Synthetic Lethal Pairs Research Articles

CRISPR-GRIT: Guide RNAs with Integrated Repair Templates Enable Precise Multiplexed Genome Editing in the Diploid Fungal Pathogen Candida albicans.

Candida albicans, an opportunistic fungal pathogen, causes severe infections in immunocompromised individuals. Limited classes and overuse of current antifungals have led to the rapid emergence of antifungal resistance. Thus, there is an urgent need to understand fungal pathogen genetics to develop new antifungal strategies. Genetic manipulation of C. albicans is encumbered by its diploid chromosomes requiring editing both alleles to elucidate gene function. Although the recent development of CRISPR-Cas systems has facilitated genome editing in C. albicans, large-scale and multiplexed functional genomic studies are still hindered by the necessity of cotransforming repair templates for homozygous knockouts. Here, we present CRISPR-GRIT (Guide RNAs with Integrated Repair Templates), a repair template-integrated guide RNA design for expedited gene knockouts and multiplexed gene editing in C. albicans. We envision that this method can be used for high-throughput library screens and identification of synthetic lethal pairs in both C. albicans and other diploid organisms with strong homologous recombination machinery.

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.

A protein expression atlas on tissue samples and cell lines from cancer patients provides insights into tumor heterogeneity and dependencies.

The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) are foundational resources in cancer research, providing extensive molecular and phenotypic data. However, large-scale proteomic data across various cancer types for these cohorts remain limited. Here, we expand upon our previous work to generate high-quality protein expression data for approximately 8,000 TCGA patient samples and around 900 CCLE cell line samples, covering 447 clinically relevant proteins, using reverse-phase protein arrays. These protein expression profiles offer profound insights into intertumor heterogeneity and cancer dependency and serve as sensitive functional readouts for somatic alterations. We develop a systematic protein-centered strategy for identifying synthetic lethality pairs and experimentally validate an interaction between protein kinase A subunit α and epidermal growth factor receptor. We also identify metastasis-related protein markers with clinical relevance. This dataset represents a valuable resource for advancing our understanding of cancer mechanisms, discovering protein biomarkers and developing innovative therapeutic strategies.

MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.

Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a lethal phenotype. In SV, inhibiting the vulnerable gene makes the cancer cell sick; inhibiting the partner gene rescues and promotes cell viability. Many low and high-throughput experimental approaches have been employed to identify SLs and SVs, but they are time-consuming and expensive. The computational tools for SL prediction involve statistical and machine-learning approaches. Almost all machine learning tools are binary classifiers and involve only identifying SL pairs. Most importantly, there are limited properties known that best describe and discriminate SL from SV. We developed MAGICAL (Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning), a multi-class random forest based machine learning model for genetic interaction prediction. Network properties of protein derived from physical protein-protein interactions are used as features to classify SL and SV. The model results in an accuracy of ~80% for the training dataset (CGIdb, BioGRID, and SynLethDB) and performs well on DepMap and other experimentally derived reported datasets. Amongst all the network properties, the shortest path, average neighbor2, average betweenness, average triangle, and adhesion have significant discriminatory power. MAGICAL is the first multi-class model to identify discriminatory features of synthetic lethal and viable interactions. MAGICAL can predict SL and SV interactions with better accuracy and precision than any existing binary classifier.

Optimal methods for analyzing targeted pairwise knockout screens.

Synthetic lethality offers a promising strategy for cancer treatment by targeting genetic vulnerabilities unique to tumor cells, leading to selective tumor cell death. However, single-gene knockout screens often miss functional redundancy due to paralog genes. Multiplex CRISPR systems, including various Cas9 and Cas12a platforms, have been developed to assay genetic interactions, yet no systematic comparison of method to identify synthetic lethality from CRISPR screens has been conducted. We evaluated data from four in4mer CRISPR/Cas12a screens in cancer cell lines, using three bioinformatic approaches to identify synthetic lethal interactions: delta log fold change (dLFC), Z-transformed dLFC (ZdLFC), and rescaled dLFC (RdLFC). Both ZdLFC and RdLFC provided more consistent identification of synthetic lethal pairs across cell lines compared to the unscaled dLFC method. The ZdLFC method offers a robust framework for scoring synthetic lethal interactions from paralog screens, providing consistent results across different cell lines without requiring a training set of known positive interactors.

Abstract B016: Discovering novel synthetic lethal relationships with large-scale cellular simulations

Abstract Synthetic Lethality (SL) is a compelling example of a large combinatorial space, with the number of (order-independent) pairwise combinations of human genes totaling approximately 400 million. When further considering higher-order combinations beyond pairs i.e. additional biomarkers that modulate the the efficacy of an SL pair, this space grows exponentially larger still. Understanding these higher-order combinations is crucial for the discovery of synthetic lethal pairs with better defined therapeutic windows and improved segmentation in clinical trials. As the size of this combinatorial space is far greater than can be searched by existing screening technologies, novel approaches are required to efficiently search this space and prioritize higher-order candidate SL combinations for screening. DeepOrigin is developing large-scale dynamical models of cellular systems to drive the in silico discovery of novel therapeutics across a range of diseases. These models can be simulated efficiently and in parallel, and are therefore ideally suited for searching large combinatorial spaces. We have applied our proprietary Cellular Simulation Pipeline to the problem of identifying higher-order combinations in Synthetic Lethality, so far predicting hundreds of novel synthetic lethal pairs. These pairs are currently being validated in vitro, and we present a selection of these results that demonstrate the utility of this approach. This approach could provide both novel therapeutic targets while providing additional stratification criteria for increased clinical study success. Citation Format: Oliver Purcell, Paul Lang, James Komianos, Nimi Vashi. Discovering novel synthetic lethal relationships with large-scale cellular simulations [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 B016.

Abstract PR014: Combinatorial genetic screens to map synthetic lethal interactions and identify new cancer drug targets in KRAS mutant cancers

Abstract The success of precision oncology depends on our ability to translate accumulating genomic data into actionable treatment options in a personalized manner. The first step requires the identification of a specific genomic signature, then matching this signature with the most effective therapy. Unfortunately, more often than not, we do not have a specific cancer drug that is effective for the mutation found in a specific tumor. We desperately need to identify new targets so that we can develop new drugs to deliver on the promise of precision cancer medicine. In addition, we need to develop a rational way to combine drugs that does not depend on trial and error. Combinatorial genetic screening CRISPR-Cas9 now allows us to identify synthetic lethal interactions in which simultaneous perturbation of two genes leads to cell death. However, multiplex gene editing to reveal these synergies between genes is a major challenge. For model systems like yeast, high-throughput methods and technologies have made it possible to create genetic networks with around 23 million double mutants (6000 genes x 6000 genes) and has resulted in the most detailed genetic interaction network to date consisting of ∼900,000 genetic interactions. This landscape of genetic interaction network of yeast has taken the scientific research community 15 years. In human cells, there are ∼20,000 genes and 400 million combinations, making this very complex network impossible to screen and test all these 400M combinations. To systematically interrogate such genetic interactions, we designed a dual CRISPR-Cas9 perturbation library targeting the top 1000 genes upregulated in KRAS mutant cancers (lung, pancreas and colon cancers). We performed these combinatorial double knockout screens on 100K (100x1000) unique gene pairs and identified 27 pairs whose co-disruption results in a loss of cellular fitness. We next validated those top synthetic lethal pairs of genes by performing secondary screens using CRISPR-Cas12a on more KRAS mutant and KRAS WT cell lines. Overall, our findings will provide insight into potential combinational targets in KRAS mutant tumors and will highlight the synthetic lethality effects that occurs among the novel targets, and other proliferation, metastasis, immune and metabolism modules. Citation Format: Rand Arafeh, Laura Chang, Lydia Sawyer, Helen Wang, James McFarland, Joshua Dempster, Peter DeWeirdt, John Doench, William C. Hahn. Combinatorial genetic screens to map synthetic lethal interactions and identify new cancer drug targets in KRAS mutant cancers [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 PR014.

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.

ASTER: A Method to Predict Clinically Relevant Synthetic Lethal Genetic Interactions.

A Synthetic Lethal (SL) interaction is a functional relationship between two genes or functional entities where the loss of either entity is viable but the loss of both is lethal. Such pairs can be used to develop targeted anticancer therapies with fewer side effects and reduced overtreatment. However, finding clinically relevant SL interactions remains challenging. Leveraging unified gene expression data of both disease-free and cancerous samples, we design a new technique based on statistical hypothesis testing, called ASTER, to identify SL pairs. We empirically find that the patterns of mutually exclusivity ASTER finds using genomic and transcriptomic data provides a strong signal of synthetic lethality. For large-scale multiple hypothesis testing, we develop an extension called ASTER++ that can utilize additional input gene features within the hypothesis testing framework. Our computational and functional experiments demonstrate the efficacy of ASTER in identifying SL pairs with potential therapeutic benefits.

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

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. 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. 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. 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. 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).

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.

KR4SL: knowledge graph reasoning for explainable prediction of synthetic lethality.

Synthetic lethality (SL) is a promising strategy for anticancer therapy, as inhibiting SL partners of genes with cancer-specific mutations can selectively kill the cancer cells without harming the normal cells. Wet-lab techniques for SL screening have issues like high cost and off-target effects. Computational methods can help address these issues. Previous machine learning methods leverage known SL pairs, and the use of knowledge graphs (KGs) can significantly enhance the prediction performance. However, the subgraph structures of KG have not been fully explored. Besides, most machine learning methods lack interpretability, which is an obstacle for wide applications of machine learning to SL identification. We present a model named KR4SL to predict SL partners for a given primary gene. It captures the structural semantics of a KG by efficiently constructing and learning from relational digraphs in the KG. To encode the semantic information of the relational digraphs, we fuse textual semantics of entities into propagated messages and enhance the sequential semantics of paths using a recurrent neural network. Moreover, we design an attentive aggregator to identify critical subgraph structures that contribute the most to the SL prediction as explanations. Extensive experiments under different settings show that KR4SL significantly outperforms all the baselines. The explanatory subgraphs for the predicted gene pairs can unveil prediction process and mechanisms underlying synthetic lethality. The improved predictive power and interpretability indicate that deep learning is practically useful for SL-based cancer drug target discovery. The source code is freely available at https://github.com/JieZheng-ShanghaiTech/KR4SL.

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.

Synthetic lethality prediction in DNA damage repair, chromatin remodeling and the cell cycle using multi-omics data from cell lines and patients.

Discovering synthetic lethal (SL) gene partners of cancer genes is an important step in developing cancer therapies. However, identification of SL interactions is challenging, due to a large number of possible gene pairs, inherent noise and confounding factors in the observed signal. To discover robust SL interactions, we devised SLIDE-VIP, a novel framework combining eight statistical tests, including a new patient data-based test iSurvLRT. SLIDE-VIP leverages multi-omics data from four different sources: gene inactivation cell line screens, cancer patient data, drug screens and gene pathways. We applied SLIDE-VIP to discover SL interactions between genes involved in DNA damage repair, chromatin remodeling and cell cycle, and their potentially druggable partners. The top 883 ranking SL candidates had strong evidence in cell line and patient data, 250-fold reducing the initial space of 200K pairs. Drug screen and pathway tests provided additional corroboration and insights into these interactions. We rediscovered well-known SL pairs such as RB1 and E2F3 or PRKDC and ATM, and in addition, proposed strong novel SL candidates such as PTEN and PIK3CB. In summary, SLIDE-VIP opens the door to the discovery of SL interactions with clinical potential. All analysis and visualizations are available via the online SLIDE-VIP WebApp.

Abstract 5310: An expanded quantitative protein expression atlas of human cancers

Abstract The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE) are the most widely used and deeply characterized resources for cancer research. Despite rich molecular and phenotypic data available for these cohorts, large-scale proteomic data across cancer lineages remain limited. Here we expanded our previous effort to generate high-quality protein expression data of 447 clinically relevant proteins for ~8,000 TCGA patient samples and ~900 CCLE cell line samples using reverse phase protein arrays. We show that the protein expression profiles provide deeper mechanistic insights into cancer dependency and serve as a sensitive functional readout for the fitness effect of somatic mutations, e.g., BRAF mutations. We develop a protein-centered strategy to identify synthetic lethality pairs with high confidence. We identify pro- and anti-metastasis protein markers and demonstrate their prognostic relevance in patient cohorts. Collectively, this dataset provides a valuable resource for elucidating cancer mechanisms, identifying protein biomarkers, and developing therapeutic strategies. Citation Format: Han Liang. An expanded quantitative protein expression atlas of human cancers. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5310.

Abstract P2-17-01: Targeted CRISPR screen to identify synthetic lethal combinations between APOBEC3B and DNA repair

Abstract APOBEC-catalyzed deamination of cytosine bases is the largest enzymatic and second largest overall source of mutation in cancer. One member from the APOBEC family of enzymes, APOBEC3B (A3B) is overexpressed and dysregulated in many different cancer types. In addition to hallmark C-to-T transitions and C-to-G transversions, APOBEC-catalyzed uracil lesions can be processed into single- and double-strand DNA breaks. Therefore, A3B-positive tumors are under continual stress to repair DNA breaks and may be vulnerable to DNA repair inhibition. Previous results from the Harris lab have identified UNG2, DNA uracil glycosylase 2 (UNG2), initiator of the base excision repair pathway, as synthetic lethal pair with A3B. The genetic disruption of UNG2 combined with high expression of A3B, causes cell death. This provides the rational to hypothesize that also other DNA repair proteins could be putative synthetic lethal pairs with A3B, when its expression and activity are high. To test this hypothesis, CRISPR guide RNA library targeting 237 DNA damage repair and response genes was used in doxycycline-inducible TREX-293-A3Bi-eGFP cell line. Cells expressing or not A3B were harvested for DNA extraction and sequencing at different time points. Comparison of guide RNA abundance between doxycycline and H2O treated cells revealed the dropout guides that disrupt genes and create putative synthetic lethal combinations with A3B. The screen design and overall results will be presented Citation Format: Bojana Stefanovska, Kevin Lin, Benjamin Troness, Chad Myers, Reuben Harris. Targeted CRISPR screen to identify synthetic lethal combinations between APOBEC3B and DNA repair [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-17-01.

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

Synthetic lethality (SL) is a form of genetic interaction that can selectively kill cancer cells without damaging normal cells. Exploiting this mechanism is gaining popularity in the field of targeted cancer therapy and anticancer drug development. Due to the limitations of identifying SL interactions from laboratory experiments, an increasing number of research groups are devising computational prediction methods to guide the discovery of potential SL pairs. Although existing methods have attempted to capture the underlying mechanisms of SL interactions, methods that have a deeper understanding of and attempt to explain SL mechanisms still need to be developed. In this work, we propose a novel SL prediction method, SLGNN. This method is based on the following assumption: SL interactions are caused by different molecular events or biological processes, which we define as SL-related factors that lead to SL interactions. SLGNN, apart from identifying SL interaction pairs, also models the preferences of genes for different SL-related factors, making the results more interpretable for biologists and clinicians. SLGNN consists of three steps: first, we model the combinations of relationships in the gene-related knowledge graph as the SL-related factors. Next, we derive initial embeddings of genes through an explicit message aggregation process of the knowledge graph. Finally, we derive the final gene embeddings through an SL graph, constructed using known SL gene pairs, utilizing factor-based message aggregation. At this stage, a supervised end-to-end training model is used for SL interaction prediction. Based on experimental results, the proposed SLGNN model outperforms all current state-of-the-art SL prediction methods and provides better interpretability. SLGNN is freely available at https://github.com/zy972014452/SLGNN.

Using graph-based model to identify cell specific synthetic lethal effects

Synthetic lethal (SL) pairs are pairs of genes whose simultaneous loss-of-function results in cell death, while a damaging mutation of either gene alone does not affect the cell’s survival. This makes SL pairs attractive targets for precision cancer therapies, as targeting the unimpaired gene of the SL pair can selectively kill cancer cells that already harbor the impaired gene. Limited by the difficulty of finding true SL pairs, especially on specific cell types, current computational approaches provide only limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. As a result, the identification of SL targets still relies on expensive, time-consuming experimental approaches. In this work, we applied cell-line specific multi-omics data to a specially designed deep learning model to predict cell-line specific SL pairs. Through incorporating multiple types of cell-specific omics data with a self-attention module, we represent gene relationships as graphs. Our approach achieves the prediction of SL pairs in a cell-specific manner and demonstrates the potential to facilitate the discovery of cell-specific SL targets for cancer therapeutics, providing a tool to unearth mechanisms underlying the origin of SL in cancer biology. The code and data of our approach can be found at https://github.com/promethiume/SLwise

Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens

The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions (ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets.