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

Methylation synthetic lethality: Exploiting selective drug targets for cancer therapy

In cancer, synthetic lethality refers to the drug-induced inactivation of one gene and the inhibition of another in cancer cells by a drug, resulting in the death of only cancer cells; however, this effect is not present in normal cells, leading to targeted killing of cancer cells. Recent intensive epigenetic research has revealed that aberrant epigenetic changes are more frequently observed than gene mutations in certain cancers. Recently, numerous studies have reported various methylation synthetic lethal combinations involving DNA damage repair genes, metabolic pathway genes, and paralogs with significant results in cellular models, some of which have already entered clinical trials with promising results. This review systematically introduces the advantages of methylation synthetic lethality and describes the lethal mechanisms of methylation synthetic lethal combinations that have recently demonstrated success in cellular models. Furthermore, we discuss the future opportunities and challenges of methylation synthetic lethality in targeted anticancer therapies.

CSSLdb: Discovery of cancer-specific synthetic lethal interactions based on machine learning and statistic inference

Synthetic lethality (SL) occurs when the inactivation of two genes results in cell death while the inactivation of either gene alone is non-lethal. SL-based therapy has become a promising anti-cancer treatment option with the increasing researches and applications in clinical practice, while the specific therapeutic opportunities for various cancers have not yet been comprehensively investigated. Herein, we described a computational approach based on machine learning and statistical inference to discover the cancer-specific synthetic lethal interactions. First, Random Forest and One-Class SVM were used to perform cancer unbiased prediction of synthetic lethality. Then, two strategies, including mutual exclusivity and differential expression, were used to screen cancer-specific synthetic lethal interactions, resulting in 14,582 SL gene pairs in 33 cancer types. Finally, we developed a freely available database of CSSLdb (Cancer Specific Synthetic Lethality Database, http://www.tmliang.cn/CSSL/) to present cancer-specific synthetic lethal genetic interactions, which would enrich the relevant research and contribute to underlying therapy strategies based on synthetic lethality.

A framework for considering prior information in network-based approaches to omics data analysis

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Multi-omics characterization of synthetic lethality-related molecular features: implications for SL-based therapeutic target screening

Synthetic lethality (SL) represents the co-occurrence of two or more non-lethal disordered genes that could lead to cell death. SL-based anticancer therapeutics could specifically kill the cancer cells carrying the targeted mutated gene while leaving normal cells alive. Recent large-scale computational and experimental screenings provide rich resources of SL information while lacking systematic research on molecular features of SL genes. Combined with comprehensive multi-omics data analysis and experimental validation of one SL gene pair, Guo et al. portrayed a systematic layout of cancer-specific SL interactions that could improve understanding of carcinogenesis and potentially assist the subsequent screening of anticancer therapeutic targets.

Systematic analysis of cancer-specific synthetic lethal interactions provides insight into personalized anticancer therapy

The concept of synthetic lethality has great potential for anticancer therapy as a new strategy to specifically kill cancer cells while sparing normal cells. To further understand the potential molecular interactions and gene characteristics involved in synthetic lethality, we performed a comprehensive analysis of predicted cancer-specific genetic interactions. Many genes were identified as cancer-associated genes that contributed to multiple biological processes and pathways, and the gene features were not random, indicating their potential roles in human carcinogenesis. Some relevant genes detected in multiple cancers were prone to be enriched in specific biological progresses and pathways, especially processes associated with DNA damage, chromosome-related functions and cancer pathways. These findings strongly implicated potential roles for these genes in cancer pathophysiology and functional relationships, as well as applications for future anticancer drug discovery. Further experimental validation indicated that the synthetic lethal interaction of APC and GFER may provide a potential anticancer strategy for patients with APC-mutant colon cancer. These results will contribute to further exploration of synthetic lethal interactions and broader application of the concept of synthetic lethality in anticancer therapeutics.