A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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.
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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Overview
This study explores synthetic lethal interactions between genes, particularly in the context of cancer treatment. By integrating data from genetic screens in model organisms, the research aims to identify effective drug combinations that can target these interactions.
Key Study Components
Area of Science
- Genetics
- Cancer Biology
- Pharmacology
Background
- Synthetic lethality occurs when the simultaneous knockdown of two genes leads to cell death, while knockdown of either gene alone does not.
- This concept is particularly relevant in cancer, where certain gene interactions can be exploited for therapeutic purposes.
- Current therapies, such as PARP inhibitors, target synthetic lethal interactions in BRCA-mutated tumors.
- Combining heterogeneous datasets can enhance the identification of potential drug combinations.
Purpose of Study
- To delineate drug combinations that target synthetic lethal interactions in cancer.
- To expand the application of synthetic lethality beyond BRCA mutations to other gene interactions and cancer types.
- To test computationally predicted drug combinations in the context of breast cancer.
Methods Used
- Data integration workflow from genetic screens in model organisms.
- Computational prediction of drug combinations.
- Experimental validation in laboratory settings.
- Analysis of cell viability in response to drug combinations.
Main Results
- Identification of novel synthetic lethal interactions that can be targeted for cancer therapy.
- Successful testing of predicted drug combinations in breast cancer models.
- Demonstration of the potential for expanding synthetic lethality applications in oncology.
- Insights into the mechanisms of cell death associated with these interactions.
Conclusions
- The study provides a framework for identifying drug combinations that exploit synthetic lethality.
- Findings could lead to new therapeutic strategies for various cancer types.
- Further research is needed to validate these interactions in clinical settings.
Frequently Asked Questions
Executive Industry Relevance
Integrating heterogeneous genetic and pharmacological datasets enables systematic identification of drug combinations that exploit synthetic lethal interactions, directly addressing the challenge of target validation and mechanistic de-risking in oncology discovery pipelines. This workflow enhances predictive confidence for prioritizing drug pairs with synergistic cytotoxicity, supporting risk-adjusted advancement decisions in early discovery and preclinical oncology portfolios.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables hypothesis-driven identification of synthetic lethal gene pairs relevant to disease models.
- Supports functional target validation by linking genetic interactions to actionable drug targets.
- Facilitates mechanistic de-risking by integrating ortholog mapping and literature annotation.
- Improves predictive confidence for triaging candidate drug combinations.
Screening & Assay Development
- Prepares validated gene-drug interaction datasets for downstream compound screening.
- Standardizes assay development by leveraging curated drug-target and clinical trial data.
- Enables reproducible evaluation of drug synergy using quantitative viability and apoptosis assays.
- Supports scalable screening of drug pairs across multiple cancer cell lines.
Translational & Preclinical Research
- Aligns candidate drug combinations with disease-relevant genetic backgrounds, such as BRCA-mutated tumors.
- Ensures continuity from computational prediction to in vitro validation in preclinical models.
- Enables risk-adjusted selection of drug pairs for further translational investigation.
- Supports biomarker-driven prioritization when gene expression or literature annotation is integrated.
Pipeline & Workflow Integration
This data integration workflow bridges early discovery, target validation, and preclinical evaluation by systematically linking genetic interaction data, ortholog mapping, drug-target associations, and clinical trial information.
- Discovery Biology: Supports hypothesis testing and pathway clarification by mapping synthetic lethal pairs to human orthologs.
- Screening: Provides assay-ready drug-gene interaction lists for reproducible compound evaluation.
- Analytics: Delivers quantitative outputs such as combinatorial index and viability metrics for condition comparison.
- Translational Research: Connects in silico predictions to in vitro validation in disease-relevant systems.
- Enterprise Reuse: Establishes a scalable, reusable workflow for ongoing drug combination discovery across oncology indications.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in drug combination selection.
- Operational Value: Standardizes data integration and assay workflows for reproducibility and scalability.
- Strategic Value: Enables informed go/no-go decisions and capital-efficient portfolio advancement.
- Portfolio Impact: Supports risk-adjusted prioritization of drug combinations for preclinical and translational progression.
Implementation Considerations
- Requires expertise in computational biology, data integration, and oncology pharmacology.
- Needs access to curated genetic interaction, ortholog mapping, drug-target, and clinical trial databases.
- Demands cross-team standardization of data formats and nomenclature for seamless integration.
- Must adapt workflow for different model organisms and cancer types as supported by available data.
- Dependent on the completeness and annotation quality of public datasets for prioritization accuracy.
Biopharma FAQs
Chapters in this video
0:04
Introduction
1:03
Retrieving Synthetic Lethal Gene Pairs
1:48
Translating Synthetic Lethal Gene Pairs to Human Orthologs
3:20
Mapping Synthetic Lethal Interaction Partners to Drugs
4:28
Establish the Set of Currently Tested Drug Combinations in Clinical Trials
4:45
Testing Proposed Drug Combinations In Vitro
5:28
Results: Proposed Novel Drug Combinations for Ovarian and Breast Cancer
6:37
Conclusion
