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Updated: Mar 31, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets
Jing Guo1, Hui Liu2, Jie Zheng3
1School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore.
Abstract:
Synthetic lethality (SL) is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. SL reflects the biologically endogenous difference between cancer cells and normal cells, and thus the inhibition of SL partners of genes with cancer-specific mutations could selectively kill cancer cells but spare normal cells. Therefore, SL is emerging as a promising anticancer strategy that could potentially overcome the drawbacks of traditional chemotherapies by reducing severe side effects. Researchers have developed experimental technologies and computational prediction methods to identify SL gene pairs on human and a few model species. However, there has not been a comprehensive database dedicated to collecting SL pairs and related knowledge. In this paper, we propose a comprehensive database, SynLethDB (http://histone.sce.ntu.edu.sg/SynLethDB/), which contains SL pairs collected from biochemical assays, other related databases, computational predictions and text mining results on human and four model species, i.e. mouse, fruit fly, worm and yeast. For each SL pair, a confidence score was calculated by integrating individual scores derived from different evidence sources. We also developed a statistical analysis module to estimate the druggability and sensitivity of cancer cells upon drug treatments targeting human SL partners, based on large-scale genomic data, gene expression profiles and drug sensitivity profiles on more than 1000 cancer cell lines. To help users access and mine the wealth of the data, we developed other practical functionalities, such as search and filtering, orthology search, gene set enrichment analysis. Furthermore, a user-friendly web interface has been implemented to facilitate data analysis and interpretation. With the integrated data sets and analytics functionalities, SynLethDB would be a useful resource for biomedical research community and pharmaceutical industry.
Insights
Synthetic lethality (SL) exploits gene interactions to selectively kill cancer cells. A new database, SynLethDB, consolidates SL gene pairs and aids in developing targeted cancer therapies with reduced side effects.
Area of Science:
- Genetics
- Genomics
- Bioinformatics
Background:
- Synthetic lethality (SL) describes genetic interactions where simultaneous gene perturbations cause cell death, offering a targeted cancer therapy approach.
- SL exploits cancer-specific genetic differences to selectively eliminate malignant cells while sparing normal tissues, potentially reducing chemotherapy side effects.
- Existing methods for identifying SL gene pairs are fragmented, necessitating a centralized resource.
Purpose of the Study:
- To introduce SynLethDB, a comprehensive database for synthetic lethality (SL) gene pairs and associated knowledge.
- To integrate SL data from diverse sources including experimental assays, databases, computational predictions, and text mining.
- To provide analytical tools for assessing the druggability and cancer cell sensitivity of SL partners.
Main Methods:
- Collected and curated SL gene pairs from human and four model organisms (mouse, fruit fly, worm, yeast).
- Developed a confidence scoring system integrating multiple evidence sources for each SL pair.
- Integrated large-scale genomic, gene expression, and drug sensitivity data for over 1000 cancer cell lines.
- Implemented functionalities for data retrieval, orthology search, gene set enrichment analysis, and a user-friendly web interface.
Main Results:
- SynLethDB provides a unified repository of experimentally validated and computationally predicted SL gene pairs.
- A confidence score quantifies the reliability of each identified SL pair.
- The database includes analytical modules to predict cancer cell response to drugs targeting SL partners.
- SynLethDB supports advanced data mining and interpretation through its integrated functionalities.
Conclusions:
- SynLethDB serves as a valuable resource for researchers and the pharmaceutical industry in the field of synthetic lethality.
- The database facilitates the discovery and development of novel, targeted cancer therapies.
- SynLethDB's integrated data and analytical tools empower deeper insights into SL interactions and their therapeutic potential.
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