A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Claudia Göttlich; Lena C. Müller; Meik Kunz; Franziska Schmitt; Heike Walles; Thorsten Walles; Thomas Dandekar; Gudrun Dandekar; Sarah L. Nietzer

doi:10.3791/53885

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Method Article

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

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DOI:

10.3791/53885

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April 6th, 2016

Claudia Göttlich^*¹ , Lena C. Müller^*¹ , Meik Kunz^*³ , Franziska Schmitt¹ , Heike Walles¹^,⁴ , Thorsten Walles² , Thomas Dandekar³ , Gudrun Dandekar¹^,⁴ , Sarah L. Nietzer¹

¹Department of Tissue Engineering and Regenerative Medicine (TERM), University Hospital Wuerzburg, ²Department of Cardiothoracic Surgery, University Hospital Wuerzburg, ³Department of Bioinformatics, University Wuerzburg, ⁴Translational Center Wuerzburg, Fraunhofer Institute Interfacial Engineering and Biotechnology IGB

^* These authors contributed equally

Summary

We present a three-dimensional (3D) lung cancer model based on a biological collagen scaffold to study sensitivity towards non-small-cell-lung-cancer-(NSCLC)-targeted therapies. We demonstrate different read-out techniques to determine the proliferation index, apoptosis and epithelial-mesenchymal transition (EMT) status. Collected data are integrated into an in silico model for prediction of drug sensitivity.

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.

Introduction

The pharmaceutical industry is facing high attrition rates of up to 95% in the field of cancer treatment in the clinical phase causing enormous costs ^1-5. One reason for this deficit is the fact that currently efficacy of potential new compounds is assessed in large-scale screenings on 2D cell cultures of cancer cell lines or in animal models. Animal models have a higher complexity but there are crucial differences between mice and men ^6,7. In the last decade, 3D cancer models using different approaches have been generated to bridge the gap between 2D culture of cancer cell lines and a complex in vivo tumor ^6,8,9. The impact o....