Mrs. Alster, Mr. Honrath and Dr. Jiang are appreciated for their outstanding technical assistance. Also appreciated is Dr. Volz and the research group of Dr. Dandri for providing the access to their digital PCR device.

1. Preparing Mix Culture Containing Both Tumor Cells and Non-tumor Cells
<ol>
	<li>Use tumor cells from an established cell line MPNST S4625. Use fibroblasts as the non-tumor cells6,7</li>
	<li>Culture both tumor cells and fibroblasts in standard DMEM medium with 10% fetal bovine serum (BSA), 3 mM glutamine, 0.1 mM 2-mercaptoethanol, 500 U/ml penicillin, 500 g/ml streptomycin and 1 mM sodium pyruvate at 37 &#176;C and 5% CO25-7. At sub-confluence, harvest the cells with 0.05% trypsin and count them using a hemocytometer.</li>
	<li>Mix 400 tumor cells and 1,600 non-tumor cells together in each well of a 96-well culture plate in 0.2 ml medium. Considering faster growth of tumor cells over the treatment period of 5 days, the initial proportion of tumor cells was set at 25%. Set up 4 replicates for each drug concentration. Incubate the cells at 37 &#176;C and 5% CO2 O/N.</li>
</ol>
2. Drug Treatment
<ol>
	<li>Use nilotinib as the test drug. Prepare stock solutions of 0, 2, 4 and 20 mM nilotinib in DMSO. Dilute each of them by 200-fold in 5 ml DMEM medium, giving 2-fold concentrated drug-solutions of 0, 10, 20 and 100 &#181;M6.7.</li>
	<li>Remove 0.1 ml medium from each well and add 0.1 ml of medium containing drug. The final concentrations are 0, 5, 10 and 50 &#181;M. Continue incubating the cells in medium containing drug for 5 days.</li>
	<li>At the end of the treatment, inspect the attached cells using a phase-contrast microscope and take photos. Aspirate the medium away, wash the survived adhesive cells once with 0.2 ml PBS.</li>
</ol>
3. Preparing DNA for Digital PCR
<ol>
	<li>Lyse cells using HotShot8.
	<ol>
		<li>Add 50 &#181;l alkaline lysis solution (25 mM NaOH, 0.2 mM EDTA) to each well. Seal the wells with foil. Incubate the plate at 95 &#176;C in an oven or on a heating plate for 30 min. Check under a microscope to make sure that no more intact cells remained at the surface of the wells.&#160;In case the cells did not lyse completely, increase the heating time or add detergent. Freeze them once at -20 &#176;C also enhances breaking cells.</li>
		<li>Add 50 &#181;l neutralizing solution (40 mM Tris-HC, pH 4.0) to each well. Transfer all lysates into U-form wells of a PCR plate and dry in a PCR cycler at 95 &#176;C for 10 to 30 min. Reconstitute the lysates with 20 &#181;l distilled water.</li>
	</ol>
	</li>
	<li>Desalt using Drop-Dialysis9
	<ol>
		<li>use membrane filters with&#160;mean pore size 0.025 &#181;m.</li>
		<li>Float the filter on distilled water with shiny side up in wells of a 12-well plate. Wet the filter for 5 min. Pay attention to avoid air bubbles between the filter and water.</li>
		<li>Pipet the reconstituted lysates of each sample from step 3.1.4 carefully onto on each of the 4 areas of the filter. Cover the plate and dialyze for 30 to 60 min.</li>
		<li>Suck away the water under the filter without flipping over the filter nor disturbing the drops.</li>
		<li>Transfer the lysate-drops into wells of a new PCR-plate. Dry the DNA-drops by heating at 95 &#176;C for 10 to 30 min in a PCR cycler. Reconstitute the lysates in 10 &#181;l water.</li>
	</ol>
	</li>
</ol>
4. Digital PCR
<ol>
	<li>Thaw frozen components such as DNA, buffer and the assay mix at RT, spin down for 10 sec at maximum force of any available centrifuge (for example, VWR mini centrifuge) and then keep them on ice.</li>
	<li>Prepare master mixture containing all components except DNA (Table 1). Consider dead volume and calculate master mixture approximately 10% more than the required amount. 
	NOTE: Use the primers and probes used in the commercial kit.</li>
	<li>Vortex the master mixture, spin down for 10 sec at maximum force of any available centrifuge (for example, VWR mini centrifuge) and dispense 15 &#181;l to each well of a 96-PCR plate. Add all lysate (which contains DNA) of each sample to the wells containing the master mixture. Transfer 20 &#181;l of each reaction mixture into a well on the sample side of a cartridge at RT.</li>
	<li>Dispense 70 &#181;l of droplet generator oil into each well on the oil side of a cartridge. Close the cartridge with the gasket and place the whole setting into a droplet generator. Start droplet generation.</li>
	<li>Transfer 40 &#181;l droplet-solution into wells of a 96-well PCR plate. Seal the plate with a foil and place the plate into a PCR cycler. Set the lid-temperature at 105 &#176;C and ramp rate at 2 &#176;C/sec. Carry out PCR using the settings&#160;in Table 2. After the PCR, transfer the plate into a droplet reader and start reading. Alternatively, store the plate at 4 &#176;C for up to 24 hr.</li>
</ol>
5. Calculating Proportion of Tumor Cells
<ol>
	<li>Load the digital PCR data and perform automatic analysis.</li>
	<li>Control the results of each assay manually by inspecting the one-dimensional and 2-dimensional distribution of the positive and negative droplets, and sure that they are well separated and the threshold lines were in the right positions. Exclude results not meeting the criteria, for example, no clear separation of positive and negative droplets, from further analysis. Copy the resulting ratio NF1/RPP30 into an excel sheet.</li>
	<li>Calculate the proportion of tumor cells as: 2 x&#160;(1- NF1/RPP30).</li>
</ol>
6. Dose-dependent Change of Proportion of Tumor Cells
<ol>
	<li>For each drug-concentration, calculate the mean and standard deviation from the 4 replicates. Plot the means and the standard deviations against drug-concentrations.</li>
</ol>

<ol><li>Caponigro, G., Sellers, W. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Advances+in+the+preclinical+testing+of+cancer+therapeutic+hypotheses%5BTitle%5D)+AND+%22Nat+Rev+Drug+Discov%22%5BJournal%5D)">Advances in the preclinical testing of cancer therapeutic hypotheses.</a> Nat Rev Drug Discov. 10 (3), 179-187 (2011).</li>
<li>McMillin, D. W., Negri, J. M., Mitsiades, C. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+role+of+tumour-stromal+interactions+in+modifying+drug+response%3A+challenges+and+opportunities%5BTitle%5D)+AND+%22Nat+Rev+Drug+Discov%22%5BJournal%5D)">The role of tumour-stromal interactions in modifying drug response: challenges and opportunities.</a> Nat Rev Drug Discov. 12 (3), 217-228 (2013).</li>
<li>Wilding, J. L., Bodmer, W. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cancer+cell+lines+for+drug+discovery+and+development%5BTitle%5D)+AND+%22Cancer+Res%22%5BJournal%5D)">Cancer cell lines for drug discovery and development.</a> Cancer Res. 74 (9), 2377-2384 (2014).</li>
<li>Edwards, A. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Preclinical+target+validation+using+patient-derived+cells%5BTitle%5D)+AND+%22Nat+Rev+Drug+Discov%22%5BJournal%5D)">Preclinical target validation using patient-derived cells.</a> Nat Rev Drug Discov. 14 (3), 149-150 (2015).</li>
<li>Frahm, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Genetic+and+phenotypic+characterization+of+tumor+cells+derived+from+malignant+peripheral+nerve+sheath+tumors+of+neurofibromatosis+type+1+patients%5BTitle%5D)+AND+%22Neurobiol+Dis%22%5BJournal%5D)">Genetic and phenotypic characterization of tumor cells derived from malignant peripheral nerve sheath tumors of neurofibromatosis type 1 patients.</a> Neurobiol Dis. 16 (1), 85-91 (2004).</li>
<li>Jiang, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Efficacy+and+selectivity+of+nilotinib+on+NF1-associated+tumors+in+vitro%5BTitle%5D)+AND+%22J+Neurooncol%22%5BJournal%5D)">Efficacy and selectivity of nilotinib on NF1-associated tumors in vitro.</a> J Neurooncol. 116 (2), 231-236 (2014).</li>
<li>Jiang, W., Mautner, V. F., Friedrich, R. E., Kluwe, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Preclinical+assessment+of+the+anticancer+drug+response+of+plexiform+neurofibroma+tissue+using+primary+cultures%5BTitle%5D)+AND+%22J+Clin+Neurol%22%5BJournal%5D)">Preclinical assessment of the anticancer drug response of plexiform neurofibroma tissue using primary cultures.</a> J Clin Neurol. 11 (2), 172-177 (2015).</li>
<li>Montero-Pau, J., Gomez, A., Munoz, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Application+of+an+inexpensive+and+high-throughput+genomic+DNA+extraction+method+for+the+molecular+ecology+of+zooplanktonic+diapausing+eggs%5BTitle%5D)+AND+%22Limnology+and+Oceanography-Methods%22%5BJournal%5D)">Application of an inexpensive and high-throughput genomic DNA extraction method for the molecular ecology of zooplanktonic diapausing eggs.</a> Limnology and Oceanography-Methods. 6, 218-222 (2008).</li>
<li>Saraswat, M., Grand, R. S., Patrick, W. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Desalting+DNA+by+drop+dialysis+increases+library+size+upon+transformation%5BTitle%5D)+AND+%22Biosci+Biotechnol+Biochem%22%5BJournal%5D)">Desalting DNA by drop dialysis increases library size upon transformation.</a> Biosci Biotechnol Biochem. 77 (2), 402-404 (2013).</li>
<li>Iglesias-Bartolome, R., Martin, D., Gutkind, J. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Exploiting+the+head+and+neck+cancer+oncogenome%3A+widespread+PI3K-mTOR+pathway+alterations+and+novel+molecular+targets%5BTitle%5D)+AND+%22Cancer+Discov%22%5BJournal%5D)">Exploiting the head and neck cancer oncogenome: widespread PI3K-mTOR pathway alterations and novel molecular targets.</a> Cancer Discov. 3, 722-725 (2013).</li>
</ol>

The procedure is patent-protected (10 2013 221 452, Germany)

The key step in this genetic- and cell-based tool for assessing drug specificity in vitro is the determination of proportion of tumor cells using one or more tumor-specific genetic features. In contrast to conventional phenotypic features7, genetic features are specific, stable and easy to quantify. For example, a tumor-specific mutation or copy number variation is present exclusively in the tumor cells but not in non-tumor cells. Such a genetic stigma can be reliably and precisely quantified, for example, using digital PCR as demonstrated in this study.
For digital PCR, high purity of DNA is not essential. However, desalting and removing inhibitory molecules are necessary which can be achieved by a drop-dialysis using a suitable filter. The combined lysis and drop-dialysis is simple and quick, requires no special instruments and therefore can be carried out in any standard laboratory.&#160;If the cells do not break well, additional treatment can be considered such as freezing the cells at -20 &#176;C, adding detergent or/and using protease.&#160;
In the present study, the heterozygous deletion of the NF1 gene was used as the tumor-specific feature for the quantification. Similarly, deletions of other genes or loci can also be used. Furthermore, mutations can&#160;also be used for quantifying tumor cells. In such case, digital PCR assays for the mutant and the normal alleles need to be extensively validated to ensure their specificity for each of them.
Dose-dependent proportion of tumor cells provides an indication for drug-specificity or selectivity. Furthermore, it also enables extracting response of tumor cells from the response of total cells of a mixed culture to a drug (Figure 4). This extraction strategy provides a solution for the technical problem in using primary cultures (which contains both tumor and non-tumor cells) for drug-testing.
The genetic- and cell-based in vitro tool demonstrated in the present study may therefore have application potential (1) in drug-discovery where it provides a platform for assessing drug specificity or selectivity in vitro and (2) in personalized cancer care where it enables testing drugs in primary cultures and consequently may contribute to drug-selection in adjuvant chemotherapy4. Furthermore, pharmacological mechanism of a drug can be studied using this tool, since certain genetic alteration or multiple genetic alterations can be followed during the drug-treatment.
A difficulty in using individualized primary cultures is the lack a universal genetic feature for the quantification of the tumor cells. However, with today&#39;s advance in whole-genome sequencing, the most frequently altered genes and regions are known for common tumors. For example, among head and neck tumors, 71%, 23% and 22% have mutations in the TP53, FAT1 and CDKN2A genes, and 60%, 34% and 26% have deletions in the gene regions of CDKN2A, STK11 and PTEN, respectively10. Screening 5 to 10 such genes or/and regions by means of targeted-sequencing or/and digital PCR will therefore likely identify one or more genetic alterations which can be used for the quantification of tumor cells in the derived primary culture. Another limitation is the often insufficient amount of resected tumors for setting up primary cultures.
Despite the advantageous features and promising application potential of the genetic- and cell-based in vitro tool, its limitations should be kept in mind and its in vitro nature should be emphasized. For example, the differential effects or cytotoxicity of a drug on tumor cells may rather due to difference in cell types (fibroblasts vs. Schwann cells) or their different origins (from different individuals). The stronger effect of a drug on tumor cells than on non-tumor cells may also be explained by the fact that the former grow faster than the latter. Most importantly, cells in vitro behavior differs than cells in the human body and consequently, results of in vitro assessment often do not or only partially reflect the real clinical situation. Therefore, efficacy, cytotoxicity and specificity of drugs obtained for cultured cells are rather of biomarker nature but do not have the reliability of preclinically measured efficacy of antibiotics. Nevertheless, moving from cell lines to mixed cultures and/or primary cultures is a step forward in assessing drug-effect and specificity/selectivity in vitro. With extensive efforts, in vitro conditions may be found which enables reasonable correlation of in vitro data with real responses of patients, at least for some drugs.

Therapeutic effects of anticancer drugs on tumor cells are associated with various unwished toxic effects on non-tumor cells. Toxic effects are difficult to assess in the laboratory, largely due to the lack of suitable tools1. Cell cultures containing both tumor and non-tumor cells would provide a potential in vitro resource for assessing specificity or selectivity of a drug which may shed light on clinical toxicity. However, conventional in vitro assays such as proliferation or viability assays measure effect of a drug on total cells, but cannot divide the effect into the part on tumor cells and the part on non-tumor cells. For example, a 50% reduction of total cell number in a culture composed of 50% tumor cells and 50% non-tumor cells can imply death of all tumor cells, death of all non-tumor cells or, more likely, death of parts of both at various ratios. 
Also because of this technical problem, primary cultures which contain tumor and non-tumor stromal cells are frequently unsuitable for testing drugs. On the other side, because of this cellular heterogeneity, primary cultures resemble their original tumors far better than clonal cells in established cell lines2. More importantly, primary cultures are patient-specific and may enable individualized drug testing2-4.
An essential issue is therefore the development of a method for determination of the proportion of tumor cells in mixed cultures including primary cultures. Toward this challenge, a genetic solution was conceived which is based on determination of the quantitative ratio of a tumor-specific genetic feature to a universal reference sequence. From this ratio, the proportion of tumor cells in a mixed culture can be calculated. 
This study illustrates the procedure of the genetic and cell-based tool for assessing specificity of anticancer drugs including (1) preparing a mixed culture from tumor cells of an established line and non-tumor fibroblasts, (2) treating the mixed culture with a test drug, (3) preparing DNA for digital PCR, (4) determining the ratio of an tumor-specific heterozygous deletion to a reference sequence using a dual-probe droplet digital PCR assay, (5) calculating proportions of tumor cells at each drug concentration and (6) drawing up dose-dependent change of proportion of tumor cells.

<table><tbody><tr><td>Membrane filter of 25 mm diameter</td><td>Merk-Millipore</td><td>VSWP 02500</td><td>For drop-dialysis</td></tr><tr><td>ddPCR assay for NF1</td><td>BioRad</td><td>dHsaCP1000177</td><td>FAM-labelled</td></tr><tr><td>ddPCR assay for RPP30</td><td>BioRad</td><td>dHsaCP2500350</td><td>HEX-labelled</td></tr><tr><td>QX200</td><td>BioRad</td><td>186-4001</td><td>The previous version QX was used in the study, which is not available any more. The replacement is QX200</td></tr><tr><td>Droplet oil</td><td>BioRad</td><td>186-3005</td><td></td></tr><tr><td>Cartriges&amp;nbsp;</td><td>BioRad</td><td>186-3004</td><td></td></tr><tr><td>Gaskets</td><td>BioRad</td><td>186-3009</td><td></td></tr><tr><td>Cartrige holder</td><td>BioRad</td><td>186-3051</td><td></td></tr><tr><td>Pierceble foil</td><td>BioRad</td><td>181-4040</td><td></td></tr></tbody></table>

assessing specificity of anticancer drugs in vitro

A procedure for assessing specificity of anticancer drugs in vitro using cultures containing both tumor and non-tumor cells is demonstrated. The key element is the quantitative determination of a tumor-specific genetic alteration in relation to a universal sequence using a dual-probe digital PCR assay and the subsequent calculation of the proportion of tumor cells. The assay is carried out on a culture containing tumor cells of an established line and spiked-in non-tumor cells. The mixed culture is treated with a test drug at various concentrations. After the treatment, DNA is prepared directly from the survived adhesive cells in wells of 96-well plates using a simple and inexpensive method, and subjected to a dual-probe digital PCR assay for measuring a tumor-specific genetic alteration and a reference universal sequence. In the present demonstration, a heterozygous deletion of the NF1 gene is used as the tumor-specific genetic alteration and a RPP30 gene as the reference gene. Using the ratio NF1/RPP30, the proportion of tumor cells was calculated. Since the dose-dependent change of the proportion of tumor cells provides an in vitro indication for specificity of the drug, this genetic and cell-based in vitro assay will likely have application potential in drug discovery. Furthermore, for personalized cancer-care, this genetic- and cell-based tool may contribute to optimizing adjuvant chemotherapy by means of testing efficacy and specificity of candidate drugs using primary cultures of individual tumors.

Quality of DNA prepared using the combined HotShot/Drop-Dialysis is sufficient for satisfactory digital PCR (Figure 1B). For 2,000 fibroblasts, approximately 3,000 &#177; 500 positive droplets were obtained, corresponding to 75% of the expected 4,000 copies (two copies in one cell). In this study, the tumor cells were from an established line MPNST S462 which are known to have a heterozygotic deletion of the NF1 gene5. This 50% gene dosage reduction was clearly detected by the dual digital PCR assay (Figure&#160;1B). By contrast, two copies of this gene were measured in fibroblasts. This genetic difference between tumor and non-tumor cells enables calculation of the proportion of tumor cells in mixed cultures (Figure&#160;2). By measuring the relative gene dosage of NF1 to RPP30, proportion of tumor cells in a mixed culture can be determined at each drug-concentration (Figure&#160;3).
<img alt="Figure 1" src="/files/ftp_upload/53752/53752fig1.jpg" /> 
Figure 1. Digital PCR.&#160;(A) illustration of the principle. (B) a dual-probe assay reveals reduced amount of a target (NF1) gene in the tumor cells MPNST S462 in relation to a reference (RPP30) gene. <a href="https://www.jove.com/files/ftp_upload/53752/53752fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/53752/53752fig2.jpg" /> 
Figure 2. Calculated Proportion of Tumor Cells. Tumor cells and non-tumor cells were mixed at various ratios and seeded into each well of a 96-plate. After attachment O/N, the cells were lysed with HotShot/Drop-dialysis and the desalted lysates were subjected to digital PCR which gave the ratio of NF1/RPP30. Base on this ratio, proportion of tumor cells in each well was calculated. Mean and standard deviation of proportion of tumor cells were calculated from 4 replicates at each set-up proportion. The calculated proportions of tumor cells (Y-axis) were plotted against the set-up proportions (X-axis). <a href="https://www.jove.com/files/ftp_upload/53752/53752fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/53752/53752fig3.jpg" /> 
Figure 3. Dose-dependent Change of Proportion of Tumor Cells.&#160;(A) dose-dependent reduction of visible vital cells. (B) proportion of tumor cells (mean and standard deviation of 4 replicates) calculated from the ratio of NF1: RPP30, decreased in the range of 0 - 10 &#181;M. At the highest dose 50 &#181;M, few vital cells were left, likely due to toxic effect to all cells. <a href="https://www.jove.com/files/ftp_upload/53752/53752fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/53752/53752fig4.jpg" /> 
Figure 4. Separating Response of Tumor Cells from That of Non-tumor Cells.&#160;(A) viability or/and proliferation of total cells can be measured using conventional assays; Proportion of tumor cells can be determined using the procedure demonstrated in the present study. (B) using the two parameters, response of total cells in a mixed culture to a drug can be divided into response of the tumor cells and response of the non-tumor cells. <a href="https://www.jove.com/files/ftp_upload/53752/53752fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table fo:keep-together.within-page="1">
	<colgroup>
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:147px;"></td>
			<td style="width:171px;">1 Reaction</td>
			<td style="width:99px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:147px;">2 x ddPCR supermix</td>
			<td style="width:171px;">12</td>
			<td style="width:99px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:147px;">Assay mix for NF1</td>
			<td style="width:171px;">1.2</td>
			<td style="width:99px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:147px;">Assay mix for RPP30</td>
			<td style="width:171px;">1.2</td>
			<td style="width:99px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:147px;">HaeIII</td>
			<td style="width:171px;">0.6</td>
			<td style="width:99px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:147px;">Subtotal</td>
			<td style="width:171px;">15</td>
			<td style="width:99px;">Master mixture</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:147px;">DNA</td>
			<td style="width:171px;">9</td>
			<td style="width:99px;"></td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:147px;">Total</td>
			<td style="width:171px;">24</td>
			<td style="width:99px;">Final PCR Mixture</td>
		</tr>
	</tbody>
</table>
Table 1 Setting Up Dual ddPCR Reaction
<table fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Step</td>
			<td>Action</td>
			<td>Temperature</td>
			<td>Time</td>
			<td>Cycles</td>
		</tr>
		<tr>
			<td>1</td>
			<td>Polymerase activation</td>
			<td>95 &#176;C</td>
			<td>10 min</td>
			<td>1</td>
		</tr>
		<tr>
			<td>2</td>
			<td>DNA denaturation</td>
			<td>94 &#176;C</td>
			<td>30 sec</td>
			<td rowspan="2">40</td>
		</tr>
		<tr>
			<td>3</td>
			<td>Annealing/exetension</td>
			<td>60 &#176;C</td>
			<td>1 min</td>
		</tr>
		<tr>
			<td>4</td>
			<td>Polymerase deactivation</td>
			<td>98 &#176;C</td>
			<td>10 min</td>
			<td>1</td>
		</tr>
		<tr>
			<td>5</td>
			<td>Hold (optional)</td>
			<td>4 &#176;C</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 2 PCR Cycler Settings

Watch this Scientific Journal Video about Assessing Specificity of Anticancer Drugs In Vitro at JoVE.com

Assessing Specificity of Anticancer Drugs In Vitro

The goal of this protocol is to assess specificity of anticancer drugs in vitro using mixed cultures containing both tumor and non-tumor cells.

Assessing Specificity of Anticancer Drugs In Vitro

A procedure for assessing specificity of anticancer drugs in vitro using cultures containing both tumor and non-tumor cells is demonstrated. The key ...

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Medicine

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Video: Assessing Specificity of Anticancer Drugs In Vitro

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Cancer Research

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Potentiation of hostile monoclonal antibodies (mAb) by chemotherapeutic agents constitutes a valuable strategy for designing effective and safer therapy against cancer. Here we provide a protocol to identify a rational combination at the preclinical step. First, we describe a cell-based assay to assess the synergism between anticancer mAb and cytotoxic drugs, that uses the combination index equation of Chou and Talalay1. This includes the measurement of tumor cell drug- and antibody-sensitivity using an MTT assay, followed by an automated computer analysis to calculate the combination index (CI) values. CI values of &lt;1 indicate synergism between tested mAbs and cytotoxic agents1. To corroborate the in vitro findings in vivo, we further describe a method to assess the combination regimen efficacy in a xenograft tumor model. In this model, the combined regimen significantly delays tumor growth, which results in a significant extended survival in comparison to single-agent controls. Importantly, the in vivo experimentation reveals that the combination regimen is well tolerated. This protocol allows the effective evaluation of anticancer drug combinations in preclinical models and the identification of rational combination to evaluate in clinical trials.

This protocol describes how to assess synergism between an anticancer antibody and antineoplastic drugs in preclinical models by using the combination index equation of Chou and Talalay.

Potentiation of hostile monoclonal antibodies (mAb) by chemotherapeutic agents constitutes a valuable strategy for designing effective and safer therapy ...

High-Throughput In Vitro Assay using Patient-Derived Tumor Organoids

Patient-derived tumor organoids (PDOs) are expected to be a preclinical cancer model with better reproducibility of disease than traditional cell culture models. PDOs have been successfully generated from a variety of human tumors to recapitulate the architecture and function of tumor tissue accurately and efficiently. However, PDOs are unsuitable for an in vitro high-throughput assay system (HTS) or cell analysis using 96-well or 384-well plates when evaluating anticancer drugs because they are heterogeneous in size and form large clusters in culture. These cultures and assays use extracellular matrices, such as Matrigel, to create tumor tissue scaffolds. Therefore, PDOs have a low throughput and high cost, and it has been difficult to develop a suitable assay system. To address this issue, a simpler and more accurate HTS was established using PDOs to evaluate the potency of anticancer drugs and immunotherapy. An in vitro HTS was created that uses PDOs established from solid tumors cultured in 384-well plates. An HTS was also developed for assessment of antibody-dependent cellular cytotoxicity activity to represent the immune response using PDOs cultured in 96-well plates.

High-Throughput In Vitro Assay using Patient-Derived Tumor Organoids

A highly accurate in vitro high-throughput assay system was developed to evaluate anticancer drugs using patient-derived tumor organoids (PDOs), similar to cancer tissues but are unsuitable for in vitro high-throughput assay systems with 96-well and 384-well plates.

Patient-derived tumor organoids (PDOs) are expected to be a preclinical cancer model with better reproducibility of disease than traditional cell culture ...

A Flow Cytometry-Based Cell Surface Protein Binding Assay for Assessing Selectivity and Specificity of an Anticancer Aptamer

A key challenge in developing an anticancer aptamer is to efficiently determine the selectivity and specificity of the developed aptamer to the target protein. Due to its several advantages over monoclonal antibodies, aptamer development has gained enormous popularity among cancer researchers. Systematic evolution of ligands by exponential enrichment (SELEX) is the most common method of developing aptamers specific for proteins of interest. Following SELEX, a quick and efficient binding assay accelerates the process of identification, confirming the selectivity and specificity of the aptamer. 
This paper explains a step-by-step flow cytometric-based binding assay of an aptamer specific for epithelial cellular adhesion molecule (EpCAM). The transmembrane glycoprotein EpCAM is overexpressed in most carcinomas and plays roles in cancer initiation, progression, and metastasis. Therefore, it is a valuable candidate for targeted drug delivery to tumors. To evaluate the selectivity and specificity of the aptamer to the membrane-bound EpCAM, EpCAM-positive and -negative cells are required. Additionally, a non-binding EpCAM aptamer with a similar length and 2-dimensional (2D) structure to the EpCAM-binding aptamer is required. The binding assay includes different buffers (blocking buffer, wash buffer, incubation buffer, and FACS buffer) and incubation steps. 
The aptamer is incubated with the cell lines. Following the incubation and washing steps, the cells will be evaluated using a sensitive flow cytometry assay. Analysis of the results shows the binding of the EpCAM-specific aptamer to EpCAM-positive cells and not the EpCAM-negative cells. In EpCAM-positive cells, this is depicted as a band shift in the binding of the EpCAM aptamer to the right compared to the non-binding aptamer control. In EpCAM-negative cells, the corresponding bands of EpCAM-binding and -non-binding aptamers overlap. This demonstrates the selectivity and specificity of the EpCAM aptamer. While this protocol is focused on the EpCAM aptamer, the protocol is applicable to other published aptamers.

A necessary step in anticancer aptamer development is to test its binding to the target. We demonstrate a flow cytometric-based assay to study this binding, emphasizing the importance of including a negative control aptamer and cancer cells that are positive or negative for that particular protein.&#160;

A key challenge in developing an anticancer aptamer is to efficiently determine the selectivity and specificity of the developed aptamer to the target ...

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

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&#39;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.

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

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.

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 ...

Bioengineering

Quantifying Antibody-Dependent Cellular Cytotoxicity in a Tumor Spheroid Model: Application for Drug Discovery

Monoclonal antibody-based immunotherapy targeting tumor antigens is now a mainstay of cancer treatment. One of the clinically relevant mechanisms of action of the antibodies is antibody-dependent cellular cytotoxicity (ADCC), where the antibody binds to the cancer cells and engages the cellular component of the immune system, e.g., natural killer (NK) cells, to kill the tumor cells. The effectiveness of these therapies could be improved by identifying adjuvant compounds that increase the sensitivity of the cancer cells or the potency of the immune cells. In addition, undiscovered drug interactions in cancer patients co-medicated for previous conditions or cancer-associated symptoms may determine the success of the antibody therapy; therefore, such unwanted drug interactions need to be eliminated. With these goals in mind, we created a cancer ADCC model and describe here a simple protocol to find ADCC-modulating drugs. Since 3D models such as cancer cell spheroids are superior to 2D cultures in predicting in vivo responses of tumors to anticancer therapies, spheroid co-cultures of EGFP-expressing HER2+ JIMT-1 breast cancer cells and the NK92.CD16 cell lines were set up and induced with Trastuzumab, a monoclonal antibody clinically approved against HER2-positive breast cancer. JIMT-1 spheroids were allowed to form in cell-repellent U-bottom 96-well plates. On day 3, NK cells and Trastuzumab were added. The spheroids were then stained with Annexin V-Alexa 647 to measure apoptotic cell death, which was quantitated in the peripheral zone of the spheroids with an automated microscope. The applicability of our assay to identify ADCC-modulating molecules is demonstrated by showing that Sunitinib, a receptor tyrosine kinase inhibitor approved by the FDA against metastatic cancer, almost completely abolishes ADCC. The generation of the spheroids and image acquisition and analysis pipelines are compatible with high-throughput screening for ADCC-modulating compounds in cancer cell spheroids.

Here, we present a method to identify compounds that modulate the ADCC mechanism, an important cancer cell-killing mechanism of antitumor antibodies. The cytotoxic effect of NK cells is measured in breast cancer cell spheroids in the presence of Trastuzumab. Image analysis identifies live and dead killer and target cells in spheroids.

Monoclonal antibody-based immunotherapy targeting tumor antigens is now a mainstay of cancer treatment. One of the clinically relevant mechanisms of ...

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Due to the high level of heterogeneity and mutations inherent in human cancers, single agent therapies, or combination regimens which target the same pathway, are likely to fail. Emphasis must be placed upon the inhibition of pathways that are responsible for intrinsic and/or adaptive resistance to therapy. An active field of investigation is the development and testing of DNA repair inhibitors that promote the action of, and prevent resistance to, commonly used chemotherapy and radiotherapy. We used a novel protocol to evaluate the effectiveness of BRCA2 inhibition as a means to sensitize tumor cells to the DNA damaging drug cisplatin. Tumor cell metabolism (acidification and respiration) was monitored in real-time for a period of 72 hr to delineate treatment effectiveness on a minute by minute basis. In combination, we performed an assessment of metastatic frequency using a chicken embryo chorioallantoic membrane (CAM) model of extravasation and invasion. This protocol addresses some of the weaknesses of commonly used in vitro and in vivo methods to evaluate novel cancer therapy regimens. It can be used in addition to common methods such as cell proliferation assays, cell death assays, and in vivo murine xenograft studies, to more closely discriminate amongst candidate targets and agents, and select only the most promising candidates for further development.

Evaluating the Effectiveness of Cancer Drug Sensitization In Vitro and In Vivo

Here, real-time monitoring of tumor cell metabolism, combined with an in vivo chicken embryo chorio-allantoic membrane (CAM) model of metastasis, are used to evaluate novel anti-cancer targets/agents for their ability to sensitize tumor cells to DNA damaging chemotherapeutics.

Due to the high level of heterogeneity and mutations inherent in human cancers, single agent therapies, or combination regimens which target the same ...

Utilization of the Soft Agar Colony Formation Assay to Identify Inhibitors of Tumorigenicity in Breast Cancer Cells

Given the inherent difficulties in investigating the mechanisms of tumor progression in vivo, cell-based assays such as the soft agar colony formation assay (hereafter called soft agar assay), which measures the ability of cells to proliferate in semi-solid matrices, remain a hallmark of cancer research. A key advantage of this technique over conventional 2D monolayer or 3D spheroid cell culture assays is the close mimicry of the 3D cellular environment to that seen in vivo. Importantly, the soft agar assay also provides an ideal tool to rigorously test the effects of novel compounds or treatment conditions on cell proliferation and migration. Additionally, this assay enables the quantitative assessment of cell transformation potential within the context of genetic perturbations. We recently identified peptidylarginine deiminase 2 (PADI2) as a potential breast cancer biomarker and therapeutic target. Here we highlight the utility of the soft agar assay for preclinical anti-cancer studies by testing the effects of the PADI inhibitor, BB-Cl-amidine (BB-CLA), on the tumorigenicity of human ductal carcinoma in situ (MCF10DCIS) cells.

Here, we document the use of the soft agar colony formation assay to test the effects of a peptidylarginine deiminase (PADI) enzyme inhibitor, BB-Cl-amidine, on breast cancer tumorigenicity in vitro.

Given the inherent difficulties in investigating the mechanisms of tumor progression in vivo, cell-based assays such as the soft agar colony formation ...

An Organotypic High Throughput System for Characterization of Drug Sensitivity of Primary Multiple Myeloma Cells

In this work we describe a novel approach that combines ex vivo drug sensitivity assays and digital image analysis to estimate chemosensitivity and heterogeneity of patient-derived multiple myeloma (MM) cells. This approach consists in seeding primary MM cells freshly extracted from bone marrow aspirates into microfluidic chambers implemented in multi-well plates, each consisting of a reconstruction of the bone marrow microenvironment, including extracellular matrix (collagen or basement membrane matrix) and stroma (patient-derived mesenchymal stem cells) or human-derived endothelial cells (HUVECs). The chambers are drugged with different agents and concentrations, and are imaged sequentially for 96 hr through bright field microscopy, in a motorized microscope equipped with a digital camera. Digital image analysis software detects live and dead cells from presence or absence of membrane motion, and generates curves of change in viability as a function of drug concentration and exposure time. We use a computational model to determine the parameters of chemosensitivity of the tumor population to each drug, as well as the number of sub-populations present as a measure of tumor heterogeneity. These patient-tailored models can then be used to simulate therapeutic regimens and estimate clinical response.

We describe a high-throughput drug sensitivity assay for primary multiple myeloma cells. It consists of a reconstruction of the bone marrow microenvironment (including extracellular matrix and stroma) in multi-well plates, and a non-invasive method for longitudinal quantification of cell viability.

In this work we describe a novel approach that combines ex vivo drug sensitivity assays and digital image analysis to estimate chemosensitivity and ...

Assessing Specificity of Anticancer Drugs In Vitro

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