Identification of predictive response and resistance factors to targeted therapy in gastric cancer using a systems medicine approach
Gastric cancer (GC) is the fifth most common cancer worldwide. Treatment options for GC 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 human epidermal growth factor receptor-2 (HER2) and epidermal growth factor receptor (EGFR) have shown mixed success in clinical trials. While the HER2 antibody trastuzumab has been approved for GC treatment, the EGFR antibody cetuximab recently failed in a phase III clinical trial as GC treatment.
The 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.
We will apply systematic molecular and, motility focused, cell phenotypic measurements. From these we derive probabilistic models of the basic signalling networks coupled to cellular phenotypes and agent based cellular behaviour models which give rise to emergent tumor characteristics. These models together with biostatistical methods will inform conclusions about the mechanisms leading to differences in the response of GC cell lines treated with trastuzumab or cetuximab, respectively. The models will be validated against 3D-cell culture and clinical sample derived molecular and morphometric tumor characteristics based on MALDI Imaging Mass Spectrometry, a powerful tool for investigating the distribution of molecules within tumor samples through the direct analysis of tissue sections. Validated models in conjunction with biostatistical methods will be used to derive multi-modal molecular profiles to predict potential response and resistance factors of HER2- and EGFR- target agents. These response predictors will be validated in tumor collectives of GC patients treated with either trastuzumab or cetuximab.
Subprojects in SYS-Stomach:
Keywords: Gastric carcinoma, targeted tumor therapy, response and resistance factors, biomarker, trastuzumab, cetuximab, Systems Medicin, computational model, machine learning