Pneumologie 2015; 69 - A72
DOI: 10.1055/s-0035-1556664

The influence of EGF/HGF receptor abundance on therapy resistance in NSCLC cell lines

F Salopiata 1, 2, H Hass 3, RM Huber 4, 5, J Timmer 3, U Klingmüller 1, 2
  • 1Division of Systems Biology of Signal Transduction, DKFZ Heidelberg
  • 2Translational Lung Research Center Heidelberg (TLRC)
  • 3Centre for Systems Biology, University of Freiburg
  • 4Division of Respiratory Medicine and Thoracic Oncology, Ludwig-Maximilians-University Munich
  • 5Comprehensive Pneumology Center München (CPC-M)

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide. Unfortunately, the currently available systemic therapies including chemotherapy and epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) treatment lead to only a few months increased survival due to resistance formation. It is suspected that both resistances can be mediated by EGF and hepatocyte growth factor (HGF) interaction. Yet, the mechanism of this EGF/HGF mediated resistance remains unknown.

In this study time resolved quantitative data for EGF and HGF induced signal transduction was acquired in NSCLC cell lines and used to develop a dynamic pathway model to gain insights into mechanisms regulating the interaction of the receptors and their crosstalk. The three NSCLC cell lines examined harbored different mutations in the EGFR and differed with respect to the Met receptor expression level. Comparing the data of the three cell lines, our mathematical model suggested an important role of the abundance of the receptors for the formation of signaling complexes and downstream signal activation. Our results showed that the difference in receptor abundance explained two cell type specific effects of the EGF/HGF interaction: First the transphosphorylation of the Met receptor via the EGFR and second the sustained activation of the Met receptor after HGF and EGF co-treatment. Accordingly, further experiments with EGFR TKIs and the analysis of the kinetics of receptor degradation supported our model-based insights. With this strategy we propose a novel approach to predict the clinical outcome of applied therapies and to develop strategies to avoid the emergence of therapy resistance.

*Presenting author