Project Overview and Scientific Background

Gastric cancer is estimated to be the fourth most common cancer and second leading cause of death from cancer worldwide. Treatment options for gastric cancer patients include surgery, chemotherapy and radiation therapy; however, the overall survival rate remains unsatisfactory and new treatment options are urgently required. Novel drugs targeting members of a family of receptor tyrosine kinases including HER2 and EGFR have shown mixed success in clinical trials. While the HER2 antibody trastuzumab has been approved for gastric cancer treatment, the EGFR antibody cetuximab recently failed in a phase III clinical trial as a gastric cancer treatment, but is approved for colorectal cancer treatment.

Supported by Germany's Federal Ministry of Education and Research (BMBF), the SYS-Stomach consortium aims to answer clinical questions about predictive factors regarding gastric carcinoma HER2-positive trastuzumab responders, cetuximab responders, and specific differences in reaction to HER2 and EGFR targeted treatments.

The SYS-Stomach project builds on the knowledge gained regarding cetuximab in an earlier BMBF project, CANCERMOTISYS, and will further systematically characterize gastric cancer cell lines in response to treatment with cetuximab as well as trastuzumab. The effects of the treatment will be determined both on the phenotypic level (motility and invasion assays) and on the molecular level (transcriptomics, epigenomics, proteomics). Time-resolved effects of the treatment on the EGFR and HER2 signaling pathways will be monitored. Candidate response and risk factors to targeted therapy will be validated in vitro and in vivo.


  • Aichler M, Luber B, Lordick F, Walch A. Proteomic and metabolic prediction of response to therapy in gastric cancer. World J Gastroenterol. 2014; 20:13648-57.
  • Aichler M, Motschmann M, Jütting U, Luber B, Becker K, Ott K, Lordick F, Langer R, Feith M, Siewert JR, Walch A. Epidermal growth factor receptor (EGFR) is an independent adverse prognostic factor in esophageal adenocarcinoma patients treated with cisplatin-based neoadjuvant chemotherapy. Oncotarget. 2014; 5:6620-32.
  • Feuchtinger A, Stiehler T, Jütting U, Marjanovic G, Luber B, Langer R, Walch A. Image analysis of immunohistochemistry is superior to visual scoring as shown for patient outcome of esophageal adenocarcinoma. Histochem Cell Biol. 2015; 143:1-9. Epub 2014 Aug 26.
  • Hasenauer, J., Jagiella, N., Hross, S., & Theis, F. J. (2015). Data-driven modelling of biological multi-scale processes. Journal of Coupled Systems and Multiscale Dynamics, 3(2):101-121.
  • Hug, S., Schwarzfischer, M., Hasenauer, J., Marr, C., & Theis, F. J. (2015). An adaptive scheduling scheme for calculating Bayes factors with thermodynamic integration using Simpson’s rule. Statistics and Computing, 1-15.
  • Fröhlich, F., Kaltenbacher, B., Theis, F. J., & Hasenauer, J. (2015). Scalable parameter estimation for genome-scale biochemical reaction networks, submitted to Bioinformatics
  • Geissen, E.-M., Hasenauer, J., Heinrich, S., Hauf, S., Theis, F. J., & Radde, N. (2015). MEMO - Multi-experiment mixture model analysis of censored data, submitted to Bioinformatics
  • Fröhlich, F., Thomas, P., Kazeroonian, A., Theis, F. J., Grima, R., & Hasenauer J. (2015). Inference for stochastic chemical kinetics using moment equations and system size expansion, submitted to PLoS Computational Biology
  • S.C. Binder, E.A. Hernandez-Vargas, M. Meyer-Hermann, Reducing complexity: An iterative strategy for parameter determination in biological networks, Computer Physics Communications 190, 2015: 15.
  • H. Kempf, M. Bleicher, M. Meyer-Hermann, Spatio-Temporal Dynamics of Hypoxia during Radiotherapy. PLoS ONE 10, 2015: e0133357.
  • A. Ghanbari, J. Dehghany, T. Schwebs, M. Müsken, S. Häussler, M. Meyer-Hermann, Initial factors determine the formation of mushroom-like biofilms by Pseudomonas aeruginosa, Submitted 2015.
  • H. Hatzikirou, J.C.L. Alfonso, S. Mühle, C. Stern, S. Weiss, M. Meyer-Hermann, Cancer therapeutic potential of combinatorial immuno- and vaso-modulatory interventions, J. Roy. Soc. Interface 12 2015: 20150439.
  • Buck A, Ly A, Balluff B, Sun N, Gorzolka K, Feuchtinger A, Janssen KP, Kuppen PJ, van de Velde CJ, Weirich G, Erlmeier F, Langer R, Aubele M, Zitzelsberger H, Aichler M, Walch A. High-resolution MALDI-FT-ICR MS Imaging for the analysis of metabolites from formalin-fixed paraffin-embedded clinical tissue samples. J Pathol. 2015, May 12.
  • Balluff B, Frese CK, Maier SK, Schöne C, Kuster B, Schmitt M, Aubele M, Höfler H, Deelder AM, Heck AJ, Hogendoorn PC, Morreau J, Altelaar AF, Walch A, McDonnell LA. De novo discovery of phenotypic intra-tumour heterogeneity using imaging mass spectrometry. J Pathol. 2015; 235:3-13.
  • Ly A, Buck A, Balluff B, Sun N, Gorzolka K, Feuchtinger A, Janssen KP, Kuppen PJ, van de Velde CJ, Weirich G, Erlmeier F, Langer R, Aubele M, Zitzelsberger H, Aichler M, Walch A. High-resolution MALDI-FT-ICR MS Imaging for the analysis of metabolites from formalin-fixed paraffin-embedded clinical tissue samples. Nat Protocols (accepted).
  • Buck A, Balluff B, Voss A, Langer R, Zitzelsberger H, Aichler M, Walch A. How Suitable is Matrix-Assisted Laser Desorption/Ionization-Time-of-Flight for Metabolite Imaging from Clinical Formalin-Fixed and Paraffin-Embedded Tissue Samples in Comparison to Matrix-Assisted Laser Desorption/Ionization-Fourier Transform Ion Cyclotron Resonance Mass Spectrometry? Anal Chem. 2016 May 2.
  • Hross S, Hasenauer J. Analysis of CFSE time-series data using division-, age- and label-structured population models. Bioinformatics, March 2016
  • Binder SC, Eckweiler D, Schulz S, Bielecka A, Nicolai T, Franke R, Häussler S, Meyer-Hermann M, Functional modules of sigma factor regulons guarantee adaptability and evolvability, Scientific Reports 6: 22212, 2016.
  • Sukhorukov VM, Meyer-Hermann M, Structural Heterogeneity of Mitochondria Induced by the Microtubule Cytoskeleton. Sci. Rep. 5: 13924, 2015
  • Nguyen VK, Binder SC, Boianelli A, Meyer-Hermann M, Hernandez-Vargas EA, Ebola virus infection modeling and identifiability problems. Front. Microbiol. 6: 257, 2015
  • Boianelli A, Nguyen VK, Ebensen T, Schulze K, Wilk E, Sharma N, Stegemann-Koniszewski S, Bruder D, Toapanta FR, Guzmán CA, Meyer Hermann M, Hernandez-Vargas EA, Modeling Influenza Virus Infection: A Roadmap for Influenza Research. Viruses 7: 5274-5304, 2015.