Synthetic lethality: emerging targets and opportunities in melanoma

Great progress has been made in the treatment of melanoma through use of targeted therapies and immunotherapy. One approach that has not been fully explored is synthetic lethality, which exploits somatically acquired changes, usually driver mutations, to specifically kill tumour cells. We outline the various approaches that may be applied to identify synthetic lethal interactions and define how these interactions may drive drug discovery efforts.     

Hitting the bull's eye: Novel directed cancer therapy through helicase‐targeted synthetic lethality

Designing strategies for anti‐cancer therapy have posed a significant challenge. One approach has been to inhibit specific DNA repair proteins and their respective pathways to enhance chemotherapy and radiation therapy used to treat cancer patients. Synthetic lethality represents an approach that exploits pre‐existing DNA repair deficiencies in certain tumors to develop inhibitors of DNA repair pathways that compensate for the tumor‐associated repair deficiency. Since helicases play critical roles in the DNA damage response and DNA repair, particularly in actively dividing and replicating cells, it is proposed that the identification and characterization of synthetic lethal relationships of DNA helicases will be of value in developing improved anti‐cancer treatment strategies. In this review, we discuss this hypothesis and current evidence for synthetic lethal interactions of eukaryotic DNA helicases in model systems. J. Cell. Biochem. 106: 758–763, 2009. Published 2009 Wiley‐Liss, Inc.     

突变型p53与其合成致死基因的研究进展

恶性肿瘤的靶向治疗已经成为现阶段肿瘤治疗的热点。随着人们对癌基因认知的加深,借助合成致死的方法靶向治疗肿瘤已成为针对肿瘤特异性治疗的新策略。p53基因突变在肿瘤的形成和发展过程中具有重要作用。因此,了解肿瘤中与突变型p53基因有合成致死关系的靶基因的作用方式,有助于指导由突变型p53基因诱发肿瘤的个性化治疗。与突变型p53基因具有合成致死关系的靶基因可分为细胞周期调控基因和细胞非周期调控基因,文章综述了这两类靶基因与突变型p53基因如何构成合成致死作用以及此作用的现实意义。     

The application of gene silencing in proteomics: from laboratory to clinic

Introduction: Since the completion of genome sequencing, gene silencing technologies have emerged as powerful tools to study gene functions in various biological processes, both in vivo and in vitro. Moreover, they have also been proposed as therapeutic agents to inhibit selected genes in a variety of pathological conditions, such as cancer, neurodegenerative, and cardiovascular diseases.

Area covered: This review summarizes the mechanisms of action and applications of genome editing tools, from RNA interference to clustered regularly interspaced short palindromic repeats-based systems, in research and in clinics. We describe their essential role in high-throughput genetic screens and, in particular, in functional proteomics studies, to identify diagnostic markers and therapeutic targets. Indeed, gene silencing and proteomics have been extensively integrated to study global proteome changes, posttranslational modifications, and protein–protein interactions.

Expert commentary: Functional proteomics approaches that leverage gene silencing tools have been successfully applied to examine the role of several genes in various contexts, leading to a deeper knowledge of biological pathways and disease mechanisms. Recent developments of gene silencing tools have improved their performance, also in terms of off-targets effects reduction, paving the way for a wider therapeutic application of these systems.     

Identification of synthetic lethality based on a functional network by using machine learning algorithms

Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 gene editing, efforts have been made to systematically identify synthetic lethal interactions, especially for frequently mutated genes in cancers. However, elucidating the mechanism of synthetic lethality remains a challenge because of the complexity of its influencing conditions. In this study, we proposed a new computational method to identify critical functional features that can accurately predict synthetic lethal interactions. This method incorporates several machine learning algorithms and encodes protein-coding genes by an enrichment system derived from gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to represent their functional features. We built a random forest-based prediction engine by using 2120 selected features and obtained a Matthews correlation coefficient of 0.532. We examined the top 15 features and found that most of them have potential roles in synthetic lethality according to previous studies. These results demonstrate the ability of our proposed method to predict synthetic lethal interactions and provide a basis for further characterization of these particular genetic combinations.     

Synthetic lethal interactions for the development of cancer therapeutics: biological and methodological advancements

Synthetic lethal interaction is defined as a combination of two mutations that is lethal when present in the same cell; each individual mutation is non-lethal. Synthetic lethal interactions attract attention in cancer research fields since the discovery of synthetic lethal genes with either oncogenes or tumor suppressor genes (TSGs) provides novel cancer therapeutic targets. Due to the selective lethal effect on cancer cells harboring specific genetic alterations, it is expected that targeting synthetic lethal genes would provide wider therapeutic windows compared with cytotoxic chemotherapeutics. Here, we review the current status of the application of synthetic lethal screening in cancer research fields from biological and methodological viewpoints. Very recent studies seeking to identify synthetic lethal genes with K-RAS and p53, which are known to be the most frequently occurring oncogenes and TSGs, respectively, are introduced. Among the accumulating amount of research on synthetic lethal interactions, the synthetic lethality between BRCA1/2 and PARP1 inhibition has been clinically proven. Thus, both preclinical and clinical data showing a preferential anti-tumor effect on BRCA1/2 deficient tumors by a PARP1 inhibitor are the best examples of the synthetic lethal approach of cancer therapeutics. Finally, methodological progress regarding synthetic lethal screening, including barcode shRNA screening and in vivo synthetic lethal screening, is described. Given the fact that an increasing number of synthetic lethal genes for major cancerous genes have been validated in preclinical studies, this intriguing approach awaits clinical verification of preferential benefits for cancer patients with specific genetic alterations as a clear predictive factor for tumor response.     

Drugging the addict: non‐oncogene addiction as a target for cancer therapy

Historically, cancers have been treated with chemotherapeutics aimed to have profound effects on tumor cells with only limited effects on normal tissue. This approach was followed by the development of small‐molecule inhibitors that can target oncogenic pathways critical for the survival of tumor cells. The clinical targeting of these so‐called oncogene addictions, however, is in many instances hampered by the outgrowth of resistant clones. More recently, the proper functioning of non‐mutated genes has been shown to enhance the survival of many cancers, a phenomenon called non‐oncogene addiction. In the current review, we will focus on the distinct non‐oncogenic addictions found in cancer cells, including synthetic lethal interactions, the underlying stress phenotypes, and arising therapeutic opportunities.     

Studying synthetic lethal interactions in the zebrafish system: insight into disease genes and mechanisms

The post-genomic era is marked by a pressing need to functionally characterize genes through understanding gene-gene interactions, as well as interactions between biological pathways. Exploiting a phenomenon known as synthetic lethality, in which simultaneous loss of two interacting genes leads to loss of viability, aids in the investigation of these interactions. Although synthetic lethal screening is a powerful technique that has been used with great success in many model organisms, including Saccharomyces cerevisiae, Drosophila melanogaster and Caenorhabditis elegans, this approach has not yet been applied in the zebrafish, Danio rerio. Recently, the zebrafish has emerged as a valuable system to model many human disease conditions; thus, the ability to conduct synthetic lethal screening using zebrafish should help to uncover many unknown disease-gene interactions. In this article, we discuss the concept of synthetic lethality and provide examples of its use in other model systems. We further discuss experimental approaches by which the concept of synthetic lethality can be applied to the zebrafish to understand the functions of specific genes.     

Synthetic Lethal Targeting of Superoxide Dismutase 1 Selectively Kills RAD54B-Deficient Colorectal Cancer Cells

Synthetic lethality is a rational approach to identify candidate drug targets for selective killing of cancer cells harboring somatic mutations that cause chromosome instability (CIN). To identify a set of the most highly connected synthetic lethal partner genes in yeast for subsequent testing in mammalian cells, we used the entire set of 692 yeast CIN genes to query the genome-wide synthetic lethal datasets. Hierarchical clustering revealed a highly connected set of synthetic lethal partners of yeast genes whose human orthologs are somatically mutated in colorectal cancer. Testing of a small matrix of synthetic lethal gene pairs in mammalian cells suggested that members of a pathway that remove reactive oxygen species that cause DNA damage would be excellent candidates for further testing. We show that the synthetic lethal interaction between budding yeast rad54 and sod1 is conserved within a human colorectal cancer context. Specifically, we demonstrate RAD54B-deficient cells are selectively killed relative to controls via siRNA-based silencing and chemical inhibition and further demonstrate that this interaction is conserved in an unrelated cell type. We further show that the DNA double strand breaks, resulting from increased reactive oxygen species following SOD1 inhibition, persist within the RAD54B-deficient cells and result in apoptosis. Collectively, these data identify SOD1 as a novel candidate cancer drug target and suggest that SOD1 inhibition may have broad-spectrum applicability in a variety of tumor types exhibiting RAD54B deficiencies.     

Target Inhibition Networks: Predicting Selective Combinations of Druggable Targets to Block Cancer Survival Pathways

A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced prediction accuracies in cross validation as well as significant reduction in computation times. Such cost-effective computational-experimental design strategies have the potential to greatly speed-up the drug testing efforts by prioritizing those interventions and interactions warranting further study in individual cancer cases.     

Road to the future of systems biotechnology: CRISPR-Cas-mediated metabolic engineering for recombinant protein production

The rising potential for CRISPR–Cas-mediated genome editing has revolutionized our strategies in basic and practical bioengineering research. It provides a predictable and precise method for genome modification in a robust and reproducible fashion. Emergence of systems biotechnology and synthetic biology approaches coupled with CRISPR–Cas technology could change the future of cell factories to possess some new features which have not been found naturally. We have discussed the possibility and versatile potentials of CRISPR–Cas technology for metabolic engineering of a recombinant host for heterologous protein production. We describe the mechanisms involved in this metabolic engineering approach and present the diverse features of its application in biotechnology and protein production.     

Systems and synthetic biology approaches in understanding biological oscillators

Background: Self-sustained oscillations are a ubiquitous and vital phenomenon in living systems. From primitive single-cellular bacteria to the most sophisticated organisms, periodicities have been observed in a broad spectrum of biological processes such as neuron firing, heart beats, cell cycles, circadian rhythms, etc. Defects in these oscillators can cause diseases from insomnia to cancer. Elucidating their fundamental mechanisms is of great significance to diseases, and yet challenging, due to the complexity and diversity of these oscillators. Results: Approaches in quantitative systems biology and synthetic biology have been most effective by simplifying the systems to contain only the most essential regulators. Here, we will review major progress that has been made in understanding biological oscillators using these approaches. The quantitative systems biology approach allows for identification of the essential components of an oscillator in an endogenous system. The synthetic biology approach makes use of the knowledge to design the simplest, de novo oscillators in both live cells and cell-free systems. These synthetic oscillators are tractable to further detailed analysis and manipulations. Conclusion: With the recent development of biological and computational tools, both approaches have made significant achievements.     

Genome-wide CRISPR screen identifies synthetic lethality between DOCK1 inhibition and metformin in liver cancer

Metformin is currently a strong candidate anti-tumor agent in multiple cancers. However, its anti-tumor effectiveness varies among different cancers or subpopulations, potentially due to tumor heterogeneity. It thus remains unclear which hepatocellular carcinoma (HCC) patient subpopulation(s) can benefit from metformin treatment. Here, through a genome-wide CRISPR-Cas9-based knockout screen, we find that DOCK1 levels determine the anti-tumor effects of metformin and that DOCK1 is a synthetic lethal target of metformin in HCC. Mechanistically, metformin promotes DOCK1 phosphorylation, which activates RAC1 to facilitate cell survival, leading to metformin resistance. The DOCK1-selective inhibitor, TBOPP, potentiates anti-tumor activity by metformin in vitro in liver cancer cell lines and patient-derived HCC organoids, and in vivo in xenografted liver cancer cells and immunocompetent mouse liver cancer models. Notably, metformin improves overall survival of HCC patients with low DOCK1 levels but not among patients with high DOCK1 expression. This study shows that metformin effectiveness depends on DOCK1 levels and that combining metformin with DOCK1 inhibition may provide a promising personalized therapeutic strategy for metformin-resistant HCC patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13238-022-00906-6.     

Identification of unique sensitizing targets for anti‐inflammatory CDDO‐Me in metastatic melanoma by a large‐scale synthetic lethal RNAi screening

CDDO‐Me has been shown to exert potent anti‐inflammatory activity for chronic kidney disease and antitumor activity for several tumors, including melanoma, in early clinical trials. To improve CDDO‐Me response in melanoma, we utilized a large‐scale synthetic lethal RNAi screen targeting 6000 human druggable genes to identify targets that would sensitize melanoma cells to CDDO‐Me. Based on screening results, five unique genes (GNPAT, SUMO1, SPINT2, FLI1, and SSX1) significantly potentiated the growth inhibitory effects of CDDO‐Me and induced apoptosis in A375, a BRAF mutated melanoma line (P < 0.001). These five genes were then individually validated as targets to potentiate CDDO‐Me activity, and related downstream signaling pathways of these genes were analyzed. In addition, the levels of phosphorylated Erk1/2, Akt, GSK‐2, and PRAS40 were dramatically decreased by downregulating each of these five genes separately, suggesting a set of common mediators. Our findings indicate that GNPAT, SUMO1, SPINT2, FLI1, and SSX1 play critical roles in synergy with inflammation pathways in modulating melanoma cell survival and could serve as sensitizing targets to enhance CDDO‐Me efficacy in melanoma growth control.     

A network-based approach to integrate nutrient microenvironment in the prediction of synthetic lethality in cancer metabolism

Synthetic Lethality (SL) is currently defined as a type of genetic interaction in which the loss of function of either of two genes individually has limited effect in cell viability but inactivation of both genes simultaneously leads to cell death. Given the profound genomic aberrations acquired by tumor cells, which can be systematically identified with -omics data, SL is a promising concept in cancer research. In particular, SL has received much attention in the area of cancer metabolism, due to the fact that relevant functional alterations concentrate on key metabolic pathways that promote cellular proliferation. With the extensive prior knowledge about human metabolic networks, a number of computational methods have been developed to predict SL in cancer metabolism, including the genetic Minimal Cut Sets (gMCSs) approach. A major challenge in the application of SL approaches to cancer metabolism is to systematically integrate tumor microenvironment, given that genetic interactions and nutritional availability are interconnected to support proliferation. Here, we propose a more general definition of SL for cancer metabolism that combines genetic and environmental interactions, namely loss of gene functions and absence of nutrients in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase (DHFR) inhibition in different environments. Finally, our approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.     

A Synthetic Lethal Screen Identifies DNA Repair Pathways that Sensitize Cancer Cells to Combined ATR Inhibition and Cisplatin Treatments

The DNA damage response kinase ATR may be a useful cancer therapeutic target. ATR inhibition synergizes with loss of ERCC1, ATM, XRCC1 and DNA damaging chemotherapy agents. Clinical trials have begun using ATR inhibitors in combination with cisplatin. Here we report the first synthetic lethality screen with a combination treatment of an ATR inhibitor (ATRi) and cisplatin. Combination treatment with ATRi/cisplatin is synthetically lethal with loss of the TLS polymerase ζ and 53BP1. Other DNA repair pathways including homologous recombination and mismatch repair do not exhibit synthetic lethal interactions with ATRi/cisplatin, even though loss of some of these repair pathways sensitizes cells to cisplatin as a single-agent. We also report that ATRi strongly synergizes with PARP inhibition, even in homologous recombination-proficient backgrounds. Lastly, ATR inhibitors were able to resensitize cisplatin-resistant cell lines to cisplatin. These data provide a comprehensive analysis of DNA repair pathways that exhibit synthetic lethality with ATR inhibitors when combined with cisplatin chemotherapy, and will help guide patient selection strategies as ATR inhibitors progress into the cancer clinic.     

A Critical Review of Genome Editing and Synthetic Biology Applications in Metabolic Engineering of Microalgae and Cyanobacteria

Being the green gold of the future, microalgae and cyanobacteria have recently attracted considerable interest worldwide, for their metabolites such as lipids, protein, pigments, and bioactive compounds have immense potential for sustainable energy and pharmaceutical production capabilities. In the last decades, the efforts attended to enhance the usage of microalgae and cyanobacteria by genetic manipulation, synthetic and metabolic engineering. However, the development of photoautotrophic cell factories have rarely compared to the heterotrophic counterparts due to limited tools, bioinformatics, and multi‐omics database. Therefore, recent advances of their genome editing techniques by clustered regularly interspaced short palindromic repeats (CRISPR) technology, and potential applications of their metabolic engineering and regulation approaches are examined in this review. Moreover, the contemporary achievements of synthetic biology approaches of microalgae and cyanobacteria in carbon fixation and sequestration, lipid and triacylglycerol (TAG), and sustainable production of high value‐added chemicals, such as carotenoids and docosahexaenoic acid (DHA), have been also discussed. From recent genomic study to trends in metabolic regulation of microalgae and cyanobacteria and a comprehensive assessment of the current challenges and opportunities for microalgae and cyanobacteria is also conducted.     

Mapping genetically compensatory pathways from synthetic lethal interactions in yeast

Background

Synthetic lethal genetic interaction analysis has been successfully applied to predicting the functions of genes and their pathway identities. In the context of synthetic lethal interaction data alone, the global similarity of synthetic lethal interaction patterns between two genes is used to predict gene function. With physical interaction data, such as protein-protein interactions, the enrichment of physical interactions within subsets of genes and the enrichment of synthetic lethal interactions between those subsets of genes are used as an indication of compensatory pathways.

Result

In this paper, we propose a method of mapping genetically compensatory pathways from synthetic lethal interactions. Our method is designed to discover pairs of gene-sets in which synthetic lethal interactions are depleted among the genes in an individual set and where such gene-set pairs are connected by many synthetic lethal interactions. By its nature, our method could select compensatory pathway pairs that buffer the deleterious effect of the failure of either one, without the need of physical interaction data. By focusing on compensatory pathway pairs where genes in each individual pathway have a highly homogenous cellular function, we show that many cellular functions have genetically compensatory properties.

Conclusion

We conclude that synthetic lethal interaction data are a powerful source to map genetically compensatory pathways, especially in systems lacking physical interaction information, and that the cellular function network contains abundant compensatory properties.     

A strategy for extracting and analyzing large-scale quantitative epistatic interaction data

Recently, approaches have been developed for high-throughput identification of synthetic sick/lethal gene pairs. However, these are only a specific example of the broader phenomenon of epistasis, wherein the presence of one mutation modulates the phenotype of another. We present analysis techniques for generating high-confidence quantitative epistasis scores from measurements made using synthetic genetic array and epistatic miniarray profile (E-MAP) technology, as well as several tools for higher-level analysis of the resulting data that are greatly enhanced by the quantitative score and detection of alleviating interactions.