title: A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds.
abstract: In the present study, we combined an in vitro 3D lung tumor model with an in silico model to optimize predictions of drug response based on a specific mutational background. The model is generated on a decellularized porcine scaffold that reproduces tissue-specific characteristics regarding extracellular matrix composition and architecture including the basement membrane. We standardized a protocol that allows artificial tumor tissue generation within 14 days including three days of drug treatment. Our article provides several detailed descriptions of 3D read-out screening techniques like the determination of the proliferation index Ki67 staining's, apoptosis from supernatants by M30-ELISA and assessment of epithelial to mesenchymal transition (EMT), which are helpful tools for evaluating the effectiveness of therapeutic compounds. We could show compared to 2D culture a reduction of proliferation in our 3D tumor model that is related to the clinical situation. Despite of this lower proliferation, the model predicted EGFR-targeted drug responses correctly according to the biomarker status as shown by comparison of the lung carcinoma cell lines HCC827 (EGFR -mutated, KRAS wild-type) and A549 (EGFR wild-type, KRAS-mutated) treated with the tyrosine-kinase inhibitor (TKI) gefitinib. To investigate drug responses of more advanced tumor cells, we induced EMT by long-term treatment with TGF-beta-1 as assessed by vimentin/pan-cytokeratin immunofluorescence staining. A flow-bioreactor was employed to adjust culture to physiological conditions, which improved tissue generation. Furthermore, we show the integration of drug responses upon gefitinib treatment or TGF-beta-1 stimulation - apoptosis, proliferation index and EMT - into a Boolean in silico model. Additionally, we explain how drug responses of tumor cells with a specific mutational background and counterstrategies against resistance can be predicted. We are confident that our 3D in vitro approach especially with its in silico expansion provides an additional value for preclinical drug testing in more realistic conditions than in 2D cell culture.
authors.affiliation: Department of Tissue Engineering and Regenerative Medicine (TERM), University Hospital Wuerzburg.
authors.forename: Claudia
authors.initials: C
authors.lastname: Göttlich
authors.affiliation: Department of Tissue Engineering and Regenerative Medicine (TERM), University Hospital Wuerzburg.
authors.forename: Lena C
authors.initials: LC
authors.lastname: Müller
authors.affiliation: Department of Bioinformatics, University Wuerzburg.
authors.forename: Meik
authors.initials: M
authors.lastname: Kunz
authors.affiliation: Department of Tissue Engineering and Regenerative Medicine (TERM), University Hospital Wuerzburg.
authors.forename: Franziska
authors.initials: F
authors.lastname: Schmitt
authors.affiliation: Department of Tissue Engineering and Regenerative Medicine (TERM), University Hospital Wuerzburg; Translational Center Wuerzburg, Fraunhofer Institute Interfacial Engineering and Biotechnology IGB.
authors.forename: Heike
authors.initials: H
authors.lastname: Walles
authors.affiliation: Department of Cardiothoracic Surgery, University Hospital Wuerzburg.
authors.forename: Thorsten
authors.initials: T
authors.lastname: Walles
authors.affiliation: Department of Bioinformatics, University Wuerzburg; dandekar@biozentrum.uni-wuerzburg.de.
authors.forename: Thomas
authors.initials: T
authors.lastname: Dandekar
authors.affiliation: Department of Tissue Engineering and Regenerative Medicine (TERM), University Hospital Wuerzburg; Translational Center Wuerzburg, Fraunhofer Institute Interfacial Engineering and Biotechnology IGB; gudrun.dandekar@uni-wuerzburg.de.
authors.forename: Gudrun
authors.initials: G
authors.lastname: Dandekar
authors.affiliation: Department of Tissue Engineering and Regenerative Medicine (TERM), University Hospital Wuerzburg.
authors.forename: Sarah L
authors.initials: SL
authors.lastname: Nietzer
mesh_terms: D000230:Adenocarcinoma / Q000188:drug therapy* / Q000235:genetics / Q000473:pathology; D000818:Animals; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D017209:Apoptosis / Q000187:drug effects; D045744:Cell Line, Tumor; D049109:Cell Proliferation; D019008:Drug Resistance, Neoplasm; D004797:Enzyme-Linked Immunosorbent Assay; D058750:Epithelial-Mesenchymal Transition / Q000187:drug effects; D066246:ErbB Receptors / Q000235:genetics; D000077156:Gefitinib; D006801:Humans; D021621:Imaging, Three-Dimensional; D008175:Lung Neoplasms / Q000188:drug therapy* / Q000235:genetics / Q000473:pathology; D009154:Mutation; D047428:Protein Kinase Inhibitors / Q000494:pharmacology; D011505:Protein-Tyrosine Kinases / Q000037:antagonists & inhibitors*; D016283:Proto-Oncogene Proteins p21(ras) / Q000235:genetics; D011799:Quinazolines / Q000627:therapeutic use*; D013552:Swine; D023822:Tissue Engineering; D053773:Transforming Growth Factor beta1
chemical_list: D000970:Antineoplastic Agents; C117307:KRAS protein, human; D047428:Protein Kinase Inhibitors; D011799:Quinazolines; D053773:Transforming Growth Factor beta1; D066246:ErbB Receptors; D011505:Protein-Tyrosine Kinases; D016283:Proto-Oncogene Proteins p21(ras); D000077156:Gefitinib