Human synthetic lethal inference as potential anti-cancer target gene detection
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Updated: Jun 17, 2026
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
Nuria Conde-Pueyo1, Andreea Munteanu, Ricard V Solé
1ICREA-Complex Systems Lab, Universitat Pompeu Fabra, Parc de Recerca Biomedica de Barcelona, Dr Aiguader 88, E-08003 Barcelona, Spain. nuria.conde@upf.edu
Background:
Two genes are called synthetic lethal (SL) if mutation of either alone is not lethal, but mutation of both leads to death or a significant decrease in organism's fitness. The detection of SL gene pairs constitutes a promising alternative for anti-cancer therapy. As cancer cells exhibit a large number of mutations, the identification of these mutated genes' SL partners may provide specific anti-cancer drug candidates, with minor perturbations to the healthy cells. Since existent SL data is mainly restricted to yeast screenings, the road towards human SL candidates is limited to inference methods.
Results:
In the present work, we use phylogenetic analysis and database manipulation (BioGRID for interactions, Ensembl and NCBI for homology, Gene Ontology for GO attributes) in order to reconstruct the phylogenetically-inferred SL gene network for human. In addition, available data on cancer mutated genes (COSMIC and Cancer Gene Census databases) as well as on existent approved drugs (DrugBank database) supports our selection of cancer-therapy candidates.
Conclusions:
Our work provides a complementary alternative to the current methods for drug discovering and gene target identification in anti-cancer research. Novel SL screening analysis and the use of highly curated databases would contribute to improve the results of this methodology.
Insights
Identifying synthetic lethal (SL) gene pairs offers a novel anti-cancer therapy strategy. This study reconstructs the human SL gene network to discover potential drug targets for cancer treatment.
Area of Science:
- Genomics
- Computational Biology
- Bioinformatics
Background:
- Synthetic lethality (SL) describes gene pairs where mutating either gene alone is non-lethal, but mutating both is lethal.
- Identifying SL gene pairs is a promising strategy for developing targeted anti-cancer therapies.
- Current SL data is primarily from yeast, necessitating inference methods for human applications.
Purpose of the Study:
- To reconstruct the phylogenetically-inferred SL gene network for humans.
- To identify potential anti-cancer drug candidates by analyzing SL gene pairs.
- To provide a complementary approach to existing drug discovery and gene target identification methods.
Main Methods:
- Utilized phylogenetic analysis and database manipulation (BioGRID, Ensembl, NCBI, Gene Ontology).
- Integrated data from cancer mutated genes (COSMIC, Cancer Gene Census) and approved drugs (DrugBank).
- Reconstructed a human SL gene network based on inferred relationships.
Main Results:
- Developed a method for inferring human synthetic lethal gene networks.
- Identified potential cancer-therapy candidates based on SL gene interactions and cancer mutation data.
- Provided a computational framework for discovering novel SL gene pairs.
Conclusions:
- The study offers an alternative to current methods for anti-cancer drug discovery and gene target identification.
- Novel SL screening analysis and curated databases can enhance the methodology.
- This approach aids in identifying specific anti-cancer drug candidates with potentially fewer side effects on healthy cells.
