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

 ,  ,  ,  ,  ,  ,  ,  , 
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

In This Article

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 of 3D environment on cell differentiation and also on signaling has been shown in several studies years ago (e.g., by Mina Bissell) 10,11. Today, many 3D cell culture models are available such as spheroid cultures, hydrogels or microfluidic chips 12-16. Even though these models enhance complexity compared to conventional 2D culture systems, they mostly lack a tissue microenvironment that is known to have tumor-supporting effects and also impacts drug efficacy.

To address this issue, we generated a 3D tumor model based on a biological scaffold called SISmuc (small-intestine-submucosa + mucosa) that is derived from a decellularized porcine jejunum. Thereby, the tissue architecture and important components of the ECM such as different collagens as well as the basement membrane structure are preserved 17. This unique feature is crucial for tumor model generation of carcinomas that arise from epithelia and comprise about 80% of solid tumors. Furthermore, the proliferation rate in our tissue-engineered tumor model is reduced compared to the artificially high rates achieved in 2D culture. As proliferation is an important parameter in assessing drug efficacy, drug testing is enabled in our model in more similar conditions to in vivo tumors 17.

In order to evaluate the potential of our model to predict biomarker-dependent drug efficacy correctly, we here present data for two different lung cancer cell lines that differ in their EGFR-biomarker status. This mutational status has started to be determined routinely in NSCLC patients. Targeted treatments with TKIs such as the EGFR-inhibitor gefitinib against tumors bearing an activating EGFR mutation show superior outcomes compared to those with platinum-based chemotherapy 18-21.

We established several read-out techniques that are relevant for evaluating compound efficacy. Furthermore, after TGF-beta-1 stimulation we are able to investigate compound actions in tumor cells that started the EMT process, which is thought to be an important step in malignant transformation 22,23 and which is connected to drug resistance 24.

The 3D tumor model allow monitoring cell-specific responses to targeted treatments, chemotherapy, or drug combinations with good contrasts. To further enhance and speed up drug-screening and to encounter resistance, this is complemented by an in silico simulation. Based on a few experiments, the tumor response can be predicted in silico regarding the outcome for a full range of drugs and their combinations.

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Protocol

1. Two-dimensional (2D) Cell Culture

  1. Commercially obtain tumor cell line HCC827 (DSMZ). Culture the lung adenocarcinoma cell line HCC827 (EGFR mutated, KRAS wild-type) in RPMI-1640 supplemented with 20% FCS. Change medium every 2 - 3 days. Split the cells twice a week. Cells are used until passage 20 is reached.
  2. Commercially obtain tumor cell line A549 (DSMZ). Culture the lung carcinoma cell line A549 (EGFR wild-type, KRAS mutated) in RPMI-1640 supplemented with 10% FCS. Perform the culture as stated above. Cells are used until passage 20 is reached.
  3. Test the cells regularly (every 4 - 6 weeks) for contaminations such as mycoplasma contaminations.
  4. Culturing A549 or HCC827 cells on glass coverslips
    1. Place 1 sterile glass coverslip in each well of a 24-well plate using a tweezer.
    2. Seed 50,000 tumor cells of both cell lines in a volume of 500 µl in the wells with the glass coverslips, respectively.
    3. Culture the cells until they reach a confluency of 70% under standard culture conditions (37 °C; 5% CO2). During this time, aspirate the old medium using a Pasteur pipette and add 500 µl fresh medium every 2 - 3 days of culture.

2. Generation of Tumor Test Systems on a Biological Collagen Scaffold

  1. Static Culture Conditions
    1. Generate the decellularized small-intestine-submucosa + mucosa (SISmuc) and fix it between two metal rings, the so-called cell crowns, as described in a previous publication25.
    2. Seed 100,000 cells of either HCC827 or A549 cells in a total volume of 500 µl onto the side of the former lumen of the gut fixed in cell crowns.
    3. Allow tumor cells to adhere for 2 hr at 37 °C, 5% CO2. Add 1 ml medium inside the cell crown and 1 ml outside.
    4. Culture the tumor model under static conditions at 37 °C, 5% CO2 in the incubator for 14 days. Change culture medium every 2 - 3 days. Therefore, aspirate the medium using a Pasteur pipette and pipette 1 ml fresh RPMI with the appropriate amount of FCS (A549: 10%, HCC827: 20%) inside the cell crown and 1.5 ml of the same medium outside.
  2. Dynamic Culture Conditions
    1. Set up the tumor test system with the decellularized matrix inside the cell crowns and cell seeding as described under subsection 2.1.1 to 2.1.3.
    2. Culture the tumor model under static conditions 37 °C, 5% CO2 in the incubator for 3 days.
    3. Assemble the autoclaved bioreactor and insert the seeded SISmuc as published previously 25.
    4. Pipette 45 ml RPMI with the appropriate amount of FCS (A549: 10%, HCC827: 20%) into the flask and connect the needle-free sampling device to the tubing system between the bioreactor and the medium flask.
    5. Place the bioreactor in the incubator, connect it to the pump and culture the tumor model for additional 14 days with a constant medium flow (3 ml/min) at 37 °C, 5% CO2.
    6. Change the entire culture medium after 7 days. For that, move the bioreactor from the incubator under a laminar flow hood. Aspirate the medium in the media flask using a Pasteur pipette. Lift up the bioreactor while aspirating to get rid of the medium in the tubing system. Pipette 45 ml fresh medium into the flask. Place the bioreactor back into the incubator and connect it to the pump.

3. Treatment of the Static Tumor Model with Gefitinib

  1. Culture the tumor model as described in section 2.1.
  2. At day 11 of culture, prepare RPMI/FCS with 1 µM gefitinib (2.5 ml for each cell crown). Aspirate the old medium from the wells using a Pasteur pipette and add 1 ml of prepared RPMI/FCS/gefitinib inside the cell crown and 1.5 ml of the same medium outside.
    NOTE: The thawed gefitinib stock solution can be stored at 4 °C for one week.
  3. Change the medium at day 13 as described under subsection 3.2.

4. Stimulation of the Static Tumor Model with TGF-beta-1 for EMT Induction

  1. Culture the tumor model as described in section 2.1.
  2. At day 3 of culture, prepare RPMI/FCS with 2 ng/ml TGF-beta-1 with carrier (2.5 ml for each cell crown). Aspirate the old medium from the wells using a Pasteur pipette and add 1 ml of prepared RPMI/FCS/TGF-beta-1 inside the cell crown and 1.5 ml of the same medium outside. Change medium supplemented with TGF-beta-1 every 2 - 3 days until day 14, as described under 4.2.

5. Read-outs

  1. Taking Samples for ELISA Measurements
    1. Take samples from the supernatants of static (section 2.1) and dynamic (section 2.2) culture during treatment with gefitinib (section 3) at days 11-14 of culture as indicated in Figure 2. Additionally, take a sample prior to treatment (T0).
    2. Under static culture conditions, take 100 µl sample from the inside of the cell crown. Under dynamic culture conditions, screw the syringe on the sampling device and take 1 ml medium out of the bioreactor system. Store all collected supernatants at -80 °C until the ELISA is performed.
  2. M30-ELISA for Apoptosis Quantification
    1. To quantify the apoptosis in the supernatants perform the cell death assay according to manufacturer's instructions. Allow all reagents to reach RT before performing the assay.
    2. Vortex all reagents. Dissolve the wash tablet in 500 ml fresh deionized water. Dilute HRP Conjugate with 9.2 ml of Conjugate Dilution Buffer and mix. Pipette 25 µl of standard or sample per well.
    3. Add 75 µl of the diluted HRP Conjugate solution per well. Cover the plate and incubate it on a shaker for 4 hr at RT. Wash the plate manually, 5 times with 250 µl of prepared wash solution.
    4. Add 200 µl of 3,3',5,5'-Tetramethylbenzidine (TMB) substrate to each well. Incubate in darkness at RT for 20 min. Add 50 µl of stop solution to each well. Shake the microplate for 10 sec and leave the microplate for 5 min before reading the absorbance. Determine the absorbance at 450 nm in a microplate reader. Calculate the standard curve and the concentrations in the samples using a suitable program for handling ELISA type data such as Origin.
  3. Sample Staining
    1. Fixing, Paraffin-embedding and Section Preparation of the 3D Model
      1. Fix the seeded SISmuc using 2.5 ml 4% paraformaldehyde (PFA) per cell crown for 2 hr and embed it into paraffin
      2. Prepare 3 µm tissue sections for staining as published previously 25.
    2. Fixing of Cells Grown in 2D Culture Conditions
      1. When 70% confluence is reached, wash cells cultured on glass coverslips in 24-well plate once with PBS and fix them with 500 µl 4% PFA/well for 10 min.
      2. Remove PFA and store the glass coverslips in the well-plate covered with PBS at 4 °C until staining is performed.
    3. H&E Staining
      1. Stain the rehydrated sections with Hematoxylin/Eosin as an overview staining according to standard protocols.
    4. Immunofluorescent Staining of the 3D Model
      1. For immunofluorescent staining, perform an antigen retrieval by placing the deparaffinized and rehydrated slides into a steam cooker with preheated citrate buffer (pH 6.0) for 20 min.
        NOTE: See table of Material and Equipment's for preparing of citrate buffer.
      2. Transfer the slides to the washing buffer (0.5 M PBS buffer + 0.5% Tween), circle sections with a PAP pen to minimize the required volume for staining solutions and place the slides in a moisture chamber.
      3. Block tissue sections with 5% serum from the host species of the secondary antibodies used in subsection 5.3.4.6 for 20 min at RT.
      4. Remove blocking serum by gently tapping the slides to a paper tissue and apply the primary antibody to the encircled sections in a dilution according to the manufacturer's instruction (rabbit anti Ki67 1:100, rabbit anti vimentin 1:100, mouse anti pan-cytokeratin 1:100). Incubate O/N at 4 °C. For double-staining, primary antibodies from different host species must be used.
      5. Carefully wash off unbound antibody with washing buffer three times for 5 min.
      6. For detection of specific antigen-antibody bindings, apply secondary antibodies in the dilution 1:400 to the sections and incubate at RT for 1 hr.
      7. Wash off unbound antibody with washing buffer three times for 5 min.
      8. Mount the slides with an aqueous medium containing DAPI to counterstain nuclei and let them dry O/N.
    5. Immunofluorescent Staining of 2D Cultured Cells
      1. Remove PBS and cover the seeded glass cover slips with 0.2% Triton-X100 in PBS for 5 min for permeabilization. Wash the glass cover slips with washing buffer (0.5 M PBS buffer + 0.5 % Tween) for 5 min.
      2. Perform steps 5.3.4.3 to 5.3.4.7. Execute all steps in the well-plate on a rocking platform and remove reagents by inverting the plate.
      3. For mounting, spot a drop of aqueous medium containing DAPI to counterstain nuclei onto an uncoated glass slide and transfer the cover slip to it with the cells facing the mounting medium using forceps. Let it dry before imaging.
  4. Determination of Proliferation Index
    1. Perform immunofluorescent staining against Ki67 according to section 5.3.4. or 5.3 5.
    2. Take fluorescent images of the tissue sections or 2D cultured cells with an inverted microscope. Use different filters for taking images of DAPI stainings and of Ki67 stainings. For the quantification, use 10 images of 3D sections or 5 images of 2D cultures (magnification 20X) of each condition from non-overlapping parts of the specimen.
    3. Determine number of nuclei and number of nuclei positive for Ki67 in 2D
      1. Count Ki67 positive nuclei manually using the plugin cell counter of the software Fiji 26. Therefore, open the image and click "Plugins" → "Analyze" → "Cell Counter". Press "Initialize" and select a counter type. Click on each Ki67 positive nucleus. The number of clicks is shown next to the selected counter type.
      2. For automatic cell counting of the total number of nuclei create a macro using Fiji 27,28. Adjust the macro for every staining and cell line. Following, see an example how to create a macro.
        1. Start macro recorder. Open a DAPI-image and convert it to 8 bit format by clicking "Image" → "Type" → "8-bit". Click "Enhance contrast", set 'saturated' as "1" and "normalize".
        2. Set "unsharp mask" to sharpen the image. Use a 'radius' of "1" and a 'mask' of "0.60". Click "auto threshold", choose method "RenyiEntropy" with 'white objects on black background' to binarise the image.
        3. Click "Plugins" → "BioVoxxel" → "watershed irregular features" with an erosion radius of "5" to separate the cells. Click "Analyze" → "Analyze Particles" and set the 'size' to "0.02-Infinity".
          NOTE: The number of particles/cells is shown in the summary table as counts.
        4. Click "Create" to save macro for the analysis of further images.
    4. Determine number of nuclei and number of nuclei positive for Ki67 in 3D manually as described in subsection 5.4.3.1.
    5. Calculate the mean proliferation index (PI) according to the following formula:
      Proliferation index formula Σ(number of Ki67/DAPI positive nuclei), mathematical equation.
      n = number of images (3D: 10, 2D: 5)

6. In Silico Tumor Model Generation and Simulation of Anti-EGFR Therapy in HCC827 Cells

  1. Network Generation using a Network Analysis Software
    1. Use different known databases to generate the tumor network based on the important experimental read-out parameters and the mutational background of the HCC827 cell line.
    2. Open the network analysis software 21 to visualize the network.
      1. Click "File" → "New" → type 'JoVE_tumor models' → click "OK" to generate a new network. Click "Square Closeup North" → "OK" to visualize the receptor in a double membrane. Click "Generic Protein" to visualize nodes (=proteins) as rectangles → type 'EGFR' → click "OK"; repeat this for each node in the network.
      2. Label the nodes in the network: Right click → "Change Color & Shape…" → select for nodes EGFR and (EGF-EGFR) color code "255,0,0" (color red); for nodes c-MET and (c-MET) color code "0,255,0" (color green); for gefitinib color code "255,255,0" (color yellow); for PI3K-Inh color code "204,204,204" (color light grey); for MEK-Inh color code "102,102,102" (color dark grey); for all other nodes in the network select color code "255,255,255" (color white).
      3. Click "Phenotype" to visualize read-out parameters as hexagons → type 'proliferation' → click "OK" → Right click → "Change Color & Shape…" → select color code "255,204,204" (color salmon); repeat this for parameter apoptosis → select color code "255,51,204" (color violet).
      4. Visualize Edges (= interactions): Click "State Transition" for activations (arrows), Click "Inhibition" for inhibitions (blunted arrows); repeat this for each interaction in the network.
    3. Click "File" → "Save As…" → type 'JoVE_tumor models.xml' → click "Save" to save the generated signaling network as .xml format.
  2. In Silico Simulation using SQUAD
    NOTE: SQUAD represents the network topology as a discrete logical Boolean system (AND, OR, NOT) and performs a steady state analysis. The steady state analysis reflects the system equilibrium and the responsibility upon signaling stimuli (e.g., drug application or mutation).
    1. Open SQUAD 17 software, Click "Load Network" → upload 'JoVE_tumor models.xml' file. Click "Run analysis":
      NOTE: Next, SQUAD applies exponential function to interpolate the Boolean network connectivity, which allows dynamic simulation of the network behavior over time (colored sigmoid activity curves).
    2. Click "Advanced" → "Perturbator" to write a simulation protocol. Simulate anti-EGFR resistance: Click "Edit Protocol"
      1. Click "perturbation" → use standard "initialstate=SS-4" (steady state 4) → Click "Add" to add 'new Constant Pulse' → Select parameter "state" → Select target "FLIP" → Select time "0" → Select value "0.4" → Click "OK".
      2. Repeat this: Click "Add" → Select parameter "state" → Select target "c-MET" → Select time "0" → Select value "0.4" → Click "OK"; Click "Add" → Select parameter "state" → Select target "gefitinib" → Select time "0" → Select value "1" → Click "OK".
      3. Set a state value to active node (EGF-EGFR): Click "activenode" → Click "Edit" → Select state "0.8" → Click "OK"; for all other nodes: Click "Edit" → Select state "0" → Click "OK". Click "OK" to save the protocol.
      4. Display specific curves: Go to panel "Options" → Select "(EGF-EGFR)", "*(EGF-EGFR)", "*MEK", "caspases", "(c-MET)", "PI3K", "gefitinib", "apoptosis", "proliferation", "MEK-Inh" and "PI3K-Inh" for graphical representation. Click "Initialize" → "Run" to start a complete simulation of the system's response. Click "Reset" to start a new simulation.
    3. Simulate combined PI3K and MEK inhibitor treatment: Click "Edit Protocol"
      1. Use same node parameter as described under 6.2.2.1 - 6.2.2.3. Click "perturbation" → Click "Add" to add 'new Constant Pulse' → Select parameter "state" → Select target "PI3K-Inh" → Select time "0" → Select value "1" → Click "OK"; Click "Add" to add 'new Constant Pulse' → Select parameter "state" → Select target "MEK-Inh" → Select time "0" → Select value "1" → Click "OK".
      2. Click "OK" to save the protocol. Go to panel "Options": Use same nodes for graphical representation as described under 6.2.2.4. Click "Initialize" → "Run" to start a complete simulation of the system's response. Click "Reset" to finish the simulation.

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Results

On the basis of the SISmuc scaffold (Figure 2A to C), we established a standardized operating protocol for the generation, stimulation and treatment of a 3D tumor test system (Figure 2D). This model enables the determination of the proliferation index and the quantification of apoptosis using M30-ELISA as shown in Figure 1 and Figure 3, respectively. Figure 3 shows representative H&E staining of A549 ...

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Discussion

We have established a combined in vitro/in silico tumor test system for biomarker-guided treatment predictions. The in vitro model evaluates different important aspects of compound actions such as changes of tumor cell proliferation and apoptosis on a specific mutational background that can also be simulated in silico17. Here, we present the standardized protocol for 3D tumor model generation and compound testing including quantification of proliferation and apoptosis and the establi...

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Disclosures

The authors have nothing to disclose.

Acknowledgements

This research was sponsored by the Center for Interdisciplinary Clinical Research (IZKF, grant BD247) of the University Hospital of Wuerzburg and the Bayern Fit program (granted to Heike Walles).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
BioreactorsChair of Tissue Engineering and Regenerative Medicine, Würzburg (GER)Bioreactor setup
BioVoxxel Toolbox (ImageJ / Fiji)Jan Brocher, Thorsten Wagner, https://github.com/biovoxxel/BioVoxxel_Toolbox
Cell crownsChair of Tissue Engineering and Regenerative Medicine, Würzburg (GER)for static 3D culture
CellDesignerhttp://www.celldesigner.org/-This software was used for drawing the network.
Citrate buffer stock solution (10x)in house production42 g/L Citric acid monohydrate, 17.6 g/L Sodium hydroxide pellets in deionized water, pH 6.0, stored at RT. 
Citrate buffer working solutionin house production10% Citrate buffer stock solution in demineralized water, stored at RT.
Citric acid monohydrateVWR, Darmstadt (GER)1002441000used for the citrate buffer
Cover slipsVWR, Darmstadt (GER)631-1339
DAPI Fluoromount-GTMSouthernBiotech, Birmingham (USA)SBA-0100-20
Databases such as KEGG, HPRD and QIAGEN (Genes & Pathways)http://www.genome.jp/kegg/pathway.html; http://www.hprd.org/; https://www.qiagen.com/de/geneglobe/-Different known literature databases were used for generating the network topology.
Female Luer Lug Style TeeMednet, Münster (GER)FTLT-1Bioreactor setup
Female Luer Thread Style with 5/16" Hex to 1/4-28 UNF ThreadMednet, Münster (GER)SFTLL-J1A Bioreactor setup
Fetal Calf SerumBio&SELL, Feucht (GER)FCS.ADD.0500not heat-inactivated
GefitinibAbsource Diagnostics GmbH, München (GER)S1025-100 mg100 mM stock solution with DMSO
Glas flask (Schott, GER) provided with glas hose connectionWeckert, Kitzingen (GER)custom made
Histofix 4% (Paraformaldehyd)Carl Roth, Karlsruhe (GER)P087.1
Hose couplingMednet, Münster (GER)CC-9Bioreactor setup
Incubator for bioreactorsChair of Tissue Engineering and Regenerative Medicine, Würzburg (GER)Bioreactor setup
M30 CytoDeathTM ELISAPeviva, Bromma (SWE)10900
Male Luer Integral Lock RingMednet, Münster (GER)MTLL230-J1ABioreactor setup
Moisture chambercustom made
Mouse anti Pan-CytokeratinSigma-Aldrich, Munich (GER)  C2562-2MLClone C-11+PCK-26+CY-90+KS-1A3 +M20+A53-B/A2, used 1/100 for immunofluorescence
Needlefree Swabable Valve Female LuerMednet, Münster (GER)NVFMLLPCBioreactor setup, for sampling, gamma-sterilized
O-Ring MVQ 10 red 37 x 3 mmArcus Dichtelemente, Seevetal (GER)21444O-ring large, Bioreactor setup
O-Ring MVQ 70 red 27 x 2.5 mmArcus Dichtelemente, Seevetal (GER)19170O-ring small, Bioreactor setup
PAP penDako, Hamburg (GER)S002
ParaffinCarl Roth, Karlsruhe (GER)6642.6
Peristaltic pumpIsmatec, Wertheim-Mondfeld (GER)Bioreactor setup
Phosphate Buffered SalineSigma-Aldrich, Munich (GER)  D8537-6x500ml
Pump tubing cassetteIsmatec, Wertheim (GER)IS 3710Bioreactor setup
Rabbit anti Ki67Abcam, Cambridge (UK)ab16667Clone SP6, used for 1/100 for IF
Rabbit anti VimentinAbcam, Cambridge (UK)ab92547used 1/100 for IF
RPMI-1640 mediumLife technologies, Darmstadt (GER)61870-044warm in 37 °C waterbath before use
Silicone tubeCarl Roth GmbH, Karlsruhe (GER)HC66.1Bioreactor setup
Sodium HydroxideSigma-Aldrich, München (GER)30620-1KG-Rused for the citrate buffer
SQUADhttp://sbos.eu/docu/docu/SQUAD/doku.php.htm-This software was used for performing the semiquantitative simulations.
Sterile air filter, pore size 0.2 µmSartorius Stedium Biotech, Göttlingen (GER)16596-HYKBioreactor setup
Syringe Luer Lok 5 mlBD Biosciences, Heidelberg (GER)309649for bioreactor sampling
Tissue culture test plates: 6-,      12-, 24-, 96- wellTPP Techno Plastic Products AG, Trasadingen (GER)92006, 92012, 92024, 92048 
Transforming growth factor-beta 1 (TGF-β1) with carrierCell Signaling, Frankfurt (GER)8915LCstock solution in sterile citrate buffer pH 3.0
Triton X-100Sigma-Aldrich, München (GER)X100-1L
Tween-20Sigma-Aldrich, München (GER)P7949-500mlfor washing buffer of immunofluorescent staining

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3D Lung Tumor ModelDrug Response PredictionEGFR Mutated CellsGefitinib TreatmentKi67 Proliferation IndexM30 ELISA ApoptosisEpithelial Mesenchymal TransitionFlow Bioreactor CultureBoolean In Silico Model

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