Name: a data integration workflow to identify drug combinations targeting synthetic lethal interactions
Uploaded: 2021-05-27T13:00:00
Description: Watch this Scientific Journal Video about A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions at JoVE.com

Funding for developing the data integration workflow was obtained from European Community&#8217;s Seventh Framework Programme under grant agreement nu. 279113 (OCTIPS). Adaption of data within this publication was kindly approved by Public Library of Sciences Publications and Impact Journals, LLC.

1. Retrieving synthetic lethal gene pairs
<ol>
	<li>Data retrieval from BioGrid.
	<ol>
		<li>Download the latest BioGRID interaction file in tab2 format from https://downloads.thebiogrid.org/Download/BioGRID/Latest-Release/BIOGRID-ALL-LATEST.tab2.zip either using a web browser or directly from the Linux command line using curl or wget18. 
		 
		##download and unpack the latest BioGRID interaction file 
		#download latest BioGRID interaction file using curl 
		curl -o ...

<ol>
	<li>Dobzhansky, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genetics+of+natural+populations;+recombination+and+variability+in+populations+of+Drosophila+pseudoobscura.">Genetics of natural populations; recombination and variability in populations of Drosophila pseudoobscura.</a> Genetics. 31, 269-290 (1946).</li><li>Hartwell, L. H., Szankasi, P., Roberts, C. J., Murray, A. W., Friend, S. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrating+genetic+approaches+into+the+discovery+of+anticancer+drugs.">Integrating genetic approaches into the discovery of anticancer drugs.</a> Science. 278 (5340), 1064-1068 (1997).</li><li>D'Amours, D., Desnoyers, S., D'Silva, I., Poirier, G. 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E., Hurvitz, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advances+in+the+use+of+PARP+inhibitor+therapy+for+breast+cancer.">Advances in the use of PARP inhibitor therapy for breast cancer.</a> Drugs in Context. 7, 212540 (2018).</li><li>Franzese, 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=PARP+inhibitors+in+ovarian+cancer.">PARP inhibitors in ovarian cancer.</a> Cancer Treatment Reviews. 73, 1-9 (2019).</li><li>Ashworth, A., Lord, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synthetic+lethal+therapies+for+cancer:+what&#8217;s+next+after+PARP+inhibitors.">Synthetic lethal therapies for cancer: what&#8217;s next after PARP inhibitors.</a> Nature Reviews. Clinical Oncology. 15 (9), 564-576 (2018).</li><li>Yu, B., Luo, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synthetic+lethal+genetic+screens+in+Ras+mutant+cancers.">Synthetic lethal genetic screens in Ras mutant cancers.</a> The Enzymes. 34, 201-219 (2013).</li><li>Thompson, J. M., Nguyen, Q. H., Singh, M., Razorenova, O. 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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=Mistaken+identifiers:+gene+name+errors+can+be+introduced+inadvertently+when+using+Excel+in+bioinformatics.">Mistaken identifiers: gene name errors can be introduced inadvertently when using Excel in bioinformatics.</a> BMC bioinformatics. 5, 80 (2004).</li><li>Ziemann, M., Eren, Y., Gene El-Osta, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=name+errors+are+widespread+in+the+scientific+literature.">name errors are widespread in the scientific literature.</a> Genome Biology. 17 (1), 177 (2016).</li><li>Kersey, P. 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=Ensembl+Genomes+2018:+an+integrated+omics+infrastructure+for+non-vertebrate+species.">Ensembl Genomes 2018: an integrated omics infrastructure for non-vertebrate species.</a> Nucleic Acids Research. 46, 802-808 (2018).</li><li>Wishart, D. 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=DrugBank+5.0:+a+major+update+to+the+DrugBank+database+for+2018.">DrugBank 5.0: a major update to the DrugBank database for 2018.</a> Nucleic Acids Research. 46, 1074-1082 (2018).</li><li>Chou, T. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Drug+combination+studies+and+their+synergy+quantification+using+the+Chou-Talalay+method.">Drug combination studies and their synergy quantification using the Chou-Talalay method.</a> Cancer Research. 70 (2), 440-446 (2010).</li><li>Marhold, 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=Synthetic+lethal+combinations+of+low-toxicity+drugs+for+breast+cancer+identified+in+silico+by+genetic+screens+in+yeast.">Synthetic lethal combinations of low-toxicity drugs for breast cancer identified in silico by genetic screens in yeast.</a> Oncotarget. 9 (91), 36379-36391 (2018).</li><li>Heinzel, 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=Synthetic+lethality+guiding+selection+of+drug+combinations+in+ovarian+cancer.">Synthetic lethality guiding selection of drug combinations in ovarian cancer.</a> PloS One. 14 (1), 0210859 (2019).</li><li>Costanzo, 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=The+genetic+landscape+of+a+cell.">The genetic landscape of a cell.</a> Science. 327 (5964), 425-431 (2010).</li><li>Koh, J. 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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=DRYGIN:+a+database+of+quantitative+genetic+interaction+networks+in+yeast.">DRYGIN: a database of quantitative genetic interaction networks in yeast.</a> Nucleic Acids Research. 38, 502-507 (2010).</li><li>Guo, J., Liu, H., Zheng, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SynLethDB:+synthetic+lethality+database+toward+discovery+of+selective+and+sensitive+anticancer+drug+targets.">SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets.</a> Nucleic Acids Research. 44, 1011-1017 (2016).</li><li>NCBI Resource Coordinators. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Database+resources+of+the+National+Center+for+Biotechnology+Information.">Database resources of the National Center for Biotechnology Information.</a> Nucleic Acids Research. 44 (1), 7-19 (2016).</li><li>Altenhoff, A. 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We have outlined a workflow to identify drug combinations impacting synthetic lethal interactions. This workflow makes use of (i) data on synthetic lethal interactions from model organisms, (ii) information of human orthologs, (iii) information on drug-target associations, (iv) drug information on clinical trials in the context of cancer, as well as (v) on information of drug-disease and gene-disease associations extracted from scientific literature. The consolidated information can be used to evaluate the impact of a gi...

Synthetic lethality defines an association of two genes, where loss of one gene does not affect viability, but loss of both genes leads to cell death. It was first described in 1946 by Dobzhansky while analyzing various phenotypes of drosophila by breeding homozygous mutants1. Mutants that did not produce viable offspring, although viable themselves, exhibited lethal phenotypes when crossed with certain other mutants, setting ground for the establishment of the theory of synthetic lethality. Hartwell and colleagues suggested that this concept might be applicable for cancer therapy in humans2. Pharmacologically provoked synthetic lethality could rely on just one mutation, given that the mutated gene&#8217;s synthetic lethal partner is targetable by a pharmacological compound. The first gene pair to enable pharmacological induction of synthetic lethality was BRCA(1/2) and PARP1. PARP1 functions as a sensor for DNA damage, and is tied to sites of double and single DNA strand-breaks, supercoils and crossovers3. BRCA1 and 2 play major roles in repair of DNA double-strand breaks through homologous recombination4. Farmer and colleagues published findings that cells deficient for BRCA1/2 were susceptible to PARP inhibition, while no cytotoxicity was observed in BRCA wild-type cells5. Ultimately, PARP inhibitors were approved for the treatment of BRCA deficient breast and ovarian cancer6,7. Further, synthetic lethality gene pairs leading to clinical approval of pharmacological compounds are much anticipated and a major area of recent cancer research efforts8.
Synthetic lethal gene interactions were modelled in multiple organisms including fruit flies, C. elegans and yeast2. Using various approaches including RNA-interference- and CRISPR/CAS-library knockouts, novel synthetic lethal gene pairs were discovered in recent years9,10,11. A protocol on the experimental procedures of RNAi in combination with CRISPR/CAS was recently published by Housden and colleagues12. Meanwhile, researchers also conducted large screens in haploid human cells to identify synthetic lethal interactions13,14. In silico methods like biological network analysis and machine learning have also shown promise in the discovery of synthetic lethal interactions15,16.
Conceptionally, one approach to make use of synthetic lethal interactions in the context of anti-tumor therapy is to identify mutated or non-functional proteins in tumor cells, making their synthetic lethal interaction partners promising drug targets for therapeutic intervention. Due to the heterogeneity of most tumor types, researchers have started the search for so-called synthetic lethal hub proteins. These synthetic lethal hubs have a number of synthetic lethal interaction partners that are either mutated and therefore non-functional or significantly downregulated in tumor samples. Addressing such synthetic lethal hubs holds promise in increasing drug efficacy or overcoming drug resistance as could be shown for instance in the context of vincristine resistant neuroblastoma17. A second approach to enhance drug treatment making use of the concept of synthetic lethal interactions is to identify drug combinations targeting synthetic lethal interactions. This could lead to new combinations of already approved single anti-tumor therapies and to the repositioning of drugs from other disease areas to the field of oncology.
In this article, we present a step-by-step procedure to yield a list of drug combinations that target synthetic lethal interaction pairs. In this workflow, we (i) use data on synthetic lethal interactions from BioGRID and (ii) information on homologous genes from Ensembl, (iii) retrieve drug-target pairs from DrugBank, (iv) build disease-drug associations from ClinicalTrials.gov, and (v) hence generate a set of drug combinations addressing synthetic lethal interactions. Lastly, we provide drug combinations in the context of ovarian and breast cancer in the representative results section.

<table>
	<tbody>
		<tr>
			<td>BioGRID</td>
			<td>n/a</td>
			<td>n/a</td>
			<td>thebiogrid.org</td>
		</tr>
		<tr>
			<td>ClinicalTrials.gov</td>
			<td>n/a</td>
			<td>n/a</td>
			<td>ClinicalTrials.gov</td>
		</tr>
		<tr>
			<td>DrugBank</td>
			<td>n/a</td>
			<td>n/a</td>
			<td>drugbank.ca</td>
		</tr>
		<tr>
			<td>Ensembl BioMart</td>
			<td>n/a</td>
			<td>n/a</td>
			<td>ensembl.org</td>
		</tr>
		<tr>
			<td colspan="4">for alternative computational databases please refer to the manuscript</td>
		</tr>
		<tr>
			<td>7-AAD</td>
			<td>ebioscience</td>
			<td>00-6993-50</td>
			<td></td>
		</tr>
		<tr>
			<td>AnnexinV-APC</td>
			<td>BD Bioscience</td>
			<td>550474</td>
			<td></td>
		</tr>
		<tr>
			<td>celecoxib</td>
			<td>Sigma-Aldrich</td>
			<td>PZ0008-25MG</td>
			<td></td>
		</tr>
		<tr>
			<td>CellTiter-Blue Viability Assay</td>
			<td>Promega</td>
			<td>G8080</td>
			<td></td>
		</tr>
		<tr>
			<td>FACS Canto II</td>
			<td>BD Bioscience</td>
			<td>n/a</td>
			<td></td>
		</tr>
		<tr>
			<td>fetal bovine serum</td>
			<td>Fisher Scientific/Gibco</td>
			<td>16000044</td>
			<td></td>
		</tr>
		<tr>
			<td>FloJo Software</td>
			<td>FloJo LLC</td>
			<td>V10</td>
			<td></td>
		</tr>
		<tr>
			<td>McCoy&#39;s 5a Medium Modified</td>
			<td>Fisher Scientific/Gibco</td>
			<td>16600082</td>
			<td></td>
		</tr>
		<tr>
			<td>penicillin G/streptomycin sulfate</td>
			<td>Fisher Scientific/Gibco</td>
			<td>15140122</td>
			<td></td>
		</tr>
		<tr>
			<td>SKBR-3 cells</td>
			<td>American Type Culture Collection (ATCC)</td>
			<td>ATCC HTB-30</td>
			<td></td>
		</tr>
		<tr>
			<td>zoledronic acid</td>
			<td>Sigma-Aldrich</td>
			<td>SML0223-50MG</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4">further materials or equipment will be made available upon request</td>
		</tr>
	</tbody>
</table>

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.

Our group has recently published two studies applying the workflow depicted in this manuscript to identify drug combinations targeting synthetic lethal interactions in the context of ovarian and breast cancer24,25. In the first study, we evaluated drug combinations that are currently tested in late stage clinical trials (phase III and IV) or already being used in clinical practice to treat ovarian cancer patients regarding their impact on synthetic lethal interac...

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

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 ...

A synthetic lethal interaction between two genes occurs when a single knockdown of either one does not affect cell viability but knockdown of both synthetic lethal partners leads to loss of cell viability or cell death. Combining heterogeneous datasets can lead to the identification of synthetic lethal interactions that can be targeted by drug combinations in diseases such as cancer, with the aim of halting cell proliferation. The concept of synthetical lethality is of interest for any disease in which cell proliferation is an issue.As for cancer, synthetic lethal gene interactions are already being targeted in BRCA-mutated tumors by using PARP inhibitors. Efforts such as ours could expand the use of the concept for other gene interactions and cancer entities. In our laboratory, we have tested computationally predicted drug combinations targeting synthetic lethal interactions in the context of breast cancer.Begin by retrieving data from BioGRID. Use the web browser to download the latest BioGRID interaction file in tab2 format. Filter the columns to only retain those relevant for subsequent analysis steps.Next, filter for synthetic lethality and negative genetic interactions. Use the information in the Experimental System column to restrict the dataset to entries with a value of either negative genetic or synthetic lethality. Identify the species for which synthetic lethal interactions were reported by determining the number of synthetic lethal interaction partner tax IDs, which will provide an estimate on the number of these interactions available per organism.To retrieve human orthologs for relevant model organisms from Ensembl BioMart, select the respective model organism dataset, click on Attributes, and select Gene name. Then click on Dataset, and select Human genes. Again, click on Attributes, and select Gene name.Then click Results. Check Unique results only, and click Go.Automatize the retrieval process, and send the query directly to BioMart RESTful access for retrieving the orthologous gene pairs. If retrieving the data manually via the Ensembl BioMart web interface, rename and note that header line was automatically added.In order to retrieve the orthologous human genes for other model organisms, replace the value of the name attribute of the first dataset element with the name of the respective Ensembl dataset. Next, discard any entries for genes with no homologs, collect all homologous mappings in a single file, and add dummy mappings for human genes. In addition, add artificial entries for human genes.Prepare the synlet file for subsequent joining by adding for each interaction partner a new column holding the combination of tax ID and gene symbol. Join synthetic lethal interactions based on organism tax ID and gene symbol with the retrieved orthologous pairs. Retrieve drug target pairs from DrugBank from the Downloads section, creating an account first if necessary.Restrict the DrugBank drug target file to relevant columns. Only retain entries for human molecular entities. Then, extract the relevant columns, and filter the data for human species.Since drug name and drug target information is provided in two separate CSV files, the information from the two files must be merged. In order to do so, the drug name entries must first be normalized. Then normalize the drug target file to have one line per drug.Continue with the DrugBank vocabulary file, and extract the relevant columns. Since drug name and drug target information are provided in two separate CSV files, merge the information from the two files using the common DrugBank ID.Join the synthetic lethal interaction dataset with the drug target drug name file generated in the previous step using the gene symbol columns, making sure to add drug names for both partners of each synthetic lethal interaction. Finally, retrieve information on clinical trials from ClinicalTrials.gov.For easy access, use the relational database provided by the Clinical Trials Transformation Initiative, creating an account first if necessary. Dissolve the proposed drugs in appropriate solvents, such as DMSO or PBS, in at least four different concentrations. Add the drugs to human breast cancer cells seeded in a 96-well plate, and incubate them for the desired amount of time to determine inhibitory concentration values.Use a viability or apoptosis assay of choice to determine cell viability with various drug combinations and concentrations, starting with the previously established IC50. Then, determine the synergistic cytotoxic effects of the drug combinations by calculating their combinatorial index. This method was used to identify drug combinations targeting synthetic lethal interactions in ovarian cancer.It was found that 21 unique drugs contributed to 84 identified drug combinations targeting a set of 39 synthetic lethal interactors. The same workflow was used to identify 243 promising drug combinations targeting 166 synthetic lethal gene pairs in the context of breast cancer. Select combinations were tested for their impact on cell viability and apoptosis in two breast cancer cell lines.Viability assay results for celecoxib, zoledronic acid, and the combination of zoledronic acid and celecoxib in SK-BR-3 breast cancer cell lines demonstrated that when combined the drugs had a significant synergistic effect on cell viability. Annexin V and 7-AAD staining of SK-BR-3 cells treated with the two drugs by themselves and combined showed that the percentage of late apoptotic and necrotic cells was increased after treatment with the drug combination. When attempting this protocol, take the time necessary to identify corresponding DrugBank drug names for all drugs and interventions retrieved from clinical trials.The inclusion of additional data such as gene expression profiles or the annotation degree in scientific literature of synthetic lethal interactors for the disease under study may further be used to prioritize synthetic lethal interactions. Due to the growing amount of available biomedical data, joining forces with computational biologists is a beneficial interaction leading to novel hypotheses to be tested in the lab. When working with cytostatic agents, make sure to follow your local guidelines dealing with laboratory safety equipment and the handling of hazardous reagents.At any time point, avoid direct contact, and make sure to gather information about the substance used prior to starting the experiment.

a-data-integration-workflow-to-identify-drug-combinations-targeting

Research

JoVE Journal

Cancer Research

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 ...

In Vitro Imaging and Quantification of the Drug Targeting Efficiency of Fluorescently Labeled GnRH Analogues

GnRH analogues are effective targeting moieties and able to deliver anticancer agents selectively into malignant tumor cells which highly express GnRH receptors. However, the quantitative analysis of GnRH analogues&#39; cellular uptake and the investigated cell types in GnRH-based drug delivery systems are currently limited. Previously introduced, selectively labeled fluorescent GnRH I, -II and -III derivatives provide great detectability, and they have suitable chemical properties for reproducible and robust experiments. We also found that the appropriate up-to-date methods with these labeled GnRH analogues could offer novel information about the GnRH-based drug delivery systems. This manuscript introduces some simple and fast experiments regarding the cellular uptake of [D-Lys6(FITC)]-GnRH-I, [D-Lys6(FITC)]-GnRH-II and [Lys8(FITC)]-GnRH-III on the EBC-1 (lung), the BxPC-3 (pancreas) and on the Detroit-562- (pharynx) malignant tumor cells. In parallel with these GnRH-FITC conjugates, the cell surface level of GnRH-I receptors was also examined on these cell lines before and after the GnRH treatment by confocal laser scanning microscopy. The cellular uptake of GnRH-FITC conjugates was quantified by fluorescence-activated cell sorting. In these experiments minor differences among GnRH analogues and major differences among cell types was observed. The significant differences among cell lines are correlated with their distinct level of cell surface GnRH-I receptors. The introduced experiments contain practical methods to visualize, quantify and compare the uptake efficiency of GnRH-FITC conjugates in a time- and concentration-dependent manner on various adherent cell cultures. These results could predict the drug targeting efficiency of GnRH conjugates on the given cell culture, and offer a good basis for further experiments in the examination of GnRH-based drug delivery systems.

In Vitro Imaging and Quantification of the Drug Targeting Efficiency of Fluorescently Labeled GnRH Analogues

Selectively labeled fluorescent GnRH-I, -II and -III derivatives are reliable tools for tracking and quantifying their cellular uptake. This manuscript introduces experiments to visualize, quantify and compare the uptake efficiency of these GnRH conjugates in various cell lines.

GnRH analogues are effective targeting moieties and able to deliver anticancer agents selectively into malignant tumor cells which highly express GnRH ...

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

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

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

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

A GPC3-targeting Bispecific Antibody, GPC3-S-Fab, with Potent Cytotoxicity

This protocol describes the construction and functional studies of a bispecific antibody (bsAb), GPC3-S-Fab. bsAbs can recognize two different epitopes through their two different arms. bsAbs have been actively studied for their ability to directly recruit immune cells to kill tumor cells. Currently, the majority of bsAbs are produced in the form of recombinant proteins, either as Fc-containing bsAbs or as smaller bsAb derivatives without the Fc region. In this study, GPC3-S-Fab, an antibody fragment (Fab) based bispecific antibody, was designed by linking the Fab of anti-GPC3 antibody GC33 with an anti-CD16 single domain antibody. The GPC3-S-Fab can be expressed in Escherichia coli and purified by two affinity chromatographies. The purified GPC3-S-Fab can specifically bind to and kill GPC3 positive liver cancer cells by recruiting natural killer cells, suggesting a potential application of GPC3-S-Fab in liver cancer therapy.

Here, we present a protocol to produce a bispecific antibody GPC3-S-Fab in Escherichia coli. The purified GPC3-S-Fab has potent cytotoxicity against GPC3 positive liver cancer cells.

This protocol describes the construction and functional studies of a bispecific antibody (bsAb), GPC3-S-Fab. bsAbs can recognize two different epitopes ...

Synthesis and Characterization of Placental Chondroitin Sulfate A (plCSA)-Targeting Lipid-Polymer Nanoparticles

An effective cancer therapeutic method reduces and eliminates tumors with minimal systemic toxicity. Actively targeting nanoparticles offer a promising approach to cancer therapy. The glycosaminoglycan placental chondroitin sulfate A (plCSA) is expressed on a wide range of cancer cells and placental trophoblasts, and malarial protein VAR2CSA can specifically bind to plCSA. A reported placental chondroitin sulfate A binding peptide (plCSA-BP), derived from malarial protein VAR2CSA, can also specifically bind to plCSA on cancer cells and placental trophoblasts. Hence, plCSA-BP-conjugated nanoparticles could be used as a tool for targeted drug delivery to human cancers and placental trophoblasts. In this protocol, we describe a method to synthesize plCSA-BP-conjugated lipid-polymer nanoparticles loaded with doxorubicin (plCSA-DNPs); the method consists of a single sonication step and bioconjugate techniques. In addition, several methods for characterizing plCSA-DNPs, including determining their physicochemical properties and cellular uptake by placental choriocarcinoma (JEG3) cells, are described.

Synthesis and Characterization of Placental Chondroitin Sulfate A plCSA-Targeting Lipid-Polymer Nanoparticles

Here, we present a protocol for the synthesis of placental chondroitin sulfate A binding peptide (plCSA-BP)-conjugated lipid-polymer nanoparticles via single-step sonication and bioconjugate techniques. These particles constitute a novel tool for the targeted delivery of therapeutics to most human tumors and placental trophoblasts to treat cancers and placental disorders.

An effective cancer therapeutic method reduces and eliminates tumors with minimal systemic toxicity. Actively targeting nanoparticles offer a promising ...

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 Simple Migration/Invasion Workflow Using an Automated Live-cell Imager

Cancer cell mobility is crucial for the initiation of metastasis. Therefore, investigation of the cell movement and invasive capacity is of great significance. Migration assays provide basic insight of cell movement at a 2D level, whereas invasion assays are more physiologically relevant, mimicking in vivo cancer cell dislodgment from the original site and invading through the extracellular matrix. The current protocol provides a single workflow for migration and invasion assays. Together with the integrated automated microscopic camera for real-time HD images and built-in analysis module, it gives researchers a time-efficient, simple and reproducible experimental option. This protocol also includes substitutions for the consumables and alternative analysis methods for users to choose from.

The current protocol describes an integrated method investigating cancer cell migration and invasion on a single platform in real-time, providing an easily reproducible and time-efficient option to study cell mobility and morphology.

Cancer cell mobility is crucial for the initiation of metastasis. Therefore, investigation of the cell movement and invasive capacity is of great ...

Establishing a Competing Risk Regression Nomogram Model for Survival Data

The Kaplan&#8211;Meier method and Cox proportional hazards regression model are the most common analyses in the survival framework. These are relatively easy to apply and interpret and can be depicted visually. However, when competing events (e.g., cardiovascular and cerebrovascular accidents, treatment-related deaths, traffic accidents) are present, the standard survival methods should be applied with caution, and real-world data cannot be correctly interpreted. It may be desirable to distinguish different kinds of events that may lead to the failure and treat them differently in the analysis. Here, the methods focus on using the competing regression model to identify significant prognostic factors or risk factors when competing events are present. Additionally, nomograms based on a proportional hazard regression model and a competing regression model are established to help clinicians make individual assessments and risk stratifications in order to explain the impact of controversial factors on prognosis.

Presented here is a protocol to build nomograms based on the Cox proportional hazards regression model and competing risk regression model. The competing method is a more rational method to apply when competing events are present in the survival analysis.

The Kaplan&#8211;Meier method and Cox proportional hazards regression model are the most common analyses in the survival framework. These are relatively ...

In Vivo Targeting of Xenografted Human Cancer Cells with Functionalized Fluorescent Silica Nanoparticles in Zebrafish

Developing nanoparticles capable of detecting, targeting, and destroying cancer cells is of great interest in the field of nanomedicine. In vivo animal models are required for bridging the nanotechnology to its biomedical application. The mouse represents the traditional animal model for preclinical testing; however, mice are relatively expensive to keep and have long experimental cycles due to the limited progeny from each mother. The zebrafish has emerged as a powerful model system for developmental and biomedical research, including cancer research. In particular, due to its optical transparency and rapid development, zebrafish embryos are well suited for real-time in vivo monitoring of the behavior of cancer cells and their interactions with their microenvironment. This method was developed to sequentially introduce human cancer cells and functionalized nanoparticles in transparent Casper zebrafish embryos and monitor in vivo recognition and targeting of the cancer cells by nanoparticles in real time. This optimized protocol shows that fluorescently labeled nanoparticles, which are functionalized with folate groups, can specifically recognize and target metastatic human cervical epithelial cancer cells labeled with a different fluorochrome. The recognition and targeting process can occur as early as 30 min postinjection of the nanoparticles tested. The whole experiment only requires the breeding of a few pairs of adult fish and takes less than 4 days to complete. Moreover, zebrafish embryos lack a functional adaptive immune system, allowing the engraftment of a wide range of human cancer cells. Hence, the utility of the protocol described here enables the testing of nanoparticles on various types of human cancer cells, facilitating the selection of optimal nanoparticles in each specific cancer context for future testing in mammals and the clinic.

Described here is a method for utilizing zebrafish embryos to study the ability of functionalized nanoparticles to target human cancer cells in vivo. This method allows for the evaluation and selection of optimal nanoparticles for future testing in large animals and in clinical trials.

Developing nanoparticles capable of detecting, targeting, and destroying cancer cells is of great interest in the field of nanomedicine. In vivo animal ...

A Biomimetic Model for Liver Cancer to Study Tumor-Stroma Interactions in a 3D Environment with Tunable Bio-Physical Properties

Hepatocellular carcinoma (HCC) is a primary liver tumor developing in the wake of chronic liver disease. Chronic liver disease and inflammation leads to a fibrotic environment actively supporting and driving hepatocarcinogenesis. Insight into hepatocarcinogenesis in terms of the interplay between the tumor stroma micro-environment and tumor cells is thus of considerable importance. Three-dimensional (3D) cell culture models are proposed as the missing link between current in vitro 2D cell culture models and in vivo animal models. Our aim was to design a novel 3D biomimetic HCC model with accompanying fibrotic stromal compartment and vasculature. Physiologically relevant hydrogels such as collagen and fibrinogen were incorporated to mimic the bio-physical properties of the tumor ECM. In this model LX2 and HepG2 cells embedded in a hydrogel matrix were seeded onto the inverted transmembrane insert. HUVEC cells were then seeded onto the opposite side of the membrane. Three formulations consisting of ECM-hydrogels embedded with cells were prepared and the bio-physical properties were determined by rheology. Cell viability was determined by a cell viability assay over 21 days. The effect of the chemotherapeutic drug doxorubicin was evaluated in both 2D co-culture and our 3D model for a period of 72h. Rheology results show that bio-physical properties of a fibrotic, cirrhotic and HCC liver can be successfully mimicked. Overall, results indicate that this 3D model is more representative of the&#160;in vivo&#160;situation compared to traditional 2D cultures.&#160;Our 3D tumor model showed a decreased response to chemotherapeutics, mimicking drug resistance typically seen in HCC patients.&#160;

This protocol presents a 3D biomimetic model with accompanying fibrotic stromal compartment. Prepared with physiologically relevant hydrogels in ratios mimicking the bio-physical properties of the stromal extracellular matrix, an active mediator of cellular interactions, tumor growth and metastasis.

Hepatocellular carcinoma (HCC) is a primary liver tumor developing in the wake of chronic liver disease. Chronic liver disease and inflammation leads to a ...