Dataset Information

Establishment of a human 3D lung cancer model based on a biological tissue matrix combined with a Boolean in silico model.

ABSTRACT: For the development of new treatment strategies against cancer, understanding signaling networks and their changes upon drug response is a promising approach to identify new drug targets and biomarker profiles. Pre-requisites are tumor models with multiple read-out options that accurately reflect the clinical situation. Tissue engineering technologies offer the integration of components of the tumor microenvironment which are known to impair drug response of cancer cells. We established three-dimensional (3D) lung carcinoma models on a decellularized tissue matrix, providing a complex microenvironment for cell growth. For model generation, we used two cell lines with (HCC827) or without (A549) an activating mutation of the epidermal growth factor receptor (EGFR), exhibiting different sensitivities to the EGFR inhibitor gefitinib. EGFR activation in HCC827 was inhibited by gefitinib, resulting in a significant reduction of proliferation (Ki-67 proliferation index) and in the induction of apoptosis (TUNEL staining, M30-ELISA). No significant effect was observed in conventional cell culture. Results from the 3D model correlated with the results of an in silico model that integrates the EGFR signaling network according to clinical data. The application of TGF?1 induced tumor cell invasion, accompanied by epithelial-mesenchymal transition (EMT) both in vitro and in silico. This was confirmed in the 3D model by acquisition of mesenchymal cell morphology and modified expression of fibronectin, E-cadherin, ?-catenin and mucin-1. Quantitative read-outs for proliferation, apoptosis and invasion were established in the complex 3D tumor model. The combined in vitro and in silico model represents a powerful tool for systems analysis.

SUBMITTER: Stratmann AT

PROVIDER: S-EPMC5528544 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Establishment of a human 3D lung cancer model based on a biological tissue matrix combined with a Boolean in silico model.

Stratmann Anna T AT Fecher David D Wangorsch Gaby G Göttlich Claudia C Walles Thorsten T Walles Heike H Dandekar Thomas T Dandekar Gudrun G Nietzer Sarah L SL

Molecular oncology 20131218 2

For the development of new treatment strategies against cancer, understanding signaling networks and their changes upon drug response is a promising approach to identify new drug targets and biomarker profiles. Pre-requisites are tumor models with multiple read-out options that accurately reflect the clinical situation. Tissue engineering technologies offer the integration of components of the tumor microenvironment which are known to impair drug response of cancer cells. We established three-di ...[more]

PMID: 24388494

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Project description: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.

Project description:BackgroundMulti-walled carbon nanotubes (MWCNT) have been shown to elicit the release of inflammatory and pro-fibrotic mediators, as well as histopathological changes in lungs of exposed animals. Current standards for testing MWCNTs and other nanoparticles (NPs) rely on low-throughput in vivo studies to assess acute and chronic toxicity and potential hazard to humans. Several alternative testing approaches utilizing two-dimensional (2D) in vitro assays to screen engineered NPs have reported conflicting results between in vitro and in vivo assays. Compared to conventional 2D in vitro or in vivo animal model systems, three-dimensional (3D) in vitro platforms have been shown to more closely recapitulate human physiology, providing a relevant, more efficient strategy for evaluating acute toxicity and chronic outcomes in a tiered nanomaterial toxicity testing paradigm.ResultsAs inhalation is an important route of nanomaterial exposure, human lung fibroblasts and epithelial cells were co-cultured with macrophages to form scaffold-free 3D lung microtissues. Microtissues were exposed to multi-walled carbon nanotubes, M120 carbon black nanoparticles or crocidolite asbestos fibers for 4 or 7 days, then collected for characterization of microtissue viability, tissue morphology, and expression of genes and selected proteins associated with inflammation and extracellular matrix remodeling. Our data demonstrate the utility of 3D microtissues in predicting chronic pulmonary endpoints following exposure to MWCNTs or asbestos fibers. These test nanomaterials were incorporated into 3D human lung microtissues as visualized using light microscopy. Differential expression of genes involved in acute inflammation and extracellular matrix remodeling was detected using PCR arrays and confirmed using qRT-PCR analysis and Luminex assays of selected genes and proteins.Conclusion3D lung microtissues provide an alternative testing platform for assessing nanomaterial-induced cell-matrix alterations and delineation of toxicity pathways, moving towards a more predictive and physiologically relevant approach for in vitro NP toxicity testing.

Dataset Information

Establishment of a human 3D lung cancer model based on a biological tissue matrix combined with a Boolean in silico model.

Publications

Establishment of a human 3D lung cancer model based on a biological tissue matrix combined with a Boolean in silico model.

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