This work was supported by grants from the Ontario Institute for Cancer Research, the Canada Foundation for Innovation, the Ottawa Regional Cancer Foundation, and the Terry Fox Research Institute. K.J.A. was supported by a Vanier Canada Graduate Scholarship, a Canadian Institute for Health Research-Master&#39;s Award, and an Ontario Graduate Scholarship.

<ol>
	<li>Cherry, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+have+RNAi+screens+taught+us+about+viral-host+interactions?.">What have RNAi screens taught us about viral-host interactions?.</a> Curr. Opin. Microbiol. 12 (4), 446-452 (2009).</li><li>Panda, D., Cherry, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cell-based+genomic+screening:+elucidating+virus-host+interactions.">Cell-based genomic screening: elucidating virus-host interactions.</a> Curr. Opin. Virol. 2 (6), 784-792 (2012).</li><li>Houzet, L., Jeang, K. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+screening+using+RNA+interference+to+study+host+factors+in+viral+replication+and+pathogenesis.">Genome-wide screening using RNA interference to study host factors in viral replication and pathogenesis.</a> Exp. Biol. Med. 236 (8), 962-967 (2011).</li><li>Friedel, C. C., Haas, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Virus-host+interactomes+and+global+models+of+virus-infected+cells.">Virus-host interactomes and global models of virus-infected cells.</a> Trends Microbiol. 19 (10), 501-508 (2011).</li><li>Gao, S., Yang, 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=Applications+of+RNA+interference+high-throughput+screening+technology+in+cancer+biology+and+virology.">Applications of RNA interference high-throughput screening technology in cancer biology and virology.</a> Protein Cell. 5 (11), 805-815 (2014).</li><li>Kilcher, S., Mercer, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Next+generation+approaches+to+study+virus+entry+and+infection.">Next generation approaches to study virus entry and infection.</a> Curr. Opin. Virol. 4, 8-14 (2014).</li><li>Ramage, H., Cherry, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Virus-Host+Interactions:+From+unbiased+genetic+screens+to+function.">Virus-Host Interactions: From unbiased genetic screens to function.</a> Annu. Rev. Virol. 2 (1), 497-524 (2015).</li><li>Mercer, J., Snijder, B., 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=RNAi+screening+reveals+proteasome-+and+Cullin3-dependent+stages+in+vaccinia+virus+infection.">RNAi screening reveals proteasome- and Cullin3-dependent stages in vaccinia virus infection.</a> Cell Rep. 2 (4), 1036-1047 (2012).</li><li>Sivan, G., Martin, S. E., 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=Human+genome-wide+RNAi+screen+reveals+a+role+for+nuclear+pore+proteins+in+poxvirus+morphogenesis.">Human genome-wide RNAi screen reveals a role for nuclear pore proteins in poxvirus morphogenesis.</a> Proc. Natl. Acad. Sci. U.S.A. 110 (9), 3519-3524 (2013).</li><li>Beard, P. M., Griffiths, S. J., 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=A+loss+of+function+analysis+of+host+factors+Influencing+vaccinia+virus+replication+by+RNA+Interference.">A loss of function analysis of host factors Influencing vaccinia virus replication by RNA Interference.</a> PLoS ONE. 9 (6), e98431 (2014).</li><li>Teferi, W. M., Dodd, K., Maranchuk, R., Favis, N., Evans, D. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+whole-genome+RNA+interference+screen+for+human+cell+factors+affecting+myxoma+virus+replication.">A whole-genome RNA interference screen for human cell factors affecting myxoma virus replication.</a> J. Virol. 87 (8), 4623-4641 (2013).</li><li>Workenhe, S. T., Ketela, T., Moffat, J., Cuddington, B. P., Mossman, K. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+lentiviral+shRNA+screen+identifies+serine/arginine-rich+splicing+factor+2+as+a+determinant+of+oncolytic+virus+activity+in+breast+cancer+cells.">Genome-wide lentiviral shRNA screen identifies serine/arginine-rich splicing factor 2 as a determinant of oncolytic virus activity in breast cancer cells.</a> Oncogene. 35 (19), 2465-2477 (2015).</li><li>Panda, D., Das, 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=RNAi+screening+reveals+requirement+for+host+cell+secretory+pathway+in+infection+by+diverse+families+of+negative-strand+RNA+viruses.">RNAi screening reveals requirement for host cell secretory pathway in infection by diverse families of negative-strand RNA viruses.</a> Proc. Natl. Acad. Sci. U. S. A. 108 (47), 19036-19041 (2011).</li><li>Lee, A. S. -. Y., Burdeinick-Kerr, R., Whelan, S. P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+genome-wide+small+interfering+RNA+screen+identifies+host+factors+required+for+vesicular+stomatitis+virus+infection.">A genome-wide small interfering RNA screen identifies host factors required for vesicular stomatitis virus infection.</a> J. Virol. 88 (15), 8355-8360 (2014).</li><li>Mahoney, D. J., Lefebvre, 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=Virus-tumor+interactome+screen+reveals+ER+stress+response+can+reprogram+resistant+cancers+for+oncolytic+virus-triggered+Caspase-2+cell+death.">Virus-tumor interactome screen reveals ER stress response can reprogram resistant cancers for oncolytic virus-triggered Caspase-2 cell death.</a> Cancer Cell. 20 (4), 443-456 (2011).</li><li>Mahoney, D. J., Stojdl, D. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+call+to+arms:+Using+RNAi+screening+to+improve+oncolytic+viral+therapy.">A call to arms: Using RNAi screening to improve oncolytic viral therapy.</a> Cytokine Growth Factor Rev. 21 (2-3), 161-167 (2010).</li><li>Mahoney, D. J., Stojdl, D. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+genomic+screening+to+enhance+oncolytic+virotherapy.">Functional genomic screening to enhance oncolytic virotherapy.</a> Br. J. Cancer. 108 (2), 245-249 (2012).</li><li>Allan, K. J., Stojdl, D. F., Swift, S. L. <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+screening+to+enhance+oncolytic+virus+immunotherapy.">High-throughput screening to enhance oncolytic virus immunotherapy.</a> Oncolytic Virother. 5, 15-25 (2016).</li><li>Lundholt, B. K., Scudder, K. M., Pagliaro, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+simple+technique+for+reducing+edge+effect+in+cell-based+assays.">A simple technique for reducing edge effect in cell-based assays.</a> J. Biomol. Screen. 8 (5), 566-570 (2003).</li><li>Birmingham, A., Selfors, L. 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=Statistical+methods+for+analysis+of+high-throughput+RNA+interference+screens.">Statistical methods for analysis of high-throughput RNA interference screens.</a> Nat. Methods. 6 (8), 569-575 (2009).</li><li>Zhang, J., Chung, T., Oldenburg, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+simple+statistical+parameter+for+use+in+evaluation+and+validation+of+high+throughput+screening+assays.">A simple statistical parameter for use in evaluation and validation of high throughput screening assays.</a> J. Biomol. Screen. 4 (2), 67-73 (1999).</li><li>Huang, D. W., Sherman, B. T., Lempicki, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+and+integrative+analysis+of+large+gene+lists+using+DAVID+bioinformatics+resources.">Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.</a> Nat. Protoc. 4 (1), 44-57 (2009).</li><li>Huang, D. W., Sherman, B. T., Lempicki, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bioinformatics+enrichment+tools:+paths+toward+the+comprehensive+functional+analysis+of+large+gene+lists.">Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.</a> Nucleic Acids Res. 37 (1), 1-13 (2009).</li><li>Thomas, P. D., Campbell, M. J., 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=PANTHER:+a+library+of+protein+families+and+subfamilies+indexed+by+function.">PANTHER: a library of protein families and subfamilies indexed by function.</a> Genome Res. 13 (9), 2129-2141 (2003).</li><li>Szklarczyk, D., Morris, J. H., 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=The+STRING+database+in+2017:+quality-controlled+protein-protein+association+networks,+made+broadly+accessible.">The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.</a> Nucleic Acids Res. 45 (D1), D362-D368 (2017).</li><li>Luc, P. -. V., Tempst, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PINdb:+a+database+of+nuclear+protein+complexes+from+human+and+yeast.">PINdb: a database of nuclear protein complexes from human and yeast.</a> Bioinformatics. 20 (9), 1413-1415 (2004).</li><li>Chung, N., Zhang, X. D., 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=Median+absolute+deviation+to+improve+hit+selection+for+genome-scale+RNAi+screens.">Median absolute deviation to improve hit selection for genome-scale RNAi screens.</a> J. Biomol. Screen. 13 (2), 149-158 (2008).</li><li>Zhang, X. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Illustration+of+SSMD,+z+Score,+SSMD*,+z*+Score,+and+t+statistic+for+hit+selection+in+RNAi+high-throughput+screens.">Illustration of SSMD, z Score, SSMD*, z* Score, and t statistic for hit selection in RNAi high-throughput screens.</a> J. Biomol. Screen. 16 (7), 775-785 (2011).</li><li>Zhang, X. D., Lacson, R., 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=The+use+of+SSMD-based+false+discovery+and+false+nondiscovery+rates+in+genome-scale+RNAi+screens.">The use of SSMD-based false discovery and false nondiscovery rates in genome-scale RNAi screens.</a> J. Biomol. Screen. 15 (9), 1123-1131 (2010).</li><li>Amberkar, S. <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+RNA+interference+screens+integrative+analysis:+towards+a+comprehensive+understanding+of+the+virus-host+interplay.">High-throughput RNA interference screens integrative analysis: towards a comprehensive understanding of the virus-host interplay.</a> World J. Virol. 2 (2), 18-31 (2013).</li><li>Barrows, N. J., Le Sommer, C., Garcia-Blanco, M. A., Pearson, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Factors+affecting+reproducibility+between+genome-scale+siRNA-based+screens.">Factors affecting reproducibility between genome-scale siRNA-based screens.</a> J. Biomol. Screen. 15 (7), 735-747 (2010).</li><li>Carralot, J. -. P., Ogier, 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=A+novel+specific+edge+effect+correction+method+for+RNA+interference+screenings.">A novel specific edge effect correction method for RNA interference screenings.</a> Bioinformatics. 28 (2), 261-268 (2011).</li><li>Caffrey, D. R., Zhao, J., 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=siRNA+off-target+effects+can+be+reduced+at+concentrations+that+match+their+individual+potency.">siRNA off-target effects can be reduced at concentrations that match their individual potency.</a> PLoS ONE. 6 (7), e21503-e21511 (2011).</li><li>Makarenkov, V., Zentilli, P., Kevorkov, D., Gagarin, A., Malo, N., Nadon, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+efficient+method+for+the+detection+and+elimination+of+systematic+error+in+high-throughput+screening.">An efficient method for the detection and elimination of systematic error in high-throughput screening.</a> Bioinformatics. 23 (13), 1648-1657 (2007).</li><li>Caraus, I., Alsuwailem, A. A., Nadon, R., Makarenkov, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detecting+and+overcoming+systematic+bias+in+high-throughput+screening+technologies:+a+comprehensive+review+of+practical+issues+and+methodological+solutions.">Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions.</a> Brief. Bioinform. 16 (6), 974-986 (2015).</li><li>Savidis, G., McDougall, W. 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=Identification+of+Zika+virus+and+dengue+virus+dependency+factors+using+functional+genomics.">Identification of Zika virus and dengue virus dependency factors using functional genomics.</a> Cell Rep. 16 (1), 232-246 (2016).</li><li>Ma, H., Dang, 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=A+CRISPR-Based+screen+identifies+genes+essential+for+West-Nile-virus-induced+cell+death.">A CRISPR-Based screen identifies genes essential for West-Nile-virus-induced cell death.</a> Cell Rep. 12 (4), 673-683 (2015).</li><li>Zhang, R., Miner, J. J., 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=A+CRISPR+screen+defines+a+signal+peptide+processing+pathway+required+by+flaviviruses.">A CRISPR screen defines a signal peptide processing pathway required by flaviviruses.</a> Nature. 535 (7610), 164-168 (2016).</li><li>Marceau, C. D., Puschnik, A. S., 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=Genetic+dissection+of+Flaviviridae+host+factors+through+genome-scale+CRISPR+screens.">Genetic dissection of Flaviviridae host factors through genome-scale CRISPR screens.</a> Nature. 535 (7610), 159-163 (2016).</li></ol>

The authors have nothing to disclose.

Here we present a protocol for employing high-throughput RNAi screening to identify host targets that can be manipulated to enhance oncolytic virus therapy. This has been tested successfully with oncolytic Maraba virus and oncolytic vaccinia virus, but, as noted, it can be adapted for use with other oncolytic viruses or other viruses generally to identify host genes that modulate virus replication. The screening protocol is also designed to identify genes that increase or decrease spread, but it can be readily modified f...

High-throughput genome-wide RNAi screening has proven to be an invaluable tool for revealing biological insights in diverse areas of study including virus biology. Over the past decade or so, numerous groups have undertaken cell-based large-scale RNAi screening to extensively identify host genes that modulate virus replication (Reviewed in1,2,3,4,5,6,7). Interestingly, a large number of these screens have been conducted in cancer cells and several with viruses that are current oncolytic virus candidates including vaccinia virus8,9,10, Myxoma virus11, herpes simplex virus12, vesicular stomatitis virus13,14, and Maraba virus15. While the focus of the majority of these studies was on identifying host factors that influence some aspect of virus replication and not on identifying biotherapeutic targets for enhancing oncolytic virus therapy, valuable data could be mined from these data sets in this regard. It is clear that the powerful potential to identify host targets that can be manipulated to enhance oncolytic virus therapy has not been fully exploited.
A plethora of oncolytic virus platforms are currently being tested in preclinical and clinical studies including herpes simplex virus, reovirus and vaccinia virus, which have had demonstrable success in late-stage clinical trials. For the majority of these platforms, efforts have been directed toward altering the virus genomes to enhance therapy. For instance, to increase tumor specificity, specific virus genes have been deleted or mutated to significantly restrict viral replication in normal cells, but not in tumor cells. In some cases, transgenes have been added like GM-CSF (granulocyte macrophage colony-stimulating factor) to potentiate the immune response or NIS (sodium iodide symporter) to enable in vivo imaging and cellular radioiodide uptake for therapeutic purposes. However, there has been very little work focused on manipulating host/tumor genes and exploring the impact of host/tumor genes on oncolytic virus replication. With the advent of high-throughput screening technology, it is now possible to probe host-virus interactions on a genome scale and to decipher opportunities to manipulate the host genome to enhance oncolytic virus therapy (Reviewed in16,17,18).
We reported the first study demonstrating proof-of-concept for using high-throughput genetic screening to identify host targets that could be manipulated to enhance oncolytic virus therapy. To discover host genes that modulate Maraba virus oncolysis, an oncolytic rhabdovirus currently being tested in phase I and II trials, we conducted genome-wide RNAi screens across three different tumor cell lines in duplicate with an siRNA (short interfering RNA) library targeting 18,120 genes15. Pathway analysis of hits identified in the screens revealed enrichment of members of the ER (endoplasmic reticulum) stress response pathways. We selected 10 hits within these pathways for secondary validation using siRNA with different targeting sequences from those of the primary screen. As part of tertiary validation, we conducted a rescue experiment with IRE1&#945; (inositol-requiring enzyme 1 alpha) to confirm that the hit was on target. In vitro and in vivo testing using a small molecule inhibitor of IRE1&#945; resulted in dramatically enhanced oncolytic efficacy.
Workenhe et al.12 also conducted a genome-wide RNAi screen using a pooled lentiviral shRNA (short hairpin RNA) library targeting 16,056 genes to identify host factors restricting the human herpes simplex virus type 1 (HSV-1) mutant KM100-mediated oncolysis of breast cancer cells. In the primary screen, they identified 343 genes the knockdown of which lead to enhanced cytotoxicity of KM100-infected cells over mock-infected cells; they selected 24 of these genes for secondary validation. Out of the 24 genes, 8 genes were confirmed in the secondary screen with one of them being SRSF2 (serine/arginine-rich splicing factor 2) which was subsequently confirmed using a genetic rescue experiment during tertiary validation. The group went on to identify a DNA topoisomerase inhibitor chemotherapeutic that reduced the phosphorylation of SRSF2 and showed that the combination treatment of HSV-1 KM100 with this inhibitor lead to extended survival of TUBO tumor-bearing mice.
In the aforementioned studies, a similar workflow was followed. Each began with a primary genome-wide RNAi screen followed by a secondary screen on a select number of hits identified in the primary screen. This was followed by tertiary validation studies, which included confirming that the hit was on-target by performing genetic rescue experiments in vitro with ultimate validation performed by manipulating the target gene product in vivo. In this article, we first provide a general protocol for the development and validation of a high-throughput siRNA-screening assay, which involves determining the optimal transfection conditions, amount of virus, and length of infection. We also offer a detailed protocol for conducting a primary a high-throughput siRNA screen as well as an overall description of the method for performing secondary screen validation and tertiary validation.

<table>
	<tbody>
		<tr>
			<td>Consumables</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>siRNA library</td>
			<td>GE Dharmacon</td>
			<td>G-005005-02</td>
			<td></td>
		</tr>
		<tr>
			<td>Corning 384-well plate</td>
			<td>Corning</td>
			<td>3985</td>
			<td></td>
		</tr>
		<tr>
			<td>Falcon 384-well plate</td>
			<td>Becton Dickinson</td>
			<td>353962</td>
			<td></td>
		</tr>
		<tr>
			<td>Water</td>
			<td>Sigma</td>
			<td>W4502</td>
			<td></td>
		</tr>
		<tr>
			<td>siGenome SMARTpool PLK1</td>
			<td>GE Dharmacon</td>
			<td>M-003290-01</td>
			<td></td>
		</tr>
		<tr>
			<td>AllStars Negative Control siRNA</td>
			<td>Qiagen</td>
			<td>SI03650318</td>
			<td></td>
		</tr>
		<tr>
			<td>siGenome Non-targeting siRNA Pool #2</td>
			<td>GE Dharmacon</td>
			<td>D-001206-14-20</td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM/HIGH Glucose</td>
			<td>GE Healthcare Life Sciences</td>
			<td>SH30022.01</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum</td>
			<td>Sigma</td>
			<td>F15051-500ML</td>
			<td></td>
		</tr>
		<tr>
			<td>HEPES solution (1M)</td>
			<td>Sigma</td>
			<td>H3535-100ML</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin 100X solution</td>
			<td>GE Healthcare Life Sciences</td>
			<td>SV30010</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypsin-EDTA (0.25%)</td>
			<td>Thermo Fisher Scientific</td>
			<td>2500056</td>
			<td></td>
		</tr>
		<tr>
			<td>DPBS/Modified</td>
			<td>GE Healthcare Life Sciences</td>
			<td>SH30028.02</td>
			<td></td>
		</tr>
		<tr>
			<td>Lipofectamine RNAiMAX</td>
			<td>Thermo Fisher Scientific</td>
			<td>13778100</td>
			<td></td>
		</tr>
		<tr>
			<td>Oligofectamine</td>
			<td>Thermo Fisher Scientific</td>
			<td>1225011</td>
			<td></td>
		</tr>
		<tr>
			<td>Opti-MEM</td>
			<td>Thermo Fisher Scientific</td>
			<td>22600-050</td>
			<td></td>
		</tr>
		<tr>
			<td>Resazurin sodium salt</td>
			<td>Sigma</td>
			<td>R7017-5G</td>
			<td></td>
		</tr>
		<tr>
			<td>Hoechst 33342</td>
			<td>Thermo Fisher Scientific</td>
			<td>H21492</td>
			<td></td>
		</tr>
		<tr>
			<td>Formaldehyde (37% by weight)</td>
			<td>Fisher Scientific</td>
			<td>F79-4</td>
			<td></td>
		</tr>
		<tr>
			<td>500 ml bottles</td>
			<td>Corning</td>
			<td>430282</td>
			<td></td>
		</tr>
		<tr>
			<td>Adhesive Sealing Film For Microplates</td>
			<td>Excel Scientific</td>
			<td>361006007</td>
			<td></td>
		</tr>
		<tr>
			<td>Peelable Heat Sealing Foil</td>
			<td>Thermo Fisher Scientific</td>
			<td>AB-3720</td>
			<td></td>
		</tr>
		<tr>
			<td>BioTek MicroFlo Select Dispenser cassette</td>
			<td>Fisher Scientific</td>
			<td>11-120-625</td>
			<td></td>
		</tr>
		<tr>
			<td>Name</td>
			<td>Company</td>
			<td>Catalog Number</td>
			<td>Comments</td>
		</tr>
		<tr>
			<td>Equipment</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>MicroFlo Select (liquid dispenser)</td>
			<td>Fisher Scientific</td>
			<td>11120621</td>
			<td></td>
		</tr>
		<tr>
			<td>BioTek Synergy HT plate reader</td>
			<td>Biotek</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td>Opera Imaging and Analysis Instrument</td>
			<td>PerkinElmer</td>
			<td>HH10000115</td>
			<td></td>
		</tr>
		<tr>
			<td>KiNEDx Robotic Arm</td>
			<td>Peak Analysis and Automation (formerly Peak Robotics)</td>
			<td>KX-300-660</td>
			<td></td>
		</tr>
		<tr>
			<td>Cytomat 24C Series Automated Incubator</td>
			<td>Thermo Fisher Scientific</td>
			<td>50080227</td>
			<td></td>
		</tr>
		<tr>
			<td>JANUS Automated Workstation</td>
			<td>PerkinElmer</td>
			<td>AJI4M01</td>
			<td></td>
		</tr>
		<tr>
			<td>Plate Carousel</td>
			<td>Peak Analysis and Automation (formerly Peak Robotics)</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td>Alps 3000 plate sealer</td>
			<td>Thermo Fisher Scientific</td>
			<td>AB-3000</td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

An overview of the workflow for identifying host targets to enhance oncolytic virus therapy is presented in Figure 1.
As illustrated in Figure 1, the critical first step to conducting a high-throughput RNAi screen is assay development. Figure 2A provides a sample plate layout for the transfection optimization assay. Fig...

Watch this Scientific Journal Video about Genome-wide RNAi Screening to Identify Host Factors That Modulate Oncolytic Virus Therapy at JoVE.com

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

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 ...

genome-wide-rnai-screening-to-identify-host-factors-that-modulate

Research

JoVE Journal

Cancer Research

9.0K Views.  Children's Hospital of Eastern Ontario (CHEO) Research Institute. 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.

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

Utilizing Functional Genomics Screening to Identify Potentially Novel Drug Targets in Cancer Cell Spheroid Cultures

The identification of functional driver events in cancer is central to furthering our understanding of cancer biology and indispensable for the discovery of the next generation of novel drug targets. It is becoming apparent that more complex models of cancer are required to fully appreciate the contributing factors that drive tumorigenesis in vivo and increase the efficacy of novel therapies that make the transition from pre-clinical models to clinical trials.
Here we present a methodology for generating uniform and reproducible tumor spheroids that can be subjected to siRNA functional screening. These spheroids display many characteristics that are found in solid tumors that are not present in traditional two-dimension culture. We show that several commonly used breast cancer cell lines are amenable to this protocol. Furthermore, we provide proof-of-principle data utilizing the breast cancer cell line BT474, confirming their dependency on amplification of the epidermal growth factor receptor HER2 and mutation of phosphatidylinositol-4,5-biphosphate 3-kinase (PIK3CA) when grown as tumor spheroids. Finally, we are able to further investigate and confirm the spatial impact of these dependencies using immunohistochemistry.

Identifying novel drug targets that transition from pre-clinical testing to human trials is a scientific priority. To that end, here we describe a functional genomics approach for examining the impact of gene depletion on cancer cell line spheroids, which more appropriately model human cancers in vivo.

The identification of functional driver events in cancer is central to furthering our understanding of cancer biology and indispensable for the discovery ...

Flow Cytometry-based Drug Screening System for the Identification of Small Molecules That Promote Cellular Differentiation of Glioblastoma Stem Cells

Glioblastoma (GBM) is the most common and most lethal primary brain tumor in adults, causing roughly 14,000 deaths each year in the U.S. alone. Median survival following diagnosis is less than 15 months with maximal surgical resection, radiation, and temozolomide chemotherapy. The challenges inherent in developing more effective GBM treatments have become increasingly clear, and include its unyielding invasiveness, its resistance to standard treatments, its genetic complexity and molecular adaptability, and subpopulations of GBM cells with phenotypic similarities to normal stem cells, herein referred to as glioblastoma stem cells (GSCs). Because GSCs are required for tumor growth and progression, differentiation-based therapy represents a viable treatment modality for these incurable neoplasms. 
The following protocol describes a collection of procedures to establish a high throughput screening platform aimed at the identification of small molecules that promote GSC astroglial differentiation. At the core of the system is a glial fibrillary acidic protein (GFAP) differentiation reporter-construct. The protocol contains the following general procedures: (1) establishing GSC differentiation reporter lines; (2) testing/validating the relevance of the reporter to GSC self-renewal/clonogenic capacity; and (3) high-capacity flow-cytometry based drug screening. 
The screening platform provides a straightforward and inexpensive approach to identify small molecules that promote GSCs differentiation. Furthermore, utilization of libraries of FDA-approved drugs holds the potential for the identification of agents that can be repurposed more rapidly. Also, therapies that promote cancer stem cell differentiation are expected to work synergistically with current "standard of care" therapies that have been shown to target and eliminate primarily more differentiated cancer cells.

An efficient screening protocol is presented for the identification of small molecules that promote astroglial differentiation in glioblastoma stem cells (GSCs). The assay is based on a stem cell differentiation reporter whereby the expression of the enhanced GFP (eGFP) is driven by the human GFAP promoter.

Glioblastoma (GBM) is the most common and most lethal primary brain tumor in adults, causing roughly 14,000 deaths each year in the U.S. alone. Median ...

An Integrated Platform for Genome-wide Mapping of Chromatin States Using High-throughput ChIP-sequencing in Tumor Tissues

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of associated loci. In addition, the presence of specific modifications has been used to determine the position and identity non-coding functional elements such as enhancers. In recent years, chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq) has become a powerful tool in determining the genome-wide profiles of individual histone modifications. However, it has become increasingly clear that the combinatorial patterns of chromatin modifications, referred to as Chromatin States, determine the identity and nature of the associated genomic locus. Therefore, workflows consisting of robust high-throughput (HT) methodologies for profiling a number of histone modification marks, as well as computational analyses pipelines capable of handling myriads of ChIP-Seq profiling datasets, are needed for comprehensive determination of epigenomic states in large number of samples. The HT-ChIP-Seq workflow presented here consists of two modules: 1) an experimental protocol for profiling several histone modifications from small amounts of tumor samples and cell lines in a 96-well format; and 2) a computational data analysis pipeline that combines existing tools to compute both individual mark occupancy and combinatorial chromatin state patterns. Together, these two modules facilitate easy processing of hundreds of ChIP-Seq samples in a fast and efficient manner. The workflow presented here is used to derive chromatin state patterns from 6 histone mark profiles in melanoma tumors and cell lines. Overall, we present a comprehensive ChIP-seq workflow that can be applied to dozens of human tumor samples and cancer cell lines to determine epigenomic aberrations in various malignancies.

Here, we describe an optimized high-throughput ChIP-sequencing protocol and computational analyses pipeline for the determination of genome-wide chromatin state patterns from frozen tumor tissues and cell lines.

Histone modifications constitute a major component of the epigenome and play important regulatory roles in determining the transcriptional status of ...

Virus Delivery of CRISPR Guides to the Murine Prostate for Gene Alteration

With an increasing incidence of prostate cancer, identification of new tumor drivers or modulators is crucial. Genetically engineered mouse models (GEMM) for prostate cancer are hampered by tumor heterogeneity and its complex microevolution dynamics. Traditional prostate cancer mouse models include, amongst others, germline and conditional knockouts, transgenic expression of oncogenes, and xenograft models. Generation of de novo mutations in these models is complex, time-consuming, and costly. In addition, most of traditional models target the majority of the prostate epithelium, whereas human prostate cancer is well known to evolve as an isolated event in only a small subset of cells. Valuable models need to simulate not only prostate cancer initiation, but also progression to advanced disease.
Here we describe a method to target a few cells in the prostate epithelium by transducing cells by viral particles. The delivery of an engineered virus to the murine prostate allows alteration of gene expression in the prostate epithelia. Virus type and quantity will hereby define the number of targeted cells for gene alteration by transducing a few cells for cancer initiation and many cells for gene therapy. Through surgery-based injection in the anterior lobe, distal from the urinary track, the tumor in this model can expand without impairing the urinary function of the animal. Furthermore, by targeting only a subset of prostate epithelial cells the technique enables clonal expansion of the tumor, and therefore mimics human tumor initiation, progression, as well as invasion through the basal membrane.
This novel technique provides a powerful prostate cancer model with improved physiological relevance. Animal suffering is limited, and since no additional breeding is required, overall animal count is reduced. At the same time, analysis of new candidate genes and pathways is accelerated, which in turn is more cost efficient.

This protocol describes a newly established method for virus delivery to the murine prostate. Using either CRISPR/Cas9 technology, gene overexpression, or Cre recombinase delivery, the technique allows orthotopic alteration of gene expression and implements a novel mouse model for prostate cancer.

With an increasing incidence of prostate cancer, identification of new tumor drivers or modulators is crucial. Genetically engineered mouse models (GEMM) ...

A Chromatin Immunoprecipitation Assay to Identify Novel NFAT2 Target Genes in Chronic Lymphocytic Leukemia

Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant B cell clones and represents the most common leukemia in western countries. The majority of CLL patients show an indolent course of the disease as well as an anergic phenotype of their leukemia cells, referring to a B cell receptor unresponsive to external stimulation. We have recently shown that the transcription factor NFAT2 is a crucial regulator of anergy in CLL. A major challenge in the analysis of the role of a transcription factor in different diseases is the identification of its specific target genes. This is of&#160;great significance for the elucidation of pathogenetic mechanisms and potential therapeutic interventions. Chromatin immunoprecipitation (ChIP) is a classic technique to demonstrate protein-DNA interactions and can, therefore, be used to identify direct target genes of transcription factors in mammalian cells. Here, ChIP was used to identify LCK as a direct target gene of NFAT2 in human CLL cells. DNA and associated proteins are crosslinked using formaldehyde and subsequently sheared by sonication into DNA fragments of approximately 200-500 base pairs (bp). Cross-linked DNA fragments associated with NFAT2 are then selectively immunoprecipitated from cell debris using an &#945;NFAT2 antibody. After purification, associated DNA fragments are detected via quantitative real-time PCR (qRT-PCR). DNA sequences with evident enrichment represent regions of the genome which are targeted by NFAT2 in vivo. Appropriate shearing of the DNA and the selection of the required antibody are particularly crucial for the successful application of this method. This protocol is ideal for the demonstration of direct interactions of NFAT2 with target genes. Its major limitation is the difficulty to employ ChIP in large-scale assays analyzing the target genes of multiple transcription factors in intact organisms.

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the western world. NFAT transcription factors are important regulators of development and activation in numerous cell types. Here, we present a protocol for the use of chromatin immunoprecipitation (ChIP) in human CLL cells to identify novel target genes of NFAT2.

Chronic lymphocytic leukemia (CLL) is characterized by the expansion of malignant B cell clones and represents the most common leukemia in western ...

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 ...

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Differential gene expression analysis is an important technique for understanding disease states. The machine learning algorithm CorEx has shown utility in analyzing differential expression of groups of genes in tumor RNA-seq in a way that may be helpful for advancing precision oncology. However, CorEx produces many factors that can be challenging to analyze and connect to existing understanding. To facilitate such connections, we have built a website, CorExplorer, that allows users to interactively explore the data and answer common questions related to its analysis. We trained CorEx on RNA-seq gene expression data for four tumor types: ovarian, lung, melanoma, and colorectal. We then incorporated corresponding survival, protein-protein interactions, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments, and heatmaps into the website for association with the factor graph visualization. Here we employ example protocols to illustrate use of the database for comprehending the significance of the learned tumor factors in the context of this external data.

We introduce the CorExplorer web portal, an resource for exploration of tumor RNA sequencing factors found by the machine learning algorithm CorEx (Correlation Explanation), and show how factors can be analyzed relative to survival, database annotations, protein-protein interactions, and one another to gain insight into tumor biology and therapeutic interventions.

Differential gene expression analysis is an important technique for understanding disease states. The machine learning algorithm CorEx has shown utility ...

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

DNA methylation is an important epigenetic change that is biologically meaningful and a frequent focus of cancer research. Genome-wide DNA methylation is a useful measure to provide an accurate analysis of the methylation status of gastrointestinal (GI) malignancies. Given the multiple potential translational uses of DNA methylation analysis, practicing clinicians and others new to DNA methylation studies need to be able to understand step by step how these genome-wide analyses are performed. The goal of this protocol is to provide a detailed description of how this method is used for the biomarker identification in GI malignancies. Importantly, we describe three critical steps that are needed to obtain accurate results during genome-wide analysis. Clearly and concisely written, these three methods are often lacking and not noticeable to those new to epigenetic studies. We used 48 samples of a GI malignancy (gastric cancer) to highlight practically how genome-wide DNA methylation analysis can be performed for GI malignancies.

Herein, we describe a procedure for genome-wide analysis of DNA methylation in gastrointestinal cancers. The procedure is of relevance to studies that investigate relationships between methylation patterns of genes and factors contributing to carcinogenesis in gastrointestinal cancers.

DNA methylation is an important epigenetic change that is biologically meaningful and a frequent focus of cancer research. Genome-wide DNA methylation is ...

A Murine Ommaya Xenograft Model to Study Direct-Targeted Therapy of Leptomeningeal Disease

Leptomeningeal disease (LMD) is an uncommon type of central nervous system (CNS) metastasis to the cerebral spinal fluid (CSF). The most common cancers that cause LMD are breast and lung cancers and melanoma. Patients diagnosed with LMD have a very poor prognosis and generally survive for only a few weeks or months. One possible reason for the lack of efficacy of systemic therapy against LMD is the failure to achieve therapeutically effective concentrations of drug in the CSF because of an intact and relatively impermeable blood-brain barrier (BBB) or blood-CSF barrier across the choroid plexus. Therefore, directly administering drugs intrathecally or intraventricularly may overcome these barriers. This group has developed a model that allows for the effective delivery of therapeutics (i.e., drugs, antibodies, and cellular therapies) chronically and the repeated sampling of CSF to determine drug concentrations and target modulation in the CSF (when the tumor microenvironment is targeted in mice). The model is the murine equivalent of a magnetic resonance imaging-compatible Ommaya reservoir, which is used clinically. This model, which is affixed to the skull, has been designated as the &#34;Murine Ommaya.&#34; As a therapeutic proof of concept, human epidermal growth factor receptor 2 antibodies (clone 7.16.4) were delivered into the CSF via the Murine Ommaya to treat mice with LMD from human epidermal growth factor receptor 2-positive breast cancer. The Murine Ommaya increases the efficiency of drug delivery using a miniature access port and prevents the wastage of excess drug; it does not interfere with CSF sampling for molecular and immunological studies. The Murine Ommaya is useful for testing novel therapeutics in experimental models of LMD.

Here, we describe a murine xenograft model that functionally resembles an Ommaya reservoir in patients. We developed the Murine Ommaya to study novel therapeutics for the universally fatal leptomeningeal disease.

Leptomeningeal disease (LMD) is an uncommon type of central nervous system (CNS) metastasis to the cerebral spinal fluid (CSF). The most common cancers ...

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