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 for other readouts. Our screens with Maraba virus, for instance, were designed to identify genes modulating oncolysis utilizing a simple resazurin-based vital dye assay to score cell viability (see Figure 4)15. In these screens, following incubation with virus, instead of fixing cells with formaldehyde and staining with Hoechst, we dispensed resazurin dye into each well (final concentration of 20 &#956;g/mL) and, after a 6 h incubation, we measured absorbance (573 nm) with a plate reader. We could have also used cell number based on Hoechst staining as a readout. While using a surrogate reporter to monitor virus replication was unnecessary in this screen, a virus with a reporter protein, particular a fluorescent protein does facilitate screen optimization as one can, for example, monitor virus replication live; furthermore, a virus with a reporter protein is less cumbersome and costly than using antibodies to stain for viral antigens, but it is possible to screen without one. In addition, one could make even further modifications to the assay to investigate host factors that impact on infectivity, burst size, and even more detailed sub-phenotypes like immunological cell death. In each case, one would follow a similar assay development protocol, screening strategy and secondary and tertiary validation methods.
In the protocol, we suggest optimizing transfection conditions ideally with multiple tumor cell lines. There are at least two reasons for optimizing with multiple cell lines. One reason is that not every cell line is amenable to high-throughput screening as some may be difficult to transfect or may not adhere well, but a key reason is to enable screening with multiple cell lines in order to avoid cell intrinsic bias. The choice of cell lines is largely dependent on one&#39;s objectives. One may elect to focus on a particular cancer type or select disparate cancer types to cover a broader spectrum. Alternatively, one may wish to focus on tumor cell lines that are resistant to identify host factors that can be manipulated to make the tumor cell lines less resistant to oncolytic virus infection.
In addition to the selection of cells lines, the selection of positive and negative virus controls is an important consideration for any screen. In some instances, virus genes that are required for or restrict replication may not be known or, if they are known, they may be cell-specific. Consequently, one can still determine the approximate robustness and dynamic range of the assay by using surrogate controls. For example, one could use uninfected cells or cells infected with a low MOI as a surrogate positive control for identifying genes that decrease spread upon knockdown. For identifying genes that enhance spread upon knockdown, one could infect cells with a dilution series of virus, and use wells with the maximum signal as a surrogate positive control.
Selection of hits for secondary screen validation and the type of technology to employ is another matter that requires careful deliberation. Candidates are generally selected for secondary screen validation based on the magnitude of the hit, on whether they significantly modulate spread in multiple cancer cell lines (if primary screens were conducted in multiple cell lines) and on biological interest. Bioinformatics tools such as DAVID22,23, PANTHER24, STRING25 and PINdb26 can help to isolate pathways and hits of biological interest. Mercer et al.8 describe the use of open-source image analysis software and have also made available an algorithm that they developed for the visualization and analysis of hits. One factor to take into account when interpreting results is that gene silencing may have different impacts depending on the stage in the virus life cycle that is being investigated. For example, it is possible that knockdown of a gene early in the life cycle may inhibit virus replication, while knockdown at a later stage may increase replication. Often, secondary screens are conducted with siRNA from a different vendor with different seed regions with multiple siRNAs per gene. However, complementary technologies targeting candidate genes can be employed such as CRISPR-Cas9 or lentivirus vectors expressing shRNA.
The method of analysis is also a critical consideration. We suggest here using the robust Z-score or MAD20,27 with suggested cut-offs for hit calling of &#62;2 or &#60;-2. The cut-offs can be increased or decreased depending on whether the focus is on eliminating more false positives or capturing more false negatives. For instance, in a recent high-throughput screen with vaccinia virus9, the lower cut-off was set at -1.5 (no upper cut-offs were described as the focus was on identifying genes that decreased spread upon knockdown). There are also other methods that can be employed including the z-score, SSMD (strictly standardized mean difference), and the B score20,28,29,30. Barrows et al.31 compared hit lists generated by sum rank, MAD, z-score and SSMD and found each method of analysis resulted in different hit lists. In toto, there is no perfect method or cut-off. Ultimately, secondary screen validation and tertiary validation studies will confirm true hits.
The method of filtering out cytotoxic hits must also be considered. One way is to conduct primary screens in parallel plus or minus virus. This permits the detection of siRNAs that are cytotoxic on their own. This was our approach in our Maraba virus screens15; however, this is not often practical. Perhaps a more practical method, in addition to being robust, is to ascertain hits that are cytotoxic as part of secondary screen validation, especially if one&#39;s focus is to identify hits that decrease replication upon knockdown. For instance, in a whole genome primary screen with Myxoma virus, Teferi et al.11 identified 1,588 siRNAs that decreased virus replication upon knockdown. To filter out cytotoxic siRNAs, they conducted a secondary cytotoxicity screen with these siRNAs; they measured cell viability 72 h post-transfection using a resazurin-based cell viability assay. Cytotoxic siRNAs were identified based on a Z-score of &#8804; -1.96. Another approach is to simply filter out cytotoxic siRNAs from the primary screening data based on a reduction of cell number by a certain percentage, for example, by a reduction &#62; 50% as in Sivan et al.9, or based on a particular score as in Lee et al.14; in their study of host factors required for vesicular stomatitis virus replication, they excluded siRNA hits that reduced cell viability by &#62; 3.0 standard deviations from the mean. The mean number of cells was determined based on the Hoechst staining of cell nuclei and, although not explicitly stated, the mean was seemingly calculated on a per plate basis as it is clear that the mean GFP signal was calculated on a per plate basis.
A limitation of any high-throughput RNAi screen is the frequent high number of false positives and negatives, which may come as a consequence of the assay design, the technology used or through systematic errors. For example, a protein may significantly affect the replication of the virus, but the time chosen for gene silencing may be too short given the protein&#39;s half-life to detect its effect leading to a false negative by virtue of the assay design. It is well established that incomplete knockdown and the off-target effects of siRNA technology can also introduce false positives and negatives. Systematic errors, such as the edge effect32, can generate misleading results as well. Here the signal in the outer wells of the plate may be systematically higher or lower than the rest of the plate due to evaporation. There are strategies to address some of these issues including, for instance: decreasing the concentration of siRNA to reduce off-target effects33, reconfiguring plate layouts to fill the outer wells with media to mitigate the edge effect, or employing computational methods that have been developed to detect and counter systematic errors such as the edge effect32,34,35. A general method to deal with false positives is to conduct a secondary screen following the primary screen. As a way of dealing with false negatives, one may relax the cut-off for hit calling, and include potential false negatives for secondary screening. This, of course, will not capture false negatives that are well below the cut-off. Primary screens should also be conducted in duplicate or triplicate to limit spurious results. Ultimately, no matter what strategies are employed, no high-throughput screen will exhaustively identify all true hits, but it can provide a treasure trove of data from which one is likely to mine some valuable hits that can be capitalized upon.
In this protocol, we described the use of arrayed siRNA and shRNA technology, though another alternative is the use of pooled shRNA and CRISPR-Cas9 libraries. A pooled shRNA library was employed in the Workenhe et al.12 study outlined in the introduction. Pooled CRISPR-Cas9 technology has been used to conduct genome-scale screens to identify host factors required for replication of viruses such as Zika virus36, West Nile virus37,38, dengue virus39 and hepatitis C virus39. The advantage of using pooled libraries is that they are less expensive to purchase, do not require specialized infrastructure (e.g. liquid handling devices, high-content microscopes, and robotic equipment), nor do they have the high costs of operating and maintaining this infrastructure, and they require fewer consumables. The disadvantage is that the types of readouts are more limited. In most if not all pooled library host-virus interaction screens to date, the readout is cell viability or lack thereof; one cannot readily probe for more complex phenotypes. The choice of format depends on the biological question being asked.
The advancements in genetic screening technology over the past several decades that first enabled screens in bacteria and yeast to now present-day genome-scale screens in human cells have opened the door wide for discovery in diverse fields of study. These genetic screens have not only allowed us to answer fundamental biology questions, but have given us keys to unlocking insights into developing and improving biotherapeutics. While there are still lessons to be learned and challenges to be overcome with this technology, the results to date have been exciting. The discoveries will likely be even greater as novel screening technology including CRISPR-Cas9 libraries, microfluidics and in vivo screening technologies continue to mature.

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. Figure 2B consists of representative images of expected results, and Figure 2C is a representative plot of the expected results. Following siRNA knockdown of PLK-1, a gene that induces cell death and can be used as a positive control to monitor siRNA knockdown efficiency, one would expect the cell survival to be between 0-30%, unless the cell line has a long doubling time in which case it will take longer to observe the cell death phenotype. The non-targeting siRNA should also be minimally toxic to the cells with a similar relative survival to that of untreated cells.
Another key element of assay development is to determine the MOI at which to infect the cells and the length of time for infection. Figure 3A provides a sample plate layout for determining the amount of virus and the length of incubation with virus. Figure 3B depicts the results of a live time-course of 786-O cells infected with vvDD-eGFP (oncolytic vaccinia virus expressing enhanced GFP with the Thymidine Kinase and Vaccinia Growth Factor genes deleted) at various MOIs. Representative images of cells infected with vvDD-eGFP at an MOI of 0.05 are shown in Figure 3C. Based on the results plotted in Figure 3B, one might select an MOI of 0.05 and a length of infection of 21 h as this would allow for the detection of increases and decreases in spread as this time-point is within a linear range, and the estimated Z-factor at this time-point is 0.6 for cohorts that increase and for cohorts that decrease spread. As part of validating this assay, one will want to fix the cells at this MOI and time-point, and calculate the Z-factor.
Following the protocol described, we conducted genome-wide RNAi screens across three different tumor cell lines (e.g. OVCAR-8, U373, NCI-H226) in duplicate with an siRNA library targeting 18,120 genes15. Using the MAD method, we identified 1008 hits common to at least 2 out of the 3 cancer cell lines (Figure 4A). Pathway analysis of hits identified in the screens revealed enrichment of members of the UPR (unfolded protein response) and ERAD (Endoplasmic-reticulum-associated protein degradation) pathways (Figure 4A). We selected 10 hits within these pathways for secondary validation using siRNA with different targeting sequences from those of the primary screen (Figure 4B). As part of tertiary validation, we conducted a genetic rescue experiment with IRE1&#945; and ATF6&#945; (activating transcription factor 6 alpha) to confirm that these hits were on target (Figure 4C).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56913/56913fig1.jpg" /> 
Figure 1: Schematic of workflow for identifying host targets to enhance oncolytic virus therapy. The first step is to develop and validate an assay for high-throughput RNAi screening, which includes optimizing transfection conditions for introducing siRNA into cells, determining the optimal amount of virus (i.e. MOI) to infect the cells with and the length of incubation with virus. The second step is to conduct a high-throughput genome-wide screen. An siRNA library targeting genes across the entire genome is arrayed on microtiter plates. Cells are then reverse transfected with the siRNA library. Following a 48-72 h incubation period to allow for gene silencing, cells are infected with the optimal amount of virus. After the optimal length of incubation with virus, cells are fixed and stained, and subsequently imaged with a high-content microscope. Subsequent image and data analysis will help to identify genes that significantly modulate virus replication, which are referred to as &#34;hits.&#34; A secondary validation screen is performed on select hits from the primary screen generally using siRNA or shRNA with different seed regions. Tertiary validation experiments are performed to confirm that the hits are on-target, tumor specific and enhance efficacy in vivo. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56913/56913fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56913/56913fig2.jpg" /> 
Figure 2: Optimization of transfection conditions for high-throughput RNAi screening assay. (A) A sample plate layout for optimizing transfection conditions with two different cell lines. (B) Representative images of 786-O cells (1500 cells/well) stained with Hoechst 33342 following 72 h incubation with transfection reagent diluent alone (i.e. untreated), with transfection reagent and diluent (i.e. mock transfected), with non-targeting siRNA and with siRNA targeting PLK-1. (C) Representative results from transfection optimization experiment showing 22% survival of cells transfected with PLK-1 siRNA (n=24) and 94% survival of cells transfected with non-targeting siRNA (n=8). Error bars = &#177; SD, standard deviation; scale bars = 200 &#956;m. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56913/56913fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56913/56913fig3.jpg" /> 
Figure 3: Determination of the amount of virus and length of incubation with virus for high-throughput RNAi screening assay. (A) A sample plate layout for optimizing the amount of virus and length of incubation with virus. Columns 1 and 24 contain transfection controls and are left uninfected. Columns 2 to 23 contain untreated cells, mock transfected cells and cells transfected with positive and negative virus controls all infected with a range of MOIs starting with no virus in columns 2 and 3. (B) 786-O cells were reverse transfected with non-targeting siRNA and incubated for 48 h. They were subsequently infected with vvDD-GFP at the MOIs indicated and imaged at 8 h intervals starting at 5 hours post-infection (hpi). The mean GFP area per well (&#177; SD) was calculated for each time-point (n=12) and plotted on the graph. The Z-factor calculated between points A and B is 0.6 and the Z-factor calculated between points B and C is 0.6. (C) Representative live images of a well infected with an MOI of 0.05 at the time-points indicated. Magnification, 10x; 9 fields/well; scale bars = 200 &#956;m. <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56913/56913fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56913/56913fig4.jpg" /> 
Figure 4: Genome-wide Screen Identifies ER Stress Response Blockade as a Potent Sensitizer of Rhabdovirus-Mediated Oncolysis. (A) Venn diagram outlining the number of overlapping hits of high-throughput RNAi screens in 3 tumor cell lines, and a table (+, synthetic lethal; -, no interaction) and schematic diagram (hits outlined in red) illustrating key hits within the UPR and ERAD pathways. (B) EC50 shifts (black bars, left y axis) were determined for U373 cells treated with Maraba virus (48 h) following treatment with siRNA (72 h) targeting a series of UPR/ERAD hits from the screen. Relative mRNA expression for each gene following siRNA knockdown (72 h) is depicted in white (right y axis). (C) Cell viability assays were conducted 48 h after Maraba virus infection, in U373 cells ectopically expressing mouse ATF6&#945; (or control GFP) &#177; siRNA targeting human ATF6&#945; (or non-targeting [NT] control; left panel) or human XBP1(s) (or control) &#177; siRNA targeting human IRE1&#945; (or control; right panel). Western blots demonstrating gene silencing and ectopic gene expression are shown. EC50 shifts = the shift in the dose of virus required to kill 50% of the cells; Error bars = &#177;SD; XBP1(s) = X-box binding protein 1 (spliced); In (B), one-way ANOVAs were performed followed by a Bonferroni multiple comparison&#39;s post hoc test to derive p values; In (C), student&#39;s t-tests were performed to derive p values; *p &#60; 0.05; #p &#60; 0.05. (This figure has been modified from Mahoney et al., 201115). <a href="https://cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56913/56913fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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

Nanyang Technological University

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 ...