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A combined tissue-engineered/in silico signature tool patient stratification in lung cancer

: Göttlich, Claudia; Kunz, Meik; Zapp, Cornelia; Nietzer, Sarah L.; Walles, Heike; Dandekar, Thomas; Dandekar, Gudrun

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Molecular oncology 12 (2018), Nr.8, S.1264-1285
ISSN: 1574-7891
ISSN: 1878-0261
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer ISC ()
tissue engineering; Lungenkrebs; In silico; chemische Beständigkeit

Patient‐tailored therapy based on tumor drivers is promising for lung cancer treatment. For this, we combined in vitro tissue models with in silico analyses. Using individual cell lines with specific mutations, we demonstrate a generic and rapid stratification pipeline for targeted tumor therapy. We improve in vitro models of tissue conditions by a biological matrix‐based three‐dimensional (3D) tissue culture that allows in vitro drug testing: It correctly shows a strong drug response upon gefitinib (Gef) treatment in a cell line harboring an EGFR‐activating mutation (HCC827), but no clear drug response upon treatment with the HSP90 inhibitor 17AAG in two cell lines with KRAS mutations (H441, A549). In contrast, 2D testing implies wrongly KRAS as a biomarker for HSP90 inhibitor treatment, although this fails in clinical studies. Signaling analysis by phospho‐arrays showed similar effects of EGFR inhibition by Gef in HCC827 cells, under both 2D and 3D conditions. Western blot analysis confirmed that for 3D conditions, HSP90 inhibitor treatment implies different p53 regulation and decreased MET inhibition in HCC827 and H441 cells. Using in vitro data (western, phospho‐kinase array, proliferation, and apoptosis), we generated cell line‐specific in silico topologies and condition‐specific (2D, 3D) simulations of signaling correctly mirroring in vitro treatment responses. Networks predict drug targets considering key interactions and individual cell line mutations using the Human Protein Reference Database and the COSMIC database. A signature of potential biomarkers and matching drugs improve stratification and treatment in KRAS‐mutated tumors. In silico screening and dynamic simulation of drug actions resulted in individual therapeutic suggestions, that is, targeting HIF1A in H441 and LKB1 in A549 cells. In conclusion, our in vitro tumor tissue model combined with an in silico tool improves drug effect prediction and patient stratification. Our tool is used in our comprehensive cancer center and is made now publicly available for targeted therapy decisions.