Individualized drug response predictions based on a combined in silico and in vitro lung cancer model on a biological scaffold

Facing heterogeneity of cancer, individualized treatment options are emerging in therapy regimens. To identify the patient population that is most likely to benefit from a specific targeted therapy and to specify new drug targets, we developed a pre-clinical 3D lung cancer (LC) model based on a decellularized biological matrix. This kind of tumor tissue model reflects tissue characteristics such as a biological tissue architecture and ECM composition, including a preserved basement membrane that is important for modeling epithelial cancers. Additionally and in contrast to 2D conditions, we observed a lower proliferation rate that correlates to patient’s tumors. These aspects of our models have major implications for drug-responses and should improve pre-clinical testing. To represent different response groups of LC patients towards tyrosine kinase inhibitors (TKI), we used cell lines, harboring specific driver mutations. Upon gefitinib treatment, we could show an increase of apoptosis and a reduced proliferation in the cell line harboring an EGFR mutation (HCC827) which was not observed in EGFR wt cells (A549, H441). For target predictions we connected our 3D test system with an in silico model based on the EGFR signaling network and determined activation changes of 49 receptor TKs and 43 phospho-kinases. To model more advanced tumor stages, we induced epithelial to mesenchymal transition (EMT) by TGFβ1 that is connected to invasive processes. The Boolean in silico model correctly calculates the observed system responses such as apoptosis, proliferation and EMT. Furthermore, we generated gefitinib resistant HCC827 cells as a basis for testing drugs, that were predicted by the in silico model as being effective after TKI resistance