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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
Computational methods, databases and tools for synthetic lethality prediction
Jing Wang1, Qinglong Zhang1, Junshan Han1
1Department of Bioinformatics, Institute of Health Service and Transfusion Medicine, Beijing 100850, China.
Abstract:
Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.
Insights
Synthetic lethality (SL) pairs are crucial for targeted cancer therapy. This review explores computational methods and databases to efficiently identify these gene pairs, accelerating drug discovery.
Area of Science:
- Genetics and Genomics
- Computational Biology
- Cancer Therapeutics
Background:
- Synthetic lethality (SL) is a genetic interaction where simultaneous inactivation of two genes leads to cell death, while individual inactivation is tolerated.
- SL-based therapies, exemplified by PARP inhibitors, represent a significant advancement in targeted cancer treatment.
- Identifying robust SL pairs is critical for expanding these therapies, but experimental screening is limited by the vast number of gene combinations.
Purpose of the Study:
- To review computational methods for predicting synthetic lethality (SL) pairs.
- To summarize relevant data resources and highlight the application of various computational approaches.
- To discuss challenges and future directions in computational SL prediction for cancer therapy.
Main Methods:
- Review of existing literature on synthetic lethality (SL) screening and prediction.
- Summary of data resources and databases relevant to SL pair identification.
- Categorization and elaboration of computational prediction methods, including statistical, network-based, classical machine learning, and deep learning approaches, with a focus on negative sampling.
Main Results:
- Computational methods significantly complement experimental approaches by reducing the search space for SL pairs.
- Various computational strategies, from statistical to deep learning, have been applied to predict SL interactions.
- The review consolidates information on SL-related data, prediction tools, and methodologies.
Conclusions:
- Computational prediction is essential for efficient identification of synthetic lethality (SL) pairs, accelerating the development of targeted cancer therapies.
- Further development of computational models and integration of diverse data sources are needed to improve prediction accuracy.
- Addressing challenges in negative sampling and model interpretability will be key for future advancements in SL-based drug discovery.
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