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

The overall goals of this combined In Vitro/In Silico Lung Cancer Model are to study the sensitivity of non-small cell lung cancer targeted therapies and to predict the efficacy of new substances. This 3-D model system allows the monitoring of cell specific responses to targeted treatments or direct combinations. The main advantage of this technique is that the covered with in Silico predictions can facilitate pre-clinical drug testing in a more physiological relevant environment.

The implications of this technique extend towards the therapy of different tumor entities, as it can be used to optimize drug response predictions based on specific mutational backgrounds. Demonstrating the procedure will be, Franziska Schmitt, a research assistant, from our laboratory. To set up a dynamic culture, begin by culturing the tumor model under static culture conditions, in a cell crown at 37 degrees Celsius and 5%CO2, in a conventional incubator.

After three days, assemble an Autoclaved Bioreactor, and insert a seeded, biological small intestine submucoso plus mucosa scaffold, into the system. Next, add 45 milliliters of RPMI, supplemented with the appropriate amount of FCS to a flask and connect the needle free sampling device to the tubing system between the Bioreactor and the flask of medium. Place the Bioreactor into the incubator and connect it to the pump to culture the tumor model, for an additional 14 days, with a constant medium flow of three milliliters per minute.

To treat a static tumor model with, Gefitinib, on day 11 of culture in the conventional incubator, first use a pasteur pipet to remove the old medium, from the wells. Then, add one milliliter of freshly prepared, RPMI, supplemented with, FCS and Gefitinib, to the inside of each cell crown and 1.5 milliliters to the bottom of each well. To collect Elisa's samples from a dynamic culture, on days 11 through 14 of culture, attach the syringe to the sampling device and remove one milliliter of medium from the Bioreactor system.

Then store the samples at, minus 80 degrees Celsius, until the Elisa is performed. To determine the number of Ki67 positive nuclei, first use an inverted microscope to obtain five DAPI and five Ki67 staining fluorescent images of 2D cultures at a 20 fold magnification of each condition, from non-overlapping sections of the specimen. To manually count the Ki67 positive nuclei, open emerged image in Fiji and select, Plugins, and Cell Counter, then, click initialize, select a counter type and click on each Ki67 positive nucleus.

The number of clicked cells will appear next to the selected counter type. For automatic cell counting of the total number of nuclei in the 2D images, start the macro recorder, open a DAPI image, and click image, type and 8-bit, then click enhance contrast and set the saturated parameter to one and normalize. To sharpen the image, set the unsharp mask, using a radius of one and a mask of 60.

To binderize the image, click auto threshold and choose the, Reny/Entropy method with white objects on black background. Then, click Plugins, followed by BioVoxxel, and watershed irregular feature, with an erosion radius of five to separate the cells. Click analyze, and analyze particles and set the size to 02 infinity and check summarize.

The number of cells will be shown in the summary table as counts. Then, click create, to save the macro to allow automatic counting of nuclei, in further 2D cell culture images. After using know databases to generate a tumor network, open the appropriate network analysis software, click file and new, and input jove tumor models.

Click ok, to generate a new network, then click, square closeup North and ok, to visualize the receptor in a double membrane. To visualize the nodes as rectangles, click generic protein, then EGFR, then Ok.To label the nodes in the network, right-click to change color and shape, and set the node color codes as indicated. To visualize the readout parameters as hexagons, select, phenotype, enter proliferation, and click ok.

Then, right-click and select change color and shape again to set the color code to 255, 204, 204. To visualize the edges of node interactions, click state transition for activation arrows, and inhibition for blunted inhibition arrows. Then, click file and save as, and save the generated signaling network, as jove tumor models.xml.

For steady state analysis of the system equilibrium, open the SQUAD software, click load network, and upload the jove tumor models. xml file. Click, run analysis.

To write a simulation protocol, click advanced, and pertubator, then click edit protocol, to simulate the anit-EGFR resistance. Next, click, pertubation and select the standard initial state equals steady state four. Click add, to add the new constant pulse, then select the state parameter and set the target to FLIP, the time to zero, and the value to 4.

Click, ok, and set the states for c-MET and Gefitinib, in the same way. To set a state value to the active EGFR node, click active node, then, click edit. Set the state to 8 and click ok.

Click ok, to save the protocol. To display the specific curves, under the options panel, select EGF-EGFR, star EGF-EGFR, star Mek, caspases, PI3K, Gegitinib, apoptosis, proliferation, MEk inhibitor and PI3K inhibitor. Then, click initialize and run to start a complete simulation of the systems response.

To simulate a combined PI3K and Mek inhibitor treatment, click edit protocol. Next, using the same node parameter as just demonstrated, click pertubation, followed by add, to add a new constant pulse. Then, select a state parameter, and set the target to PI3K inhibitor, the time to zero and the value to one, and click ok.

Click add, again, to add another new constant pulse and under the state parameter, set the target to Mek inhibitor, set the time to zero and the value to one, and click ok, followed by ok again, to save the protocol. Under the options panel, using the same nodes for the graphical representation as just demonstrated, click initialize, and run to start a complete simulation of the systems response. Then, to finish the simulation, click reset.

This 3D tumor test system, facilitates the determination of the proliferation index. And the quantification of apoptosis using M30 Elisa. In this representative experiment, for example, only EGFR mutated HCC827 tumor cells, incubated under static culture conditions, responded to epidermal growth factor inhibition.

By the Tyrosine-kinase inhibitor, Gefitinib, as evident by the increase in apoptosis observed in the HCC827 tumor model system. Here representative images of static 3D tumor models, with or without, Epithelial-Mesenchymal transition inducer, TGF-beta-1 simulation, are shown. Note the increase in vimentin expression, in the treated tumor cell cultures.

Under dynamic conditions, Gefitinib, exerts a robust effect on, EGFR mutated, HCC827 cells, as evidenced by the decrease of tissue-like structures, in the presence of the Tyrosine-kinase inhibitor, compared to the untreated controlled culture. In this graph, the simulation of the known drive in mutation, EGFR and c-MET co-activation, and increased proliferation rate, and a decreased apoptosis rate in HCC827 cells, over time, are observed, reflecting a Gefitinib induced resistance that correlates with activation of MEK and PI3K. Indeed, modeling of the combined PI3K and MEK inhibitors results in a reversal of the anti-EGFR resistance effect, a reduction in proliferation and the induction of apoptosis.

Following this procedure, other methods like, Western Blot or can be performed to investigate downstream signaling pathways. Moreover, co-culture systems with different cell types may also be used to analyze the cross-talk between various cells in the tumor micro environment. This technique paves the way for researchers in the field of drug development to explore drug efficacy in Silico.

After watching this video, you should have a good understanding of how to predict drug efficacy in stratified patient groups, by simulating signaling changes and analyzing the resulting system responses in different mutation and backgrounds.