Computational methods, databases and tools for synthetic lethality prediction

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.

SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis

“…As a strategy in cancer therapy with increasing interests and potential applications, SL has been widely concerned in cancer treatment, especially for recent clinical success ( 8 ). Given the importance of SL, many studies aim to comprehensively obtain experimentally verified and computationally predicted candidate synthetic lethal interactions that can provide potential targets for anticancer drugs ( 18–21 , 30 , 32 , 42–45 ), which can greatly promote the rapid development of cancer treatment based on the theory of SL ( 46 ). Although the concept of SL is an attractive therapeutic strategy, only PARPi has entered the clinic.…”

SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network

“…The clinical utilization of SL prediction based on machine learning is limited by the lack of information regarding its biomedical relevance, its interpretability. Consequently, novel computational approaches are needed to solve this defect ( Wang, 2022 ).…”

Cell context-specific Synthetic lethality Prediction and Mechanism Analysis

“…Review by Wang et al provides specific references to machine learning model approaches, databases, and tools for summarization[34]. However, current state of the art ML and DL models in predicting SL interactions have significant limitations.…”

“…However, due to the vast library size, gene pairs need to be filtered based on various criteria, resulting in only a limited number of gene pairs being tested. In response to these challenges, computational approaches that utilize machine learning algorithms and large-scale omics datasets have emerged as promising alternatives for the discovery of SL targets in genome wise scale [33,34]. Review by Wang et al provides specific references to machine learning model approaches, databases, and tools for summarization [34].…”