We thank The National Natural Science Foundation of China (81672962), the Jiangsu Provincial Innovation Team Program Foundation, and the Joint Key Project Foundation of Southeast University and Nanjing Medical University for their support.

GBM specimen was acquired from a patient during a routine operation after obtaining fully informed consent by human research ethics committee of The First Affiliated Hospital of Nanjing Medical University.
1. Isolation and culture of patient-derived GSCs
<ol>
	<li>Place fresh surgically resected glioblastoma tissue in a 15 mL centrifuge tube filled with sterile PBS and store the tissue on ice until further operation.</li>
	<li>Mince the GBM tissue into approximately 0.5 to 1 mm diameter pieces using dissection scissors and wash the tissue specimens with neuronal basal medium to remove cellular debris in a biosafety cabinet.</li>
	<li>Digest the tissue fragments with 1 mg/mL collagenase A at 37 &#176;C for 30 min and centrifuge at 400 x g for 5 min at 4 &#176;C.</li>
	<li>Remove the supernatant and suspend the pellet with blank neuronal basal medium and dissociate the pellet mechanically by repetitive pipetting on ice.</li>
	<li>Culture the mixture in ultra-low attachment 6-well culture plates filled with GSC culture medium (see Table 1 for the recipe) in a sterile cell incubator with 5% CO2 and 90% humidity at 37 &#176;C until neurosphere formation.</li>
	<li>On sufficient neurosphere formation, collect them using a pipette in a 1.5 mL microtube and centrifuge at 800 x g for 5 min at room temperature.</li>
	<li>Resuspend the pellet and split it into several flasks filled with the above culture medium for maintaining the primary GSCs. 
	NOTE: Patient-derived GSCs used in the example were derived from surgical biopsies of a 34-year-old male patient with WHO grade IV recurrent GBM. The GSCs were named as XG387 for the future experiments. PCR-based mycoplasma tests were performed for the above GSCs to confirm no mycoplasma contamination is present. All the experiments involving GSCs used in this protocol were carried out &#60;15 passages.</li>
</ol>
2. Preparing luciferase-tagged GSCs
<ol>
	<li>Collect the GSCs from the medium culture and centrifuge them at 70 x g for 3 min at room temperature.</li>
	<li>Remove the supernatant, digest the cells with accutase for 4 min at 37 &#176;C. Use a 200 &#181;L tip and pipette repeatedly to dissociate and resuspend the cell pellet.</li>
	<li>Dilute the cells to 2 x 105 cells per well in a 12-well culture plate and culture the cells overnight.</li>
	<li>Add 30 &#181;L luciferase-EGFP virus supernatant (titer &#62;108 TU /mL) into each well in the plate and then centrifuge the cells at 1,000 x g for 2 h at 25 &#176;C. Culture the cells overnight.</li>
	<li>Refresh the medium the next day and culture the cells for another 48 h.</li>
	<li>Observe the cells under a florescent microscope to confirm the appearance of the GFP positive cells.</li>
	<li>Use a flow cell sorter to sort and select the GSCs with high GFP fluorescence to culture the cells further.</li>
</ol>
3. Bio-luminescence based measurement of cell viability
<ol>
	<li>Coating plates with the extracellular matrix (ECM) mixture (e.g., Matrigel): Add 40 &#181;L of 0.15 mg/mL ECM mixture to each well and incubate the plate for 1 h at 37 &#176;C. Remove the excess ECM mixture and gently rinse once with PBS.</li>
	<li>Add 100 &#181;L culture medium containing 15,000, 10,000, 8,000, 6,000, 4,000, 2,000, 1,000, and 500 XG387-Luc cells together with 100 &#181;L blank medium as control into each well for 6 replicates in a 96-well optical bottom plate and culture the cells overnight at 37 &#176;C.</li>
	<li>Remove the supernatant, add 50 &#181;L culture medium containing 150 ng/&#181;L D-luciferin into each well and incubate the cells for 5 min at 37 &#176;C.</li>
	<li>Take images of the cellular bio-luminescence in the plate using the IVIS spectrum imaging system. Use the built-in software to create multiple circular areas of the region of interest (ROI) and quantify the cellular bio-luminescence.</li>
</ol>
4. Temozolomide treatment and combination screening
<ol>
	<li>Precoat four 96-well plates as described above, prior to the treatment.</li>
	<li>Seed XG387-Luc cells at a density of 1,000 cells in 100 &#181;L culture medium into each well of a 96-well optical bottom plate and culture the cells overnight.</li>
	<li>Prepare temozolomide and the targeted agents from the stock solution in advance. Prepare a concentration series composed of 800 &#181;M, 600 &#181;M, 400 &#181;M, 300 &#181;M, 200 &#181;M, 100 &#181;M, and 50 &#181;M temozolomide in culture medium for the single-agent treatment. Dilute temozolomide and the targeted agents in stock solution in the culture medium, respectively, to obtain final concentrations of 200 &#181;M and 2 &#181;M for combination drug screening (Table 2).</li>
	<li>Remove the culture medium when most of the GSCs adhere to the bottom of the plates; add the above-prepared medium containing temozolomide into each well for three technical replicates per treatment.</li>
	<li>To treat Temozolomide and to screen the drug combinations remove the blank medium and add the above-prepared medium containing either 200 &#181;M temozolomide, or 2 &#181;M targeted agent, or a combination of both into each well for three technical replicates per treatment.</li>
	<li>Incubate all plates at 37 &#176;C, 5% CO2 for 3 days.</li>
	<li>Remove the drug-containing medium, add 50 &#181;L blank medium containing 150 ng/&#181;L D-luciferin into each well and incubate the cells for 5 min at 37 &#176;C.</li>
	<li>Take images of the cellular bio-luminescence in the plate using the IVIS spectrum imaging system. Use the built-in software to create multiple circular ROIs and quantify the cellular bio-luminescence.</li>
</ol>
5. Combination treatment of temozolomide and UMI-77 in XG387-Luc and XG328-Luc cell lines
<ol>
	<li>Precoat three 96-well plates as described above, prior to the treatment.</li>
	<li>Seed XG387-Luc and XG328-Luc cells at a density of 1,000 cells respectively in 100 &#181;L of culture medium into each well of a 96-well optical bottom plate and culture the cells overnight.</li>
	<li>Prepare a concentration series composed of 600 &#181;M, 400 &#181;M, 300 &#181;M, 200 &#181;M, 100 &#181;M, 50 &#181;M, and 0 &#181;M temozolomide and a concentration series composed of 6 &#181;M, 4 &#181;M, 3 &#181;M, 2 &#181;M, 1 &#181;M, 0.5 &#181;M, and 0 &#181;M UMI-77 in the culture medium for six-by-six dose titration matrix treatments.</li>
	<li>Remove the blank medium when most of the GSCs adhere to the bottom of the plate; add the above-prepared medium into each well for three technical replicates per treatment.</li>
	<li>Incubate these plates for 3 days at 37 &#176;C, 5% CO2.</li>
	<li>Remove the drug-containing medium; add 50 &#181;L of blank medium containing 150 ng/&#181;L D-luciferin into each well and incubate the cells for 5 min at 37 &#176;C for bioluminescence measurement.</li>
</ol>
6. Data analysis
<ol>
	<li>Calculate the sensitivity Index (SI) score of temozolomide and targeted agent according to the formula in Figure 2A. 
	NOTE: The SI score is to quantify the influence of the addition of another drug. It ranged from -1 to +1, with positive values indicating temozolomide synergistic effects.</li>
	<li>Calculate the combination index (CI) values between temozolomide and UMI-77 using CompuSyn software to analyze their combined interactions. CI value &#60;1 indicates synergy; CI value &#62;1 indicates antagonism.</li>
	<li>Calculate the high single agent (HSA) values between temozolomide and UMI-77 using Combenefit software. HSA value indicates the combined inhibitory effect. HSA value &#62;0 indicates synergy and the HSA value &#60;0 indicates antagonism.</li>
</ol>

<ol>
	<li>Lathia, J. D., Mack, S. C., Mulkearns-Hubert, E. E., Valentim, C. L., Rich, J. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+stem+cells+in+glioblastoma.">Cancer stem cells in glioblastoma.</a> Genes &amp; Development. 29 (12), 1203-1217 (2015).</li><li>Binello, E., Germano, I. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeting+glioma+stem+cells:+a+novel+framework+for+brain+tumors.">Targeting glioma stem cells: a novel framework for brain tumors.</a> Cancer Science. 102 (11), 1958-1966 (2011).</li><li>Mathews Griner, L. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+combinatorial+screening+identifies+drugs+that+cooperate+with+ibrutinib+to+kill+activated+B-cell-like+diffuse+large+B-cell+lymphoma+cells.">High-throughput combinatorial screening identifies drugs that cooperate with ibrutinib to kill activated B-cell-like diffuse large B-cell lymphoma cells.</a> Proceedings of the National Academy of Sciences of the United States of America. 111 (6), 2349-2354 (2014).</li><li>Di Veroli, G. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Combenefit:+an+interactive+platform+for+the+analysis+and+visualization+of+drug+combinations.">Combenefit: an interactive platform for the analysis and visualization of drug combinations.</a> Bioinformatics. 32 (18), 2866-2868 (2016).</li><li>Shi, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ibrutinib+inactivates+BMX-STAT3+in+glioma+stem+cells+to+impair+malignant+growth+and+radioresistance.">Ibrutinib inactivates BMX-STAT3 in glioma stem cells to impair malignant growth and radioresistance.</a> Science Translational Medicine. 10 (443), 1-13 (2018).</li><li>Tan, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+identification+of+synergistic+drug+pairs+targeting+HIV.">Systematic identification of synergistic drug pairs targeting HIV.</a> Nature Biotechnology. 30 (11), 1125-1130 (2012).</li><li>Jansen, V. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Kinome-wide+RNA+interference+screen+reveals+a+role+for+PDK1+in+acquired+resistance+to+CDK4/6+inhibition+in+ER-positive+breast+cancer.">Kinome-wide RNA interference screen reveals a role for PDK1 in acquired resistance to CDK4/6 inhibition in ER-positive breast cancer.</a> Cancer Research. 77 (9), 2488-2499 (2017).</li><li>Malyutina, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drug+combination+sensitivity+scoring+facilitates+the+discovery+of+synergistic+and+efficacious+drug+combinations+in+cancer.">Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer.</a> PLoS Computational Biology. 15 (5), 1006752 (2019).</li><li>He, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Methods+for+High-throughput+drug+combination+screening+and+synergy+scoring.">Methods for High-throughput drug combination screening and synergy scoring.</a> Cancer Systems Biology. 1711, 351-398 (2018).</li><li>Chen, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeting+the+synthetic+vulnerability+of+PTEN-deficient+glioblastoma+cells+with+MCL1+inhibitors.">Targeting the synthetic vulnerability of PTEN-deficient glioblastoma cells with MCL1 inhibitors.</a> Molecular Cancer Therapeutics. 19 (10), 2001-2011 (2020).</li></ol>

The authors declare no conflicts to disclose.

In the present study, a protocol that can be applied to identify potential combination therapy for GBM using patient-derived GSCs was described. Unlike the standard synergy/additivity metric model such as Loewe, BLISS, or HSA methods, a simple and quick workflow was used that does not require a drug pair to be combined at multiple concentrations in a full factorial manner as the traditional methods. In this workflow, SI (sensitivity index) which is originated from a study to evaluate the sensitizing effect of siRNAs in c...

Glioblastoma (GBM) is the most common and aggressive type of primary brain tumor. Currently, the overall survival of GBM patients who received maximal treatment (a combination of surgery, chemotherapy, and radiotherapy) is still shorter than 15 months; so novel and effective therapies for GBM are urgently required.
The presence of glioma stem cells (GSCs) in GBM constitutes a considerable challenge for the conventional treatment as these stem-like cells play pivot roles in the maintenance of tumor microenvironment, drug resistance, and tumor recurrence1. Therefore, targeting GSCs could be a promising strategy for GBM treatment2. Nevertheless, a major drawback for the drug efficacy in GBM is its heterogenetic nature, including but not limited to the difference in genetic mutations, mixed subtypes, epigenetic regulation, and tumor microenvironment which makes them very refractory for treatment. After many failed clinical trials, scientists and clinical researchers realized that single-agent targeted therapy is probably incapable of fully controlling the progression of highly heterogeneous cancers such as GBM. Whereas, carefully selected drug combinations have been approved for their effectiveness by synergistically enhancing the effect of each other, thus providing a promising solution for GBM treatment.
Although there are many ways to evaluate the drug-drug interactions of a drug combination, such as the CI (Combination Index), HSA (Highest Single Agent), and Bliss values, etc.3,4, these calculation methods are usually based on multiple concentration combinations. Indeed, these methods can provide affirmative assessment of drug-drug interaction but can be very laborious if they are applied in high-throughput screening. To simplify the process, a screening workflow for rapidly identifying the potential drug combinations that inhibit the growth of GSCs originated from surgical biopsies of patient GBM was developed. A sensitivity Index (SI) that reflects the difference of the expected combined effect and the observed combined effect was introduced into this method to quantify the synergizing effect of each drug, so the potential candidates can be easily identified by the SI ranking. Meanwhile, this protocol demonstrates an example screen to identify the potential candidate(s) that can synergize the anti-glioma effect with temozolomide, the first-line chemotherapy for GBM treatment, among 20 small molecular inhibitors.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>B-27</td>
			<td>Gibco</td>
			<td>17504-044</td>
			<td>50X</td>
		</tr>
		<tr>
			<td>EGF</td>
			<td>Gibco</td>
			<td>PHG0313</td>
			<td>20 ng/ml</td>
		</tr>
		<tr>
			<td>FGF</td>
			<td>Gibco</td>
			<td>PHG0263</td>
			<td>20 ng/ml</td>
		</tr>
		<tr>
			<td>Gluta Max</td>
			<td>Gibco</td>
			<td>35050061</td>
			<td>100X</td>
		</tr>
		<tr>
			<td>Neurobasal</td>
			<td>Gibco</td>
			<td>21103049</td>
			<td>1X</td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin</td>
			<td>HyClone</td>
			<td>SV30010</td>
			<td>P: 10,000 units/ml&#160;&#160;&#160;&#160; S:&#160; 10,000 ug/ml</td>
		</tr>
		<tr>
			<td>Sodium Pyruvate</td>
			<td>Gibco</td>
			<td>2088876</td>
			<td>100 mM</td>
		</tr>
		<tr>
			<td colspan="4">Table 1. The&#160;formulation&#160;of GSC&#160;complete culture medium.&#160;&#160;</td>
		</tr>
		<tr>
			<td>ABT-737</td>
			<td>MCE</td>
			<td></td>
			<td>Selective and BH3 mimetic&#160;Bcl-2,&#160;Bcl-xL&#160;and&#160;Bcl-w&#160;inhibitor</td>
		</tr>
		<tr>
			<td>Adavosertib (MK-1775)</td>
			<td>MCE</td>
			<td></td>
			<td>Wee1&#160;inhibitor</td>
		</tr>
		<tr>
			<td>Axitinib</td>
			<td>MCE</td>
			<td></td>
			<td>Multi-targeted tyrosine kinase inhibitor</td>
		</tr>
		<tr>
			<td>AZD5991</td>
			<td>MCE</td>
			<td></td>
			<td>Mcl-1&#160;inhibitor</td>
		</tr>
		<tr>
			<td>A 83-01</td>
			<td>MCE</td>
			<td></td>
			<td>Potent inhibitor of TGF-&#946; type I receptor&#160;ALK5 kinase</td>
		</tr>
		<tr>
			<td>CGP57380</td>
			<td>Selleck</td>
			<td></td>
			<td>Potent&#160;MNK1&#160;inhibitor</td>
		</tr>
		<tr>
			<td>Dactolisib (BEZ235)</td>
			<td>Selleck</td>
			<td></td>
			<td>Dual ATP-competitive&#160;PI3K&#160;and&#160;mTOR&#160;inhibitor</td>
		</tr>
		<tr>
			<td>Dasatinib</td>
			<td>MCE</td>
			<td></td>
			<td>Dual&#160;Bcr-Abl and Src family tyrosine kinase&#160;inhibitor</td>
		</tr>
		<tr>
			<td>Erlotinib</td>
			<td>MCE</td>
			<td></td>
			<td>EGFR&#160;tyrosine kinase inhibitor</td>
		</tr>
		<tr>
			<td>Gefitinib</td>
			<td>MCE</td>
			<td></td>
			<td>EGFR tyrosine kinase&#160;inhibitor</td>
		</tr>
		<tr>
			<td>Linifanib</td>
			<td>MCE</td>
			<td></td>
			<td>Multi-target inhibitor of&#160;VEGFR&#160;and&#160;PDGFR&#160;family</td>
		</tr>
		<tr>
			<td>Masitinib</td>
			<td>MCE</td>
			<td></td>
			<td>Inhibitor of&#160;c-Kit</td>
		</tr>
		<tr>
			<td>ML141</td>
			<td>Selleck</td>
			<td></td>
			<td>Non-competitive inhibitor of&#160;Cdc42 GTPase&#160;</td>
		</tr>
		<tr>
			<td>OSI-930</td>
			<td>MCE</td>
			<td></td>
			<td>Multi-target inhibitor of Kit, KDR and CSF-1R&#160;</td>
		</tr>
		<tr>
			<td>Palbociclib</td>
			<td>MCE</td>
			<td></td>
			<td>Selective&#160;CDK4&#160;and&#160;CDK6&#160;inhibitor</td>
		</tr>
		<tr>
			<td>SB 202190</td>
			<td>MCE</td>
			<td></td>
			<td>Selective&#160;p38 MAP kinase&#160;inhibitor</td>
		</tr>
		<tr>
			<td>Sepantronium bromide (YM-155)</td>
			<td>MCE</td>
			<td></td>
			<td>Survivin&#160;inhibitor</td>
		</tr>
		<tr>
			<td>TCS 359</td>
			<td>Selleck</td>
			<td></td>
			<td>Potent FLT3 inhibitor</td>
		</tr>
		<tr>
			<td>UMI-77</td>
			<td>MCE</td>
			<td></td>
			<td>Selective&#160;Mcl-1&#160;inhibitor</td>
		</tr>
		<tr>
			<td>4-Hydroxytamoxifen(Afimoxifene)</td>
			<td>Selleck</td>
			<td></td>
			<td>Selective&#160;estrogen receptor (ER)&#160;modulator</td>
		</tr>
		<tr>
			<td colspan="4">Table 2. The information of 20 targeted agents used in the test screen. All of these are target selective small molecular inhibitors. The provider, name, and targets were given in the table.</td>
		</tr>
	</tbody>
</table>

a rapid screening workflow to identify potential combination therapy for gbm using patient-derived glioma stem cells

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in glioblastoma (GBM), the most prevalent and devastating primary brain tumor. The presence of GSCs makes the GBM very refractory to most of individual targeted agents, so high-throughput screening methods are required to identify potential effective combination therapeutics. The protocol describes a simple workflow to enable rapid screening for potential combination therapy with synergistic interaction. The general steps of this workflow consist of establishing luciferase-tagged GSCs, preparing matrigel coated plates, combination drug screening, analyzing, and validating the results.

The XG387 cells formed neurospheres in the culture medium described in the Table 1 in an ultra-low attachment 6-well culture plate or a non-coated plate5 (Figure 1A). First, a test was performed to check whether the bio-luminescence intensity from XG387-Luc cells was proportional to the cell number. As shown in Figure 1B, the bio-luminescence intensity increased proportionally to the cell density and resulted in a linear ...

Watch this Scientific Journal Video about A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells at JoVE.com

A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in ...

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2.7K Views.  Nanjing Medical University. The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in glioblastoma (GBM), the most prevalent and devastating primary brain tumor. The presence of GSCs makes the GBM very refractory to most of individual targeted agents, so high-throughput screening methods are required to identify potential effective combination therapeutics. The protocol describes a simple workflow to enable ...

Video: A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells

A Protocol for Rapid Post-mortem Cell Culture of Diffuse Intrinsic Pontine Glioma (DIPG)

Diffuse Intrinsic Pontine Glioma (DIPG) is a childhood brainstem tumor that carries a universally fatal prognosis. Because surgical resection is not a viable treatment strategy and biopsy is not routinely performed, the availability of patient samples for research is limited. Consequently, efforts to study this disease have been challenged by a paucity of faithful disease models. To address this need, we describe here a protocol for the rapid processing of post-mortem autopsy tissue samples in order to generate durable patient-derived cell culture models that can be used in in vitro assays or in vivo orthotopic xenograft experiments. These models can be used to screen for potential drug targets and to study fundamental pathobiological processes within DIPG. This protocol can further be extended to analyze and isolate tumor and microenvironmental cells using Fluorescence-activated Cell Sorting (FACS), which enables subsequent analysis of gene expression, protein expression, or epigenetic modifications of DNA at the bulk cell or single cell level. Finally, this protocol can also be adapted to generate patient-derived cultures for other central nervous system tumors.

A Protocol for Rapid Post-mortem Cell Culture of Diffuse Intrinsic Pontine Glioma DIPG

This protocol describes a method for the rapid processing of post-mortem diffuse intrinsic pontine glioma samples for the establishment of patient-derived cell culture models or direct characterization of tumor and microenvironmental cells.

Diffuse Intrinsic Pontine Glioma (DIPG) is a childhood brainstem tumor that carries a universally fatal prognosis. Because surgical resection is not a ...

Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy

High-throughput genome-wide RNAi (RNA interference) screening technology has been widely used for discovering host factors that impact&#160;virus replication. Here we present the application of this technology to uncovering host targets that specifically modulate the replication of Maraba virus, an oncolytic rhabdovirus, and vaccinia virus with the goal of enhancing therapy. While the protocol has been tested for use with oncolytic Maraba virus and oncolytic vaccinia virus, this approach is applicable to other oncolytic viruses and can also be utilized for identifying host targets that modulate virus replication in mammalian cells in general. This protocol describes the development and validation of an assay for high-throughput RNAi screening in mammalian cells, the key considerations and preparation steps important for conducting a primary high-throughput RNAi screen, and a step-by-step guide for conducting a primary high-throughput RNAi screen; in addition, it broadly outlines the methods for conducting secondary screen validation and tertiary validation studies. The benefit of high-throughput RNAi screening is that it allows one to catalogue, in an extensive and unbiased fashion, host factors that modulate any aspect of virus replication for which one can develop an in vitro assay such as infectivity, burst size, and cytotoxicity. It has the power to uncover biotherapeutic targets unforeseen based on current knowledge.

Here we describe a protocol for employing high-throughput RNAi screening to uncover host targets that can be manipulated to enhance oncolytic virus therapy, specifically rhabodvirus and vaccinia virus therapy, but it can be readily adapted to other oncolytic virus platforms or for discovering host genes that modulate virus replication generally.

High-throughput genome-wide RNAi (RNA interference) screening technology has been widely used for discovering host factors that impact&#160;virus ...

Modeling Osteosarcoma Using Li-Fraumeni Syndrome Patient-derived Induced Pluripotent Stem Cells

Li-Fraumeni syndrome (LFS) is an autosomal dominant hereditary cancer disorder. Patients with LFS are predisposed to a various type of tumors, including osteosarcoma--one of the most frequent primary non-hematologic malignancies in the childhood and adolescence. Therefore, LFS provides an ideal model to study this malignancy. Taking advantage of iPSC methodologies, LFS-associated osteosarcoma can be successfully modeled by differentiating LFS patient iPSCs to mesenchymal stem cells (MSCs), and then to osteoblasts--the cells of origin for osteosarcomas. These LFS osteoblasts recapitulate oncogenic properties of osteosarcoma, providing an attractive model system for delineating the pathogenesis of osteosarcoma. This manuscript demonstrates a protocol for the generation of iPSCs from LFS patient fibroblasts, differentiation of iPSCs to MSCs, differentiation of MSCs to osteoblasts, and in vivo tumorigenesis using LFS osteoblasts. This iPSC disease model can be extended to identify potential biomarkers or therapeutic targets for LFS-associated osteosarcoma.

Here, we present a protocol for the generation of induced pluripotent Stem Cells (iPSCs) from Li-Fraumeni Syndrome (LFS) patient derived fibroblasts, differentiation of iPSCs via mesenchymal stem cells (MSCs) to osteoblasts, and modeling in vivo tumorigenesis using LFS patient-derived osteoblasts.

Li-Fraumeni syndrome (LFS) is an autosomal dominant hereditary cancer disorder. Patients with LFS are predisposed to a various type of tumors, including ...

Physiologic Patient Derived 3D Spheroids for Anti-neoplastic Drug Screening to Target Cancer Stem Cells

In this protocol, we outline the procedure for generation of tumor spheroids within 384-well hanging droplets to allow for high-throughput screening of anti-cancer therapeutics in a physiologically representative microenvironment. We outline the formation of patient derived cancer stem cell spheroids, as well as, the manipulation of these spheroids for thorough analysis following drug treatment. Specifically, we describe collection of spheroid morphology, proliferation, viability, drug toxicity, cell phenotype and cell localization data. This protocol focuses heavily on analysis techniques that are easily implemented using the 384-well hanging drop platform, making it ideal for high throughput drug screening. While we emphasize the importance of this model in ovarian cancer studies and cancer stem cell research, the 384-well platform is amenable to research of other cancer types and disease models, extending the utility of the platform to many fields. By improving the speed of personalized drug screening and the quality of screening results through easily implemented physiologically representative 3D cultures, this platform is predicted to aid in the development of new therapeutics and patient-specific treatment strategies, and thus have wide-reaching clinical impact.

This protocol describes generation of patient-derived spheroids, and downstream analysis including quantification of proliferation, cytotoxicity testing, flow cytometry, immunofluorescence staining and confocal imaging, in order to assess drug candidates&#8217; potential as anti-neoplastic therapeutics. This protocol supports precision medicine with identification of specific drugs for each patient and stage of disease.

In this protocol, we outline the procedure for generation of tumor spheroids within 384-well hanging droplets to allow for high-throughput screening of ...

Using Human Induced Pluripotent Stem Cells for the Generation of Tumor Antigen-specific T Cells

The generation and expansion of functional T cells in vitro can lead to a broad range of clinical applications. One such use is for the treatment of patients with advanced cancer. Adoptive T cell transfer (ACT) of highly enriched tumor antigen-specific T cells has been shown to cause durable regression of metastatic cancer in some patients. However, during expansion, these cells may become exhausted or senescent, limiting their effector function and persistence in vivo. Induced pluripotent stem cell (iPSC) technology may overcome these obstacles by leading to in vitro generation of large numbers of less differentiated tumor antigen-specific T cells. Human iPSC (hiPSC) have the capacity to differentiate into any type of somatic cell, including lymphocytes, which retain the original T cell receptor (TCR) genomic rearrangement when a T cell is used as a starting cell. Therefore, reprogramming of human tumor antigen-specific T cells to hiPSC followed by redifferentiation to T cell lineage has the potential to produce rejuvenated tumor antigen-specific T cells. Described here is a method for generating tumor antigen-specific CD8&#945;&#946;+ single positive (SP) T cells from hiPSC using OP9/DLL1 co-culture system. This method is a powerful tool for in vitro T cell lineage generation and will facilitate the development of in vitro derived T cells for use in regenerative medicine and cell-based therapies.

This article describes a method to generate functional tumor antigen-specific induced pluripotent stem cell-derived CD8&#945;&#946;+ single positive T cells using OP9/DLL1 co-culture system.

The generation and expansion of functional T cells in vitro can lead to a broad range of clinical applications. One such use is for the treatment of ...

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of both synthetic lethal interactors leads to loss of cell viability or cell death. The best studied synthetic lethal interaction is between BRCA1/2 and PARP1, with PARP1 inhibitors being used in clinical practice to treat patients with BRCA1/2 mutated tumors. Large genetic screens in model organisms but also in haploid human cell lines have led to the identification of numerous additional synthetic lethal interaction pairs, all being potential targets of interest in the development of novel tumor therapies. One approach is to therapeutically target genes with a synthetic lethal interactor that is mutated or significantly downregulated in the tumor of interest. A second approach is to formulate drug combinations addressing synthetic lethal interactions. In this article, we outline a data integration workflow to evaluate and identify drug combinations targeting synthetic lethal interactions. We make use of available datasets on synthetic lethal interaction pairs, homology mapping resources, drug-target links from dedicated databases, as well as information on drugs being investigated in clinical trials in the disease area of interest. We further highlight key findings of two recent studies of our group on drug combination assessment in the context of ovarian and breast cancer.

Large genetic screens in model organisms have led to the identification of negative genetic interactions. Here, we describe a data integration workflow using data from genetic screens in model organisms to delineate drug combinations targeting synthetic lethal interactions in cancer.

A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of ...

Combined Conditional Knockdown and Adapted Sphere Formation Assay to Study a Stemness-Associated Gene of Patient-derived Gastric Cancer Stem Cells

Cancer stem cells (CSCs) are implicated in tumor initiation, development and recurrence after treatment, and have become the center of attention of many studies in the last decades. Therefore, it is important to develop methods to investigate the role of key genes involved in cancer cell stemness. Gastric cancer (GC) is one of the most common and mortal types of cancers. Gastric cancer stem cells (GCSCs) are thought to be the root of gastric cancer relapse, metastasis and drug resistance. Understanding GCSCs&#160;biology is needed to advance the development of targeted therapies and eventually to reduce mortality among patients. In this protocol, we present an experimental design using a conditional knockdown system and an adapted sphere formation assay to study the effect of clusterin on the stemness of patient-derived GCSCs. The protocol can be easily adapted to study both in vitro and in vivo function of stemness-associated genes in different types of CSCs.

In this protocol, we present an experimental design using a conditional knockdown system and an adapted sphere formation assay to study the effect of clusterin on the stemness of patient-derived GCSCs. The protocol can be easily adapted to study both in vitro and in vivo function of stemness-associated genes in different types of CSCs.

Cancer stem cells (CSCs) are implicated in tumor initiation, development and recurrence after treatment, and have become the center of attention of many ...

Drug Screening of Primary Patient Derived Tumor Xenografts in Zebrafish

Patient derived xenograft models are critical in defining how different cancers respond to drug treatment in an in vivo system. Mouse models are the standard in the field, but zebrafish have emerged as an alternative model with several advantages, including the ability for high-throughput and low-cost drug screening. Zebrafish also allow for in vivo drug screening with large replicate numbers that were previously only obtainable with in vitro systems. The ability to rapidly perform large scale drug screens may open up the possibility for personalized medicine with rapid translation of results back to clinic. Zebrafish xenograft models could also be used to rapidly screen for actionable mutations based on tumor response to targeted therapies or to identify new anti-cancer compounds from large libraries. The current major limitation in the field has been quantifying and automating the process so that drug screens can be done on a larger scale and be less labor-intensive. We have developed a workflow for xenografting primary patient samples into zebrafish larvae and performing large scale drug screens using a fluorescence microscope equipped imaging unit and automated sampler unit. This method allows for standardization and quantification of engrafted tumor area and response to drug treatment across large numbers of zebrafish larvae. Overall, this method is advantageous over traditional cell culture drug screening as it allows for growth of tumor cells in an in vivo environment throughout drug treatment, and is more practical and cost-effective than mice for large scale in vivo drug screens.

Zebrafish xenograft models allow for high-throughput drug screening and fluorescent imaging of human cancer cells in an in vivo microenvironment. We developed a workflow for large scale, automated drug screening on patient-derived leukemia samples in zebrafish using an automated fluorescence microscope equipped imaging unit.

Patient derived xenograft models are critical in defining how different cancers respond to drug treatment in an in vivo system. Mouse models are the ...

Pooled shRNA Library Screening to Identify Factors that Modulate a Drug Resistance Phenotype

Understanding clinically relevant driver mechanisms of acquired chemo-resistance is crucial for elucidating ways to circumvent resistance and improve survival in patients with acute myeloid leukemia (AML). A small fraction of leukemic cells that survive chemotherapy have a poised epigenetic state to tolerate chemotherapeutic insult. Further exposure to chemotherapy allows these drug persister cells to attain a fixed epigenetic state, which leads to altered gene expression, resulting in the proliferation of these drug-resistant populations and eventually relapse or refractory disease. Therefore, identifying epigenetic modulations that necessitate the survival of drug-resistant leukemic cells is critical. We detail a protocol to identify epigenetic modulators that mediate resistance to the nucleoside analog cytarabine (AraC) using pooled shRNA library screening in an acquired cytarabine-resistant AML cell line. The library consists of 5,485 shRNA constructs targeting 407 human epigenetic factors, which allows high-throughput epigenetic factor screening.

High-throughput RNA interference (RNAi) screening using a pool of lentiviral shRNAs can be a tool to detect therapeutically relevant synthetic lethal targets in malignancies. We provide a pooled shRNA screening approach to investigate the epigenetic effectors in acute myeloid leukemia (AML).

Understanding clinically relevant driver mechanisms of acquired chemo-resistance is crucial for elucidating ways to circumvent resistance and improve ...

Orthotopic Implantation of Patient-Derived Cancer Cells in Mice Recapitulates Advanced Colorectal Cancer

Over the last decade, more sophisticated preclinical colorectal cancer (CRC) models have been established using patient-derived cancer cells and 3D tumoroids. Since patient derived tumor organoids can retain the characteristics of the original tumor, these reliable preclinical models enable cancer drug screening and the study of drug resistance mechanisms. However, CRC related death in patients is mostly associated with the presence of metastatic disease. It is therefore essential to evaluate the efficacy of anti-cancer therapies in relevant in vivo models that truly recapitulate the key molecular features of human cancer metastasis. We have established an orthotopic model based on the injection of CRC patient-derived cancer cells directly into the cecum wall of mice. These tumor cells develop primary tumors in the cecum that metastasize to the liver and lungs, which is frequently observed in patients with advanced CRC. This CRC mouse model can be used to evaluate drug responses monitored by microcomputed tomography (&#181;CT), a clinically relevant small-scale imaging method that can easily identify primary tumors or metastases in patients. Here, we describe the surgical procedure and the required methodology to implant patient-derived cancer cells in the cecum wall of immunodeficient mice.

This protocol describes the orthotopic implantation of patient-derived cancer cells in the cecum wall of immunodeficient mice. The model recapitulates advanced colorectal cancer metastatic disease and allows for the evaluation of new therapeutic drugs in a clinically relevant scenario of lung and liver metastases.

Over the last decade, more sophisticated preclinical colorectal cancer (CRC) models have been established using patient-derived cancer cells and 3D ...