Synthetic Lethal Gene Research Articles

Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort.

Developing innovative precision and personalized cancer therapeutics is essential to enhance cancer survivability, particularly for prevalent cancer types such as colorectal cancer. This study aims to demonstrate various approaches for discovering new targets for precision therapies using artificial intelligence (AI) on a Polish cohort of colorectal cancer patients. We analyzed 71 patients with histopathologically confirmed advanced resectional colorectal adenocarcinoma. Whole exome sequencing was performed on tumor and peripheral blood samples, while RNA sequencing (RNAseq) was conducted on tumor samples. We employed three approaches to identify potential targets for personalized and precision therapies. First, using our in-house neoantigen calling pipeline, ARDentify, combined with an AI-based model trained on immunopeptidomics mass spectrometry data (ARDisplay), we identified neoepitopes in the cohort. Second, based on recurrent mutations found in our patient cohort, we selected corresponding cancer cell lines and utilized knock-out gene dependency scores to identify synthetic lethality genes. Third, an AI-based model trained on cancer cell line data was employed to identify cell lines with genomic profiles similar to selected patients. Copy number variants and recurrent single nucleotide variants in these cell lines, along with gene dependency data, were used to find personalized synthetic lethality pairs. We identified approximately 8,700 unique neoepitopes, but none were shared by more than two patients, indicating limited potential for shared neoantigenic targets across our cohort. Additionally, we identified three synthetic lethality pairs: the well-known APC-CTNNB1 and BRAF-DUSP4 pairs, along with the recently described APC-TCF7L2 pair, which could be significant for patients with APC and BRAF variants. Furthermore, by leveraging the identification of similar cancer cell lines, we uncovered a potential gene pair, VPS4A and VPS4B, with therapeutic implications. Our study highlights three distinct approaches for identifying potential therapeutic targets in cancer patients. Each approach yielded valuable insights into our cohort, underscoring the relevance and utility of these methodologies in the development of precision and personalized cancer therapies. Importantly, we developed a novel AI model that aligns tumors with representative cell lines using RNAseq and methylation data. This model enables us to identify cell lines closely resembling patient tumors, facilitating accurate selection of models needed for in vitro validation.

Multiple Heterogeneous Networks Representation With Latent Space for Synthetic Lethality Prediction.

Computational synthetic lethality (SL) method has become a promising strategy to identify SL gene pairs for targeted cancer therapy and cancer medicine development. Feature representation for integrating various biological networks is crutial to improve the identification performance. However, previous feature representation, such as matrix factorization and graph neural network, projects gene features onto latent variables by keeping a specific geometric metric. There is a lack of models of gene representational latent space with considerating multiple dimentionalities correlation and preserving latent geometric structures in both sample and feature spaces. Therefore, we propose a novel method to model gene Latent Space using matrix Tri-Factorization (LSTF) to obtain gene representation with embedding variables resulting from the potential interpretation of synthetic lethality. Meanwhile, manifold subspace regularization is applied to the tri-factorization to capture the geometrical manifold structure in the latent space with gene PPI functional and GO semantic embeddings. Then, SL gene pairs are identified by the reconstruction of the associations with gene representations in the latent space. The experimental results illustrate that LSTF is superior to other state-of-the-art methods. Case study demonstrate the effectiveness of the predicted SL associations.

BioPathNet: Enhancing Link Prediction in Biomedical Knowledge Graphs through Path Representation Learning.

Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimer's, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.

Synthetic lethality of combined ULK1 defection and p53 restoration induce pyroptosis by directly upregulating GSDME transcription and cleavage activation through ROS/NLRP3 signaling

BackgroundHigh expression of ubiquitin ligase MDM2 is a primary cause of p53 inactivation in many tumors, making it a promising therapeutic target. However, MDM2 inhibitors have failed in clinical trials due to p53-induced feedback that enhances MDM2 expression. This underscores the urgent need to find an effective adaptive genotype or combination of targets.MethodsKinome-wide CRISPR/Cas9 knockout screen was performed to identify genes that modulate the response to MDM2 inhibitor using TP53 wild type cancer cells and found ULK1 as a candidate. The MTT cell viability assay, flow cytometry and LDH assay were conducted to evaluate the activation of pyroptosis and the synthetic lethality effects of combining ULK1 depletion with p53 activation. Dual-luciferase reporter assay and ChIP-qPCR were performed to confirm that p53 directly mediates the transcription of GSDME and to identify the binding region of p53 in the promoter of GSDME. ULK1 knockout / overexpression cells were constructed to investigate the functional role of ULK1 both in vitro and in vivo. The mechanism of ULK1 depletion to activate GSMDE was mainly investigated by qPCR, western blot and ELISA.ResultsBy using high-throughput screening, we identified ULK1 as a synthetic lethal gene for the MDM2 inhibitor APG115. It was determined that deletion of ULK1 significantly increased the sensitivity, with cells undergoing typical pyroptosis. Mechanistically, p53 promote pyroptosis initiation by directly mediating GSDME transcription that induce basal-level pyroptosis. Moreover, ULK1 depletion reduces mitophagy, resulting in the accumulation of damaged mitochondria and subsequent increasing of reactive oxygen species (ROS). This in turn cleaves and activates GSDME via the NLRP3-Caspase inflammatory signaling axis. The molecular cascade makes ULK1 act as a crucial regulator of pyroptosis initiation mediated by p53 activation cells. Besides, mitophagy is enhanced in platinum-resistant tumors, and ULK1 depletion/p53 activation has a synergistic lethal effect on these tumors, inducing pyroptosis through GSDME directly.ConclusionOur research demonstrates that ULK1 deficiency can synergize with MDM2 inhibitors to induce pyroptosis. p53 plays a direct role in activating GSDME transcription, while ULK1 deficiency triggers upregulation of the ROS-NLRP3 signaling pathway, leading to GSDME cleavage and activation. These findings underscore the pivotal role of p53 in determining pyroptosis and provide new avenues for the clinical application of p53 restoration therapies, as well as suggesting potential combination strategies.

Path-based reasoning for biomedical knowledge graphs with BioPathNet.

Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimer's, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.

Harnessing DNA replication stress to target RBM10 deficiency in lung adenocarcinoma

The splicing factor RNA-binding motif protein 10 (RBM10) is frequently mutated in lung adenocarcinoma (LUAD) (9-25%). Most RBM10 cancer mutations are loss-of-function, correlating with increased tumorigenesis and limiting the efficacy of current LUAD targeted therapies. Remarkably, therapeutic strategies leveraging RBM10 deficiency remain unexplored. Here, we conduct a CRISPR-Cas9 synthetic lethality (SL) screen and identify ~60 RBM10 SL genes, including WEE1 kinase. WEE1 inhibition sensitizes RBM10-deficient LUAD cells in-vitro and in-vivo. Mechanistically, we identify a splicing-independent role of RBM10 in regulating DNA replication fork progression and replication stress response, which underpins RBM10-WEE1 SL. Additionally, RBM10 interacts with active DNA replication forks, relying on DNA Primase Subunit 1 (PRIM1) that synthesizes Okazaki RNA primers. Functionally, we demonstrate that RBM10 serves as an anchor for recruiting Histone Deacetylase 1 (HDAC1) to facilitate H4K16 deacetylation and R-loop homeostasis to maintain replication fork stability. Collectively, our data reveal a role of RBM10 in fine-tuning DNA replication and provide therapeutic arsenal for targeting RBM10-deficient tumors.

G6PD and ACSL3 are synthetic lethal partners of NF2 in Schwann cells

Neurofibromatosis Type II (NFII) is a genetic condition caused by loss of the NF2 gene, resulting in activation of the YAP/TAZ pathway and recurrent Schwann cell tumors, as well as meningiomas and ependymomas. Unfortunately, few pharmacological options are available for NFII. Here, we undertake a genome-wide CRISPR/Cas9 screen to search for synthetic-lethal genes that, when inhibited, cause death of NF2 mutant Schwann cells but not NF2 wildtype cells. We identify ACSL3 and G6PD as two synthetic-lethal partners for NF2, both involved in lipid biogenesis and cellular redox. We find that NF2 mutant Schwann cells are more oxidized than control cells, in part due to reduced expression of genes involved in NADPH generation such as ME1. Since G6PD and ME1 redundantly generate cytosolic NADPH, lack of either one is compatible with cell viability, but not down-regulation of both. Since genetic deficiency for G6PD is tolerated in the human population, G6PD could be a good pharmacological target for NFII.

Abstract IA001: Exploiting pathway activation as a new form of synthetic lethality

Abstract Targeting activated oncogenes is an effective treatment strategy across many cancers now including therapeutics targeting KRAS. Genetic events including DNA damage deficiencies and tumor suppressor mutations require alternative strategies and the concept of synthetic lethality has been applied to these alterations. PARPi inhibitors were a founding member of this class of therapeutics demonstrating enhanced activity in the setting of BRCA-deficiency. By applying large-scale loss-of-function approaches1 we and other discovered the vulnerability to PRMT5 and WRN inhibition imposed by co-deletions of MTAP and CDKN2A, and the MSI+ genotype respectively2,3. Such inhibitors are now in clinical trials. To explore the potential for paralogous genes to act as synthetic lethal gene pairs we enacted dual knockout screens. These early efforts led to the discovery of DUSP4/6 paralog dependence in the setting of BRAF and NRAS mutations in melanoma4. Intriguingly the loss of DUSP4/6 impaired cancer cell viability through the increased activation of ERK highlighting the susceptibility of cancers to pathway activation in addition to the more common sensitivity to pathway inhibition. This inappropriate activation of the ERK signaling pathway, the conflict between EGFR and KRAS activation, the synthetic lethality enacted by TRIM8 knockouts, and the effects of AR agonists on prostate cancer viability, points to a wider than expected vulnerability of cancer to inappropriate gene activation. To systematically identify context-specific gene activation induced lethalities in cancer, we developed methods for enacting gain-of-function perturbations across ∼500 barcoded cancer cell lines. With this approach, we queried the pan-cancer vulnerability landscape upon activating 10 key cancer pathway revealing activation dependencies in MAPK and PI3K pathways. Notably, we discovered novel pathway hyperactivation dependencies in subsets of APC-mutant colorectal cancers where further activation of the WNT pathway by APC knockdown or direct β-catenin overexpression led to robust antitumor effects in xenograft and patient-derived organoid models. These latter discoveries paradoxically point to the residual activity of the APC ubiquitin-ligase complex as a target in APC-mutant CRC5. 1. McDonald, E. R., 3rd et al. Project DRIVE: A Compendium of Cancer Dependencies and Synthetic Lethal Relationships Uncovered by Large-Scale, Deep RNAi Screening. Cell 170, 577-592.e10 (2017). 2. Mavrakis, K. J. et al. Disordered methionine metabolism in MTAP/CDKN2A-deleted cancers leads to dependence on PRMT5. Science 351, 1208–1213 (2016). 3. Chan, E. M. et al. WRN helicase is a synthetic lethal target in microsatellite unstable cancers. Nature 568, 551–556 (2019). 4. Ito, T. et al. Paralog knockout profiling identifies DUSP4 and DUSP6 as a digenic dependence in MAPK pathway-driven cancers. Nat. Genet. 53, 1664–1672 (2021). 5. Chang, L. et al. Systematic profiling of conditional pathway activation identifies contextdependent synthetic lethalities. Nat. Genet. 55, 1709–1720 (2023). Citation Format: William R. Sellers. Exploiting pathway activation as a new form of synthetic lethality [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr IA001.

Abstract 5813: AI-driven identification and validation of novel synthetic lethal gene pairs through deep mining of cancer dependency data

Abstract The targeting of synthetic lethal (SL) gene pairs is emerging as a new paradigm in precision oncology, with the potential to deliver treatments with higher efficacy and fewer side-effects. However, existing approaches have focused on a few well-validated pairs, and there is a clear need to expand the toolbox of potential SL targets. We have performed deep mining of cancer dependency data using a suite of machine learning tools and AI algorithms underpinned by robust statistical analysis to identify the next generation of SL gene pairs. To ensure robust, high confidence validation of multiple SL pairs simultaneously, we have used three cell lines and orthogonal assay formats. This has led to the confirmation of new druggable SL genes suitable for clinical development. We have identified the cancer subtypes with the strongest evidence of SL vulnerabilities and quantified patient populations who would most benefit from novel therapeutic agents. For one gene pair, we have generated novel best-in-class inhibitors of the target of interest, with exceptional selectivity over the SL partner, and used these chemical tools to validate the SL relationship in 2D and 3D models. Citation Format: Daniel S. Miller, Oliver Vipond, Alfie Brennan, Anna Hercot. AI-driven identification and validation of novel synthetic lethal gene pairs through deep mining of cancer dependency data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5813.

Abstract 5963: VGLL1-derived peptides demonstrated anticancer effect by inhibiting VGLL1-TEAD4 interaction

Abstract YAP/TAZ, cofactors of TEA domain-containing transcription factor TEADs (TEAD1-4), play a key role in the HIPPO pathway, which is involved in various function including embryonic development, organ size, cell proliferation, tissue regeneration, and the development and progression of several cancers. YAP/TAZ is therefore considered as master regulators of various cancers. However, we are interested in VGLL1, a cofactor of TEAD4, because it is mainly involved in the proliferation and metastasis of various cancers, including gastric, breast, pancreatic, and ovarian cancers. In gastric cancer, VGLL1 expression is regulated by the PI3K/AKT/β-catenin pathway. VGLL1 is then phosphorylated at S84 by TGF-β/ribosomal S6 kinase 2 (RSK2) and interacts with TEAD4 to express target genes such as MMP9, cyclin D1, IGFBP3, IGFBP5 and ITGαV. which are involved in cancer malignancy. We designed S84-containing VGLL1-derived peptides (VGLL1S84 peptides, SCVP), which consist of 7 to 15 amino acids linked to CPP, a cell-penetration peptide. We evaluated SCVP to inhibit the VGLL1-TEAD4 interaction and their therapeutic potential as anticancer agents. SCVP suppressed the growth of gastric and breast cancer cells expressing high levels of VGLL1, EGFR, and/or HER2. In the RNA-seq analysis of NUGC3 to elucidate the molecular mechanisms of SCVP induced cell death, SCVP-treated cells showed gene expression changes in metabolic pathway, RAS signaling, PI3K-AKT signaling, Wnt signaling, TNF signaling, and mTOR signaling. However, RNA-seq analysis of breast cancer cells treated with SCVP showed changes in the expression of genes involved in cell cycle, DNA replication, DNA repair, and apoptosis. Then, we performed CRISPR/Cas9 screening using SCVP and sgRNA library to explore companion biomarkers and genetic interactors of VGLL1 for targeted cancer therapy. As a result, we identified several candidate genes, including ZNF292 and PLK1, as synthetic lethal genes of VGLL1. As expected, HCC70 cells with ZNF292 mutation showed increased sensitivity to SCVP. We also found that SCVP in combination and the PLK inhibitor volasertib resulted in a synergistic effect on the growth inhibition of breast cancer cells. In conclusion, we demonstrated VGLL1 as a novel target for anticancer drug development, and elucidated the molecular mechanism and therapeutic potential of the SCVP in gastric and breast cancers. Citation Format: Sangmi Ock, Changsub Lee, Wongi Jung, Ina Whang, Kyung-Soo Kim, Jongmin Han, Sujin Yim, Bo-Kyung Kim, Hong-Sub Lee, Kwan-Young Chang, Misun Won. VGLL1-derived peptides demonstrated anticancer effect by inhibiting VGLL1-TEAD4 interaction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5963.

ELISL: early-late integrated synthetic lethality prediction in cancer.

Anti-cancer therapies based on synthetic lethality (SL) exploit tumour vulnerabilities for treatment with reduced side effects, by targeting a gene that is jointly essential with another whose function is lost. Computational prediction is key to expedite SL screening, yet existing methods are vulnerable to prevalent selection bias in SL data and reliant on cancer or tissue type-specific omics, which can be scarce. Notably, sequence similarity remains underexplored as a proxy for related gene function and joint essentiality. We propose ELISL, Early-Late Integrated SL prediction with forest ensembles, using context-free protein sequence embeddings and context-specific omics from cell lines and tissue. Across eight cancer types, ELISL showed superior robustness to selection bias and recovery of known SL genes, as well as promising cross-cancer predictions. Co-occurring mutations in a BRCA gene and ELISL-predicted pairs from the HH, FGF, WNT, or NEIL gene families were associated with longer patient survival times, revealing therapeutic potential. Data: 10.6084/m9.figshare.23607558 & Code: github.com/joanagoncalveslab/ELISL.

Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer.

Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.

SLKB: synthetic lethality knowledge base.

Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases limit their utility, and diverse methods of calculating SL scores prohibit their comparison. To overcome these shortcomings, we have developed SL knowledge base (SLKB) that incorporates data of 11 CDKO experiments in 22 cell lines, 16,059 SL gene pairs and 264,424 non-SL gene pairs. Additionally, within SLKB, we have implemented five SL calculation methods: median score with and without background control normalization (Median-B/NB), sgRNA-derived score (sgRNA-B/NB), Horlbeck score, GEMINI score and MAGeCK score. The five scores have demonstrated a mere 1.21% overlap among their top 10% SL gene pairs, reflecting high diversity. Users can browse SL networks and assess the impact of scoring methods using Venn diagrams. The SL network generated from all data in SLKB shows a greater likelihood of SL gene pair connectivity with other SL gene pairs than non-SL pairs. Comparison of SL networks between two cell lines demonstrated greater likelihood to share SL hub genes than SL gene pairs. SLKB website and pipeline can be freely accessed at https://slkb.osubmi.org and https://slkb.docs.osubmi.org/, respectively.

Genome-Wide CRISPR Screens Identify Multiple Synthetic Lethal Targets That Enhance KRASG12C Inhibitor Efficacy.

Identification of synthetic lethal genes with KRASG12C using genome-wide CRISPR/Cas9 screening and credentialing of the ability of TEAD inhibition to enhance KRASG12C efficacy provides a roadmap for combination strategies. See related commentary by Johnson and Haigis, p. 4005.

MAGCN: A Multiple Attention Graph Convolution Networks for Predicting Synthetic Lethality.

Synthetic lethality (SL) is a potential cancer therapeutic strategy and drug discovery. Computational approaches to identify synthetic lethality genes have become an effective complement to wet experiments which are time consuming and costly. Graph convolutional networks (GCN) has been utilized to such prediction task as be good at capturing the neighborhood dependency in a graph. However, it is still a lack of the mechanism of aggregating the complementary neighboring information from various heterogeneous graphs. Here, we propose the Multiple Attention Graph Convolution Networks for predicting synthetic lethality (MAGCN). First, we obtain the functional similarity features and topological structure features of genes from different data sources respectively, such as Gene Ontology data and Protein-Protein Interaction. Then, graph convolutional network is utilized to accumulate the knowledge from neighbor nodes according to synthetic lethal associations. Meanwhile, we propose a multiple graphs attention model and construct a multiple graphs attention network to learn the contribution factors of different graphs to generate embedded representation by aggregating these graphs. Finally, the generated feature matrix is decoded to predict potential synthetic lethal interaction. Experimental results show that MAGCN is superior to other baseline methods. Case study demonstrates the ability of MAGCN to predict human SL gene pairs.

Engineered Extracellular Vesicle-Delivered CRISPR/Cas9 for Radiotherapy Sensitization of Glioblastoma.

Radiotherapy is a mainstay of glioblastoma (GBM) treatment; however, the development of therapeutic resistance has hampered the efficacy of radiotherapy, suggesting that additional treatment strategies are needed. Here, an in vivo loss-of-function genome-wide CRISPR screen was carried out in orthotopic tumors in mice subjected to radiation treatment to identify synthetic lethal genes associated with radiotherapy. Using functional screening and transcriptome analyses, glutathione synthetase (GSS) was found to be a potential regulator of radioresistance through ferroptosis. High GSS levels were closely related to poor prognosis and relapse in patients with glioma. Mechanistic studies demonstrated that GSS was associated with the suppression of radiotherapy-induced ferroptosis in glioma cells. The depletion of GSS resulted in the disruption of glutathione (GSH) synthesis, thereby causing the inactivation of GPX4 and iron accumulation, thus enhancing the induction of ferroptosis upon radiotherapy treatment. Moreover, to overcome the obstacles to broad therapeutic translation of CRISPR editing, we report a previously unidentified genome editing delivery system, in which Cas9 protein/sgRNA complex was loaded into Angiopep-2 (Ang) and the trans-activator of the transcription (TAT) peptide dual-modified extracellular vesicle (EV), which not only targeted the blood-brain barrier (BBB) and GBM but also permeated the BBB and penetrated the tumor. Our encapsulating EVs showed encouraging signs of GBM tissue targeting, which resulted in high GSS gene editing efficiency in GBM (up to 67.2%) with negligible off-target gene editing. These results demonstrate that a combination of unbiased genetic screens, and CRISPR-Cas9-based gene therapy is feasible for identifying potential synthetic lethal genes and, by extension, therapeutic targets.

Genomics of deletion 7 and 7q in myeloid neoplasm: from pathogenic culprits to potential synthetic lethal therapeutic targets

Complete or partial deletions of chromosome 7 (-7/del7q) belong to the most frequent chromosomal abnormalities in myeloid neoplasm (MN) and are associated with a poor prognosis. The disease biology of -7/del7q and the genes responsible for the leukemogenic properties have not been completely elucidated. Chromosomal deletions may create clonal vulnerabilities due to haploinsufficient (HI) genes contained in the deleted regions. Therefore, HI genes are potential targets of synthetic lethal strategies. Through the most comprehensive multimodal analysis of more than 600 -7/del7q MN samples, we elucidated the disease biology and qualified a list of most consistently deleted and HI genes. Among them, 27 potentially synthetic lethal target genes were identified with the following properties: (i) unaffected genes by hemizygous/homozygous LOF mutations; (ii) prenatal lethality in knockout mice; and (iii) vulnerability of leukemia cells by CRISPR and shRNA knockout screens. In -7/del7q cells, we also identified 26 up or down-regulated genes mapping on other chromosomes as downstream pathways or compensation mechanisms. Our findings shed light on the pathogenesis of -7/del7q MNs, while 27 potential synthetic lethal target genes and 26 differential expressed genes allow for a therapeutic window of -7/del7q.

Mslar: Microbial synthetic lethal and rescue database.

Synthetic lethality (SL) occurs when mutations in two genes together lead to cell or organism death, while a single mutation in either gene does not have a significant impact. This concept can also be extended to three or more genes for SL. Computational and experimental methods have been developed to predict and verify SL gene pairs, especially for yeast and Escherichia coli. However, there is currently a lack of a specialized platform to collect microbial SL gene pairs. Therefore, we designed a synthetic interaction database for microbial genetics that collects 13,313 SL and 2,994 Synthetic Rescue (SR) gene pairs that are reported in the literature, as well as 86,981 putative SL pairs got through homologous transfer method in 281 bacterial genomes. Our database website provides multiple functions such as search, browse, visualization, and Blast. Based on the SL interaction data in the S. cerevisiae, we review the issue of duplications' essentiality and observed that the duplicated genes and singletons have a similar ratio of being essential when we consider both individual and SL. The Microbial Synthetic Lethal and Rescue Database (Mslar) is expected to be a useful reference resource for researchers interested in the SL and SR genes of microorganisms. Mslar is open freely to everyone and available on the web at http://guolab.whu.edu.cn/Mslar/.

Paralog-based synthetic lethality: rationales and applications.

Tumor cells can result from gene mutations and over-expression. Synthetic lethality (SL) offers a desirable setting where cancer cells bearing one mutated gene of an SL gene pair can be specifically targeted by disrupting the function of the other genes, while leaving wide-type normal cells unharmed. Paralogs, a set of homologous genes that have diverged from each other as a consequence of gene duplication, make the concept of SL feasible as the loss of one gene does not affect the cell's survival. Furthermore, homozygous loss of paralogs in tumor cells is more frequent than singletons, making them ideal SL targets. Although high-throughput CRISPR-Cas9 screenings have uncovered numerous paralog-based SL pairs, the unclear mechanisms of targeting these gene pairs and the difficulty in finding specific inhibitors that exclusively target a single but not both paralogs hinder further clinical development. Here, we review the potential mechanisms of paralog-based SL given their function and genetic combination, and discuss the challenge and application prospects of paralog-based SL in cancer therapeutic discovery.

Role of synthetic lethal mutations for the immune response: Exploratory studies of two non-small cell lung cancer cohorts and two pan-cancer cohorts.

e21137 Background: Synthetic lethality (SL) is a phenomenon in which mutations in either of two genes do not affect cell survival, but abnormalities in both genes lead to cell death. However, it is little known whether synthetic lethality affects the response of immunotherapy. Methods: Synthetic lethal gene pairs come from the SynLethDB database, which provides a webserver to calculate the confidence scores for each SL pair by integrating individual scores derived from different evidence sources. The analysis included two immunotherapy cohorts of non-small cell lung cancer (Chan, 2015 and Hellmann, 2018) and two solid tumor cohorts (Morris, 2019 and Berger, 2020) in the published literature. Synthetic lethal gene pairs were defined as genes with a score > 0.5. The patients with synthetic lethal gene pairs were divided into one subgroup (SL group), and the other patients were divided into another subgroup (non-SL group). Kaplan-Meier analysis was used to determine progression-free (PFS) and overall survival (OS). Results: Overall, four public cohorts with 12271 patients were included in the analysis. In the cohort of Chan 2015, a total of 34 patients with non-small cell lung cancer (NSCLC) received immunotherapy. Among them, 21 patients were in the SL group and 13 were in the non-SL group, with the former having a significantly longer PFS (p = 0.0345). In the other cohort of immunotherapy (Hellmann, 2018), 90 (37.5%) patients with NSCLC were in SL group. PFS for patients with SL group was significantly longer than those in non-SL group (p = 0.0261). In cohorts with pan-cancer, patients in Morris 2019 cohort (n = 1661) received immunotherapy and patients in Berger 2020 cohort (n = 10336) received other kinds of treatment. A better prognosis was observed in patients with the immunotherapy in SL group than non-SL group (p < 0.0001). In contrast, patients with other kinds of treatment had a significantly worse prognosis in SL group than non-SL group (p < 0.0001). Conclusions: Our results suggested that synthetic lethal gene pairs containing scores greater than 0.5 may be associated with higher progression-free survival in patients with various types of tumors. The specific reasons for the impact still need to be further explored.