Related Experiment Video
Updated: Aug 17, 2025
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens
Sumana Srivatsa1,2, Hesam Montazeri3, Gaia Bianco4
1Department of Biosystems Science and Engineering, ETH Zurich, 4058, Basel, Switzerland.
Abstract:
The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions (ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets.
Insights
We developed SLIdR, a new statistical method to find synthetic lethal (SL) gene pairs for cancer drug targets. SLIdR accurately identifies potential SL interactions from large screening datasets, aiding in the discovery of novel cancer therapies.
Area of Science:
- Genomics
- Computational Biology
- Cancer Research
Background:
- Developing effective cancer therapies relies on identifying viable drug targets.
- Synthetic lethality (SL) offers a promising strategy, where targeting pairs of genes is lethal to cancer cells but not normal cells.
Purpose of the Study:
- To introduce SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL gene pairs from large-scale perturbation screens.
- To demonstrate SLIdR's capability in predicting SL pairs accurately, even with limited data and minimal false positives.
Main Methods:
- Development of the SLIdR statistical framework.
- Application of SLIdR to analyze Project DRIVE perturbation screen data.
- Experimental validation of predicted SL interactions in hepatocellular carcinoma models.
Main Results:
- SLIdR successfully identified both known and novel pan-cancer and cancer-specific SL pairs.
- The framework demonstrated high accuracy in predicting SL interactions, validated by existing literature and drug screening data.
- Experimental validation confirmed two predicted SL interactions (ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma.
Conclusions:
- SLIdR is a robust tool for identifying synthetic lethal pairs from large-scale screening data.
- The framework facilitates the discovery of potential cancer drug targets, advancing therapeutic development.
- Experimental validation supports SLIdR's utility in uncovering clinically relevant synthetic lethal interactions.
Related Concept Videos
02:26Protein Networks
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
02:50Combination Therapies and Personalized Medicine
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
01:19Interactions Between Signaling Pathways
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...