Synthetic Lethality between Cohesin and WNT Signaling Pathways in Diverse Cancer Contexts.

Cohesin is a highly conserved ring-shaped complex involved in topologically embracing chromatids, gene expression regulation, genome compartmentalization, and genome stability maintenance. Genomic analyses have detected mutations in the cohesin complex in a wide array of human tumors. These findings have led to increased interest in cohesin as a potential target in cancer therapy. Synthetic lethality has been suggested as an approach to exploit genetic differences in cancer cells to influence their selective killing. In this study, we show that mutations in ESCO1, NIPBL, PDS5B, RAD21, SMC1A, SMC3, STAG2, and WAPL genes are synthetically lethal with stimulation of WNT signaling obtained following LY2090314 treatment, a GSK3 inhibitor, in several cancer cell lines. Moreover, treatment led to the stabilization of ß-catenin and affected the expression of c-MYC, probably due to the occupancy decrease in cohesin at the c-MYC promoter. Finally, LY2090314 caused gene expression dysregulation mainly involving pathways related to transcription regulation, cell proliferation, and chromatin remodeling. For the first time, our work provides the underlying molecular basis for synthetic lethality due to cohesin mutations and suggests that targeting the WNT may be a promising therapeutic approach for tumors carrying mutated cohesin.

Genome-wide CRISPR screen reveals the synthetic lethality between BCL2L1 inhibition and radiotherapy.

Radiation therapy (RT) is one of the most commonly used anticancer therapies. However, the landscape of cellular response to irradiation, especially to a single high-dose irradiation, remains largely unknown. In this study, we performed a whole-genome CRISPR loss-of-function screen and revealed temporal inherent and acquired responses to RT. Specifically, we found that loss of the IL1R1 pathway led to cellular resistance to RT. This is in part because of the involvement of radiation-induced IL1R1-dependent transcriptional regulation, which relies on the NF-κB pathway. Moreover, the mitochondrial anti-apoptotic pathway, particularly the BCL2L1 gene, is crucially important for cell survival after radiation. BCL2L1 inhibition combined with RT dramatically impeded tumor growth in several breast cancer cell lines and syngeneic models. Taken together, our results suggest that the combination of an apoptosis inhibitor such as a BCL2L1 inhibitor with RT may represent a promising anticancer strategy for solid cancers including breast cancer.

A systematic analysis of the landscape of synthetic lethality-driven precision oncology.

BACKGROUND: Synthetic lethality (SL) denotes a genetic interaction between two genes whose co-inactivation is detrimental to cells. Because more than 25 years have passed since SL was proposed as a promising way to selectively target cancer vulnerabilities, it is timely to comprehensively assess its impact so far and discuss its future. METHODS: We systematically analyzed the literature and clinical trial data from the PubMed and Trialtrove databases to portray the preclinical and clinical landscape of SL oncology. FINDINGS: We identified 235 preclinically validated SL pairs and found 1,207 pertinent clinical trials, and the number keeps increasing over time. About one-third of these SL clinical trials go beyond the typically studied DNA damage response (DDR) pathway, testifying to the recently broadening scope of SL applications in clinical oncology. We find that SL oncology trials have a greater success rate than non-SL-based trials. However, about 75% of the preclinically validated SL interactions have not yet been tested in clinical trials. CONCLUSIONS: Dissecting the recent efforts harnessing SL to identify predictive biomarkers, novel therapeutic targets, and effective combination therapy, our systematic analysis reinforces the hope that SL may serve as a key driver of precision oncology going forward. FUNDING: Funded by the Samsung Research Funding & Incubation Center of Samsung Electronics, the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Republic of Korea government (MSIT), the Kwanjeong Educational Foundation, the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute (NCI), and Center for Cancer Research (CCR).

Complex synthetic lethality in cancer.

The concept of synthetic lethality has been widely applied to identify therapeutic targets in cancer, with varying degrees of success. The standard approach normally involves identifying genetic interactions between two genes, a driver and a target. In reality, however, most cancer synthetic lethal effects are likely complex and also polygenic, being influenced by the environment in addition to involving contributions from multiple genes. By acknowledging and delineating this complexity, we describe in this article how the success rate in cancer drug discovery and development could be improved.

Targeting the TCA cycle through cuproptosis confers synthetic lethality on ARID1A-deficient hepatocellular carcinoma.

ARID1A is among the most commonly mutated tumor suppressor genes in hepatocellular carcinoma (HCC). In this study, we conduct a CRISPR-Cas9 synthetic lethality screen using ARID1A-deficient HCC cells to identify approaches to treat HCC patients harboring ARID1A deficiency. This strategy reveals that the survival of these ARID1A-deficient HCC cells is highly dependent on genes related to the tricarboxylic acid (TCA) cycle. Mechanistically, ARID1A loss represses expression of key glycolysis-related gene PKM, shifting cellular glucose metabolism from aerobic glycolysis to dependence on the TCA cycle and oxidative phosphorylation. Cuproptosis is a recently defined form of copper-induced cell death reported to directly target the TCA cycle. Here, we find that ARID1A-deficient HCC cells and xenograft tumors are highly sensitive to copper treatment. Together, these results offer evidence of the synthetic lethality between ARID1A deficiency and mitochondrial respiration impairment, suggesting that copper treatment constitutes a promising therapeutic strategy for selectively targeting ARID1A-deficient HCC.

Evidence of a synthetic lethality interaction between SETDB1 histone methyltransferase and CHD4 chromatin remodeling protein in a triple negative breast cancer cell line.

During the tumorigenic process, cancer cells may become overly dependent on the activity of backup cellular pathways for their survival, representing vulnerabilities that could be exploited as therapeutic targets. Certain molecular vulnerabilities manifest as a synthetic lethality relationship, and the identification and characterization of new synthetic lethal interactions may pave the way for the development of new therapeutic approaches for human cancer. Our goal was to investigate a possible synthetic lethal interaction between a member of the Chromodomain Helicase DNA binding proteins family (CHD4) and a member of the histone methyltransferases family (SETDB1) in the molecular context of a cell line (Hs578T) representing the triple negative breast cancer (TNBC), a subtype of breast cancer lacking validated molecular targets for treatment. Therefore, we employed the CRISPR-Cas9 gene editing tool to individually or simultaneously introduce indels in the genomic loci corresponding to the catalytic domains of SETDB1 and CHD4 in the Hs578T cell line. Our main findings included: a) introduction of indels in exon 22 of SETDB1 sensitized Hs578T to the action of the genotoxic chemotherapy doxorubicin; b) by sequentially introducing indels in exon 22 of SETDB1 and exon 23 of CHD4 and tracking the percentage of the remaining wild-type sequences in the mixed cell populations generated, we obtained evidence of the existence of a synthetic lethality interaction between these genes. Considering the lack of molecular targets in TNBC, our findings provided valuable insights for development of new therapeutic approaches not only for TNBC but also for other cancer types.

Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells.

Synthetic lethality (SL) is a promising concept in cancer research. A wide array of computational tools has been developed to predict and exploit synthetic lethality for the identification of tumour-specific vulnerabilities. Previously, we introduced the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to SL developed for genome-scale metabolic networks. The major challenge in our gMCS framework is to go beyond metabolic networks and extend existing algorithms to more complex protein-protein interactions. In this article, we take a step further and incorporate linear regulatory pathways into our gMCS approach. Extensive algorithmic modifications to compute gMCSs in integrated metabolic and regulatory models are presented in detail. Our extended approach is applied to calculate gMCSs in integrated models of human cells. In particular, we integrate the most recent genome-scale metabolic network, Human1, with 3 different regulatory network databases: Omnipath, Dorothea and TRRUST. Based on the computed gMCSs and transcriptomic data, we discovered new essential genes and their associated synthetic lethal for different cancer cell lines. The performance of the different integrated models is assessed with available large-scale in-vitro gene silencing data. Finally, we discuss the most relevant gene essentiality predictions based on published literature in cancer research.

A ribosomal gene panel predicting a novel synthetic lethality in non-BRCAness tumors.

Poly (ADP-ribose) polymerase (PARP) inhibitors are one of the most exciting classes of targeted therapy agents for cancers with homologous recombination (HR) deficiency. However, many patients without apparent HR defects also respond well to PARP inhibitors/cisplatin. The biomarker responsible for this mechanism remains unclear. Here, we identified a set of ribosomal genes that predict response to PARP inhibitors/cisplatin in HR-proficient patients. PARP inhibitor/cisplatin selectively eliminates cells with high expression of the eight genes in the identified panel via DNA damage (ATM) signaling-induced pro-apoptotic ribosomal stress, which along with ATM signaling-induced pro-survival HR repair constitutes a new model to balance the cell fate in response to DNA damage. Therefore, the combined examination of the gene panel along with HR status would allow for more precise predictions of clinical response to PARP inhibitor/cisplatin. The gene panel as an independent biomarker was validated by multiple published clinical datasets, as well as by an ovarian cancer organoids library we established. More importantly, its predictive value was further verified in a cohort of PARP inhibitor-treated ovarian cancer patients with both RNA-seq and WGS data. Furthermore, we identified several marketed drugs capable of upregulating the expression of the genes in the panel without causing HR deficiency in PARP inhibitor/cisplatin-resistant cell lines. These drugs enhance PARP inhibitor/cisplatin sensitivity in both intrinsically resistant organoids and cell lines with acquired resistance. Together, our study identifies a marker gene panel for HR-proficient patients and reveals a broader application of PARP inhibitor/cisplatin in cancer therapy.

Synthetic Lethality Targeting Polθ.

Research studies regarding synthetic lethality (SL) in human cells are primarily motivated by the potential of this phenomenon to be an effective, but at the same time, safe to the patient's anti-cancer chemotherapy. Among the factors that are targets for the induction of the synthetic lethality effect, those involved in DNA repair seem to be the most relevant. Specifically, when mutation in one of the canonical DNA double-strand break (DSB) repair pathways occurs, which is a frequent event in cancer cells, the alternative pathways may be a promising target for the elimination of abnormal cells. Currently, inhibiting RAD52 and/or PARP1 in the tumor cells that are deficient in the canonical repair pathways has been the potential target for inducing the effect of synthetic lethality. Unfortunately, the development of resistance to commonly used PARP1 inhibitors (PARPi) represents the greatest obstacle to working out a successful treatment protocol. DNA polymerase theta (Polθ), encoded by the POLQ gene, plays a key role in an alternative DSB repair pathway-theta-mediated end joining (TMEJ). Thus, it is a promising target in the treatment of tumors harboring deficiencies in homologous recombination repair (HRR), where its inhibition can induce SL. In this review, the authors discuss the current state of knowledge on Polθ as a potential target for synthetic lethality-based anticancer therapies.

Synthetic lethality between TP53 and ENDOD1.

The atypical nuclease ENDOD1 functions with cGAS-STING in innate immunity. Here we identify a previously uncharacterized ENDOD1 function in DNA repair. ENDOD1 is enriched in the nucleus following H2O2 treatment and ENDOD1-/- cells show increased PARP chromatin-association. Loss of ENDOD1 function is synthetic lethal with homologous recombination defects, with affected cells accumulating DNA double strand breaks. Remarkably, we also uncover an additional synthetic lethality between ENDOD1 and p53. ENDOD1 depletion in TP53 mutated tumour cells, or p53 depletion in ENDOD1-/- cells, results in rapid single stranded DNA accumulation and cell death. Because TP53 is mutated in ~50% of tumours, ENDOD1 has potential as a wide-spectrum target for synthetic lethal treatments. To support this we demonstrate that systemic knockdown of mouse EndoD1 is well tolerated and whole-animal siRNA against human ENDOD1 restrains TP53 mutated tumour progression in xenograft models. These data identify ENDOD1 as a potential cancer-specific target for SL drug discovery.

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.

Targeting Ribonucleotide Reductase Induces Synthetic Lethality in PP2A-Deficient Uterine Serous Carcinoma.

Uterine serous carcinoma (USC) is a highly aggressive endometrial cancer subtype with limited therapeutic options and a lack of targeted therapies. While mutations to PPP2R1A, which encodes the predominant protein phosphatase 2A (PP2A) scaffolding protein Aα, occur in 30% to 40% of USC cases, the clinical actionability of these mutations has not been studied. Using a high-throughput screening approach, we showed that mutations in Aα results in synthetic lethality following treatment with inhibitors of ribonucleotide reductase (RNR). In vivo, multiple models of Aα mutant uterine serous tumors were sensitive to clofarabine, an RNR inhibitor (RNRi). Aα-mutant cells displayed impaired checkpoint signaling upon RNRi treatment and subsequently accumulated more DNA damage than wild-type (WT) cells. Consistently, inhibition of PP2A activity using LB-100, a catalytic inhibitor, sensitized WT USC cells to RNRi. Analysis of The Cancer Genome Atlas data indicated that inactivation of PP2A, through loss of PP2A subunit expression, was prevalent in USC, with 88% of patients with USC harboring loss of at least one PP2A gene. In contrast, loss of PP2A subunit expression was rare in uterine endometrioid carcinomas. While RNRi are not routinely used for uterine cancers, a retrospective analysis of patients treated with gemcitabine as a second- or later-line therapy revealed a trend for improved outcomes in patients with USC treated with RNRi gemcitabine compared with patients with endometrioid histology. Overall, our data provide experimental evidence to support the use of ribonucleotide reductase inhibitors for the treatment of USC. SIGNIFICANCE: A drug repurposing screen identifies synthetic lethal interactions in PP2A-deficient uterine serous carcinoma, providing potential therapeutic avenues for treating this deadly endometrial cancer.

The rarA gene as part of an expanded RecFOR recombination pathway: Negative epistasis and synthetic lethality with ruvB, recG, and recQ.

The RarA protein, homologous to human WRNIP1 and yeast MgsA, is a AAA+ ATPase and one of the most highly conserved DNA repair proteins. With an apparent role in the repair of stalled or collapsed replication forks, the molecular function of this protein family remains obscure. Here, we demonstrate that RarA acts in late stages of recombinational DNA repair of post-replication gaps. A deletion of most of the rarA gene, when paired with a deletion of ruvB or ruvC, produces a growth defect, a strong synergistic increase in sensitivity to DNA damaging agents, cell elongation, and an increase in SOS induction. Except for SOS induction, these effects are all suppressed by inactivating recF, recO, or recJ, indicating that RarA, along with RuvB, acts downstream of RecA. SOS induction increases dramatically in a rarA ruvB recF/O triple mutant, suggesting the generation of large amounts of unrepaired ssDNA. The rarA ruvB defects are not suppressed (and in fact slightly increased) by recB inactivation, suggesting RarA acts primarily downstream of RecA in post-replication gaps rather than in double strand break repair. Inactivating rarA, ruvB and recG together is synthetically lethal, an outcome again suppressed by inactivation of recF, recO, or recJ. A rarA ruvB recQ triple deletion mutant is also inviable. Together, the results suggest the existence of multiple pathways, perhaps overlapping, for the resolution or reversal of recombination intermediates created by RecA protein in post-replication gaps within the broader RecF pathway. One of these paths involves RarA.

Predicting Synthetic Lethality in Human Cancers via Multi-Graph Ensemble Neural Network.

Synthetic lethality (SL) is currently one of the most effective methods to identify new drugs for cancer treatment. It means that simultaneous inactivation target of two non-lethal genes will cause cell death, but loss of either will not. However, detecting SL pair is challenging due to the experimental costs. Artificial intelligence (AI) is a low-cost way to predict the potential SL relation between two genes. In this paper, a new Multi-Graph Ensemble (MGE) network structure combining graph neural network and existing knowledge about genes is proposed to predict SL pairs, which integrates the embedding of each feature with different neural networks to predict if a pair of genes have SL relation. It has a higher prediction performance compared with existing SL prediction methods. Also, with the integration of other biological knowledge, it has the potential of interpretability.

Mapping the landscape of synthetic lethal interactions in liver cancer.

Almost all the current therapies against liver cancer are based on the "one size fits all" principle and offer only limited survival benefit. Fortunately, synthetic lethality (SL) may provide an alternate route towards individualized therapy in liver cancer. The concept that simultaneous losses of two genes are lethal to a cell while a single loss is non-lethal can be utilized to selectively eliminate tumors with genetic aberrations. Methods: To infer liver cancer-specific SL interactions, we propose a computational pipeline termed SiLi (statistical inference-based synthetic lethality identification) that incorporates five inference procedures. Based on large-scale sequencing datasets, SiLi analysis was performed to identify SL interactions in liver cancer. Results: By SiLi analysis, a total of 272 SL pairs were discerned, which included 209 unique target candidates. Among these, polo-like kinase 1 (PLK1) was considered to have considerable therapeutic potential. Further computational and experimental validation of the SL pair TP53-PLK1 demonstrated that inhibition of PLK1 could be a novel therapeutic strategy specifically targeting those patients with TP53-mutant liver tumors. Conclusions: In this study, we report a comprehensive analysis of synthetic lethal interactions of liver cancer. Our findings may open new possibilities for patient-tailored therapeutic interventions in liver cancer.

Synthetic Lethality in Ovarian Cancer.

Ovarian cancers include several distinct malignancies which differ with respect to clinicopathologic features and prognosis. High-grade serous cancer is the most common histologic subtype and accounts for most ovarian cancer-related deaths. High-grade serous ovarian cancer (HGSOC) is treated with surgery and platinum-based chemotherapy, but most patients relapse and succumb to chemoresistant disease. The genetic concept of synthetic lethality, in which the synergy of mutations in multiple genes results in cell death, provides a framework to design novel therapeutic approaches to overcome chemoresistance in ovarian cancer. Recent progress in understanding the genomic architecture and hereditary drivers of ovarian cancer has shown potential for synthetic lethality strategies designed around homologous DNA repair. Clinical trials have validated high response rates for PARP inhibitors in patients with BRCA1 or BRCA2 mutations. Here we discuss the biological rationale behind targeting BRCA-PARP synthetic lethality based on genetic context in ovarian cancer and how this approach is being assessed in the clinic. Applying the concept of synthetic lethality to target non-BRCA-mutant cancers is an ongoing challenge, and we discuss novel approaches to target ovarian cancer using synthetic lethality in combination with and beyond PARP inhibitors. This review will also describe obstacles for synthetic lethality in ovarian cancer and new opportunities to develop potent targeted drugs for patients with ovarian cancer.

Comprehensive prediction of robust synthetic lethality between paralog pairs in cancer cell lines.

Pairs of paralogs may share common functionality and, hence, display synthetic lethal interactions. As the majority of human genes have an identifiable paralog, exploiting synthetic lethality between paralogs may be a broadly applicable approach for targeting gene loss in cancer. However, only a biased subset of human paralog pairs has been tested for synthetic lethality to date. Here, by analyzing genome-wide CRISPR screens and molecular profiles of over 700 cancer cell lines, we identify features predictive of synthetic lethality between paralogs, including shared protein-protein interactions and evolutionary conservation. We develop a machine-learning classifier based on these features to predict which paralog pairs are most likely to be synthetic lethal and to explain why. We show that our classifier accurately predicts the results of combinatorial CRISPR screens in cancer cell lines and furthermore can distinguish pairs that are synthetic lethal in multiple cell lines from those that are cell-line specific. A record of this paper's transparent peer review process is included in the supplemental information.

Polθ inhibitors elicit BRCA-gene synthetic lethality and target PARP inhibitor resistance.

To identify approaches to target DNA repair vulnerabilities in cancer, we discovered nanomolar potent, selective, low molecular weight (MW), allosteric inhibitors of the polymerase function of DNA polymerase Polθ, including ART558. ART558 inhibits the major Polθ-mediated DNA repair process, Theta-Mediated End Joining, without targeting Non-Homologous End Joining. In addition, ART558 elicits DNA damage and synthetic lethality in BRCA1- or BRCA2-mutant tumour cells and enhances the effects of a PARP inhibitor. Genetic perturbation screening revealed that defects in the 53BP1/Shieldin complex, which cause PARP inhibitor resistance, result in in vitro and in vivo sensitivity to small molecule Polθ polymerase inhibitors. Mechanistically, ART558 increases biomarkers of single-stranded DNA and synthetic lethality in 53BP1-defective cells whilst the inhibition of DNA nucleases that promote end-resection reversed these effects, implicating these in the synthetic lethal mechanism-of-action. Taken together, these observations describe a drug class that elicits BRCA-gene synthetic lethality and PARP inhibitor synergy, as well as targeting a biomarker-defined mechanism of PARPi-resistance.

Targeting cell cycle regulation via the G2-M checkpoint for synthetic lethality in melanoma.

Disruption of cell cycle checkpoints has been well established as a hallmark of cancer. In particular, the G1-S transition mediated by the cyclin D-cyclin-dependent kinase 4/6 (CDK4/6) pathway is dysregulated in more than 90% of melanoma cases. Therefore, tumor cells mainly rely on the G2-M checkpoint to halt the cell cycle in order to repair DNA damage. Here, we review the promising method of cell cycle-mediated synthetic lethality for melanoma treatment, which entails exploiting somatically acquired mutations in the G1-S transition with inhibitors of the G2-M transition in order to specifically kill melanoma cells. The idea stems from the theory that melanoma cells lacking G1-S checkpoints are particularly vulnerable to mitotic catastrophe when presented with G2-M checkpoint inhibition in addition to DNA damage, whereas normal cells with intact G1-S checkpoints should theoretically be spared. This review explores the link between cell cycle dysregulation and synthetic lethality in melanoma cells and discusses potential future applications for this treatment.