Cancer Research
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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Chapters
Summary May 27th, 2021
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.
Transcript
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.
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