth Control is seeking a
Chronic inflammation is a main driver of the development of hepatocellular carcinoma (HCC), one of the most frequent and dismal human malignancies. Employing mouse models of inflammation-driven cancer including squamous cell carcinoma and HCC, our division has identified the receptor RAGE and its ligands, the damage molecular pattern (DAMP) molecules S100A8/A9 and HMGB1 as important players of chronic inflammation and cancer development (Gebhardt et al., 2008, J Exp Med 205:275-85; Nemeth et al., 2009, Hepatology 50:1251-62; Wiechert et al., 2012, Cell Comm Signal, 10:40; Pusterla et al., 2013, Hepatology, 58:363-73; de Ponti et al., 2015, Cancer Letters 369: 396-404; de Ponti et al., 2015 Int J Cancer 136:2458-63).
The current project aims at analysing the function of the receptor RAGE in liver progenitor cells during acute and chronic liver damage, hepatitis and development and progression of liver cancer. The candidate will apply gain-of-function and loss-of-function approaches in genetically modified mice and in vitro cell cultures to define the role of this molecule in chronic liver damage, inflammation, tumor cell formation and invasion using state-of-the-art transgenic and global gene expression technologies.
This project is part of the DFG funded research consortium TR-209 “Liver cancer” (https://www.livercancer.de/).
We are looking for a highly motivated and ambitious postdoc with a Ph.D. to complement our team. The candidate should have a strong background in molecular and cellular biology demonstrated by peer-reviewed publications and a strong interest in liver tumor biology. Excellent experimental and analytical skills in advanced cell culture techniques and mouse genetics including animal experimentation are mandatory. Familiarity with multi-omics datasets are a plus.