IG Cellular Systems Genomics works on
the ERBB gene interaction network
(Breast) cancer progression as well as the development of drug resistance are processes that involve mechanisms taking place in different time frames. Fast events, like the dynamic and spatial interaction of proteins as well as the activation of signaling via phosphorylation cascades, take place in seconds to minutes, while changes at the genome (mutations, CNVs) as well epigenome levels (promoter methylation) take longer to develop and manifest. IG-CSG is structured in a number of subprojects and applies functional genomic and proteomic technologies in order to capture the range of cellular events leading to cancer, metastasis, and drug resistance (timeline in the figure on the left).
The major focus of research carried out in IG-CSG is on growth-factor (EGFR/ERBB) (figure on the right) as well as hormone (ER) signaling in ERBB2 amplified, ER+, and triple negative breast cancer subtypes.
 The ERBB-signaling network and major entry-points of IG-CSG subprojects (SPs numbered in circles): IG-CSG's initial focus had been mostly on short-time events that are directly involved in signaling, including alterations in gene expression, and that mostly take place in seconds to days (depicted on the right). This has since been expanded to cover also longer lasting events, namely epigenetic and genomic changes (mutation, CNV) as well as the analysis of combined effects in patients. These take longer times to develop and are highly relevant for tumor progression as well as the establishment and manifestation of drug resistance processes. Subproject numbers along the time-line (on the left) indicate that the technologies and applications established in IG-CSG are able to capture events taking place in the different and relevant time frames. Thus, the regulation and feedback regulation of signaling are comprehensively investigated in the subprojects of IG-CSG and are all aimed at generating a comprehensive view on major subtypes of breast cancer by integrating clinical data with in vitro models.
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