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Updated: May 11, 2026
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
Synthetic sickness or lethality points at candidate combination therapy targets in glioblastoma
Ewa Szczurek1, Navodit Misra, Martin Vingron
1Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Ihnestr. 63-73, 14195, Berlin, Germany. ewa.szczurek@bsse.ethz.ch
Abstract:
Synthetic lethal interactions in cancer hold the potential for successful combined therapies, which would avoid the difficulties of single molecule-targeted treatment. Identification of interactions that are specific for human tumors is an open problem in cancer research. This work aims at deciphering synthetic sick or lethal interactions directly from somatic alteration, expression and survival data of cancer patients. To this end, we look for pairs of genes and their alterations or expression levels that are "avoided" by tumors and "beneficial" for patients. Thus, candidates for synthetic sickness or lethality (SSL) interaction are identified as such gene pairs whose combination of states is under-represented in the data. Our main methodological contribution is a quantitative score that allows ranking of the candidate SSL interactions according to evidence found in patient survival. Applying this analysis to glioblastoma data, we collect 1,956 synthetic sick or lethal partners for 85 abundantly altered genes, most of which show extensive copy number variation across the patient cohort. We rediscover and interpret known interaction between TP53 and PLK1, as well as provide insight into the mechanism behind EGFR interacting with AKT2, but not AKT1 nor AKT3. Cox model analysis determines 274 of identified interactions as having significant impact on overall survival in glioblastoma, which is more informative than a standard survival predictor based on patient's age.
Insights
Researchers identified gene pairs with synthetic sickness or lethality (SSL) interactions by analyzing cancer patient data. These interactions, under-represented in tumors, offer potential for novel combination therapies and improved patient survival predictions.
Area of Science:
- Oncology
- Genomics
- Computational Biology
Background:
- Synthetic lethal interactions offer a promising avenue for cancer combination therapies, potentially overcoming limitations of single-target treatments.
- Identifying tumor-specific synthetic lethal interactions remains a challenge in cancer research.
Purpose of the Study:
- To develop a method for identifying synthetic sick or lethal (SSL) interactions directly from patient somatic alteration, gene expression, and survival data.
- To discover novel SSL interactions specific to human tumors.
Main Methods:
- Developed a quantitative score to rank candidate SSL interactions based on patient survival data.
- Analyzed gene pairs whose combined states (alterations or expression levels) are under-represented in tumor data.
- Applied the method to glioblastoma patient data.
Main Results:
- Identified 1,956 synthetic sick or lethal partners for 85 frequently altered genes in glioblastoma.
- Confirmed known interactions (e.g., TP53-PLK1) and provided mechanistic insights (e.g., EGFR-AKT2).
- Discovered 274 SSL interactions significantly impacting glioblastoma patient survival, outperforming age as a predictor.
Conclusions:
- The developed method effectively identifies clinically relevant synthetic sick or lethal interactions from patient data.
- These findings highlight the potential of SSL interactions for developing targeted cancer combination therapies.
- The identified interactions provide a valuable resource for future glioblastoma treatment strategies.
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