Synthetic Lethal Gene Pairs Research Articles

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.

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.

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.

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.

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.

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/.

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.

Investigation of synthetic lethality gene pairs in the DNA damage response pathway in response to immunotherapy in pan-cancer.

2567 Background: The concept of synthetic lethality (SL) refers to the phenomenon where the simultaneous loss of function of two genes leads to cell death, while the loss of function of either gene alone has no effect. PARP inhibitors target poly ADP ribose polymerase (PARP) enzymes, which are involved in DNA repair, are the successful cases of utilizing synthetic lethality-based therapy to treat cancers with DNA damage response (DDR) deficiency. The prognosis for cancer patients already has a combination of synthetic lethal mutations especially in DDR pathway can vary and it is not yet fully understood how these mutations may impact the response to immunotherapy. Methods: SL gene combinations in the DDR pathway were screened out from SynLethDB database (http://histone.sce.ntu.edu.sg/SynLethDB/). The immunotherapy cohort (Samstein 2019, n=1661) and non-immunotherapy cohort (Zehir 2017, n=10336) with solid tumor were used to Kaplan-Meier analyze the correlation between SL gene combinations and prognosis. TMB scores were compared between DDR co-mutation group and non DDR co-mutation group. For investigating the associations between immune infiltrates and genetic features, TIMER 2.0 database ( http://timer.cistrome.org/ ) was conducted to estimation of immune infiltration levels on DDR mutation genes in pan-cancer. Results: 430 pairs of DDR mutation combinations were used to subgroup stratification, the DDR co-mutation group was associated with worse prognosis in non-immunotherapy cohort (HR=1.305, p<0.0001) but better in immunotherapy cohort (HR=0.741,p<0.0061). Interestingly, the TMB score in the DDR co-mutation group was significantly higher than non DDR co-mutation group (mean 50.03 vs 8.868, p<0.0001) in immunotherapy cohort. The high-frequency mutation combinations (>10%) were ATM-POLE(14.40%), ATM-ATR (13.60%), ATR-BRCA2 (13.60%), ATM-BRCA2 (12.00%) and BRCA2-RECQL4 (10.40%), respectively. Comparing common immune component with statistically significant under different DDR mutant genes, the ATM-POLE co-mutation tumor tended to have more CD4+ T cell infiltration while the ATM-ATR co-mutation tumor appeared to have more myeloid dendritic cell infiltration. Conclusions: The combinations of DDR SL mutation tumor might result to worse prognosis whereas these patients might benefit from immunotherapy. Our results will help to shed light on the complex interplay for the combinations of SL mutation in DDR pathway between cancer cells and the immune system and provide new insights into the development of effective cancer treatments.

Abstract 6581: SLKB: Synthetic lethality knowledge base for gene combination double knockout experiments

Abstract Introduction: Synthetic lethality (SL) of two genes is defined that a cell loses its viability if both genes lost their functions, but it remains viable if losing only one gene. Because of the emerging CRISPR-cas9 technology, a number of cancer cells were screened in gene combination double knockout (CDKO) experiments. These CDKO experiments have been effective in identifying SL gene pairs as potential combination targets in treating cancer. The existing SL database, SynLethDB v2.0, attempted to integrate SL experiments and data, but has several knowledge gaps: (1) missing all non-SL gene pairs; (2) inaccurate cell line annotations; (3) incomprehensive coverage of CDKO experiments; (4) deviation from originally reported SL scores, and most importantly (5) ignoring the difference among various SL calculation methods, including sequence mapping, sequence count processing, sample normalization, background control normalization, and SL score calculation. Methods: SL knowledge base (SLKB) is developed. It includes SL data from 11 published CDKO experiments in 22 cell lines. Between SLKB and SynLethDB, their cell line data and positive/negative SL gene pair data collected from CDKO experiments were compared. Five SL calculation methods are compared: median SL score with and without background control normalization (Median-B/NB), similarly sgRNA-Derived B/NB, Horlbeck-Score, GEMINI, and MAGeCK-based SL score (MAGeCK-Score). We compared their overall overlapping and pairwise overlapping among top 10% SL gene pairs. Results: - For human SL, SynLethDB only included two CDKO experiments, 189 SL gene pairs, and 0 non-SL gene pairs. Our SLKB has 11 CDKO experiments, 22 cell lines, 16,044 SL gene pairs, and 264,444 non-SL gene pairs.. - Among top 10% SL gene pairs from five SL calculation methods, only 0.05% are overlapping. - In pairwise overlapping analysis, Median-B/NB Score has the highest averaging pairwise overlapping with the other four methods, 26.82%. GEMINI has the lowest average pairwise overlapping with the other methods, 5.11%. The averaging overlapping rates for MAGeCK-Score, sgRNA-Derived B/NB Score, and Horlbeck-Score are 24.52%, 18.50%, and 9.89% respectively. Conclusion: SLKB is a much more comprehensive SL database than SynLethDB in CDKO experiment data integration and includes original scorings. There is a significant amount of difference between five SL calculation methods and their SL scores. All five SL scores can be queried and ranked in SLKB. SLKB also documents sufficient technical details of these five methods, allowing users to review and choose proper methods for their data analysis and presentation. Citation Format: Birkan Gökbağ, Shan Tang, Kunjie Fan, Lijun Cheng, Lang Li. SLKB: Synthetic lethality knowledge base for gene combination double knockout experiments. [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 6581.

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.

Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction.

Synthetic lethal (SL) genetic interactions have been regarded as a promising focus for investigating potential targeted therapeutics to tackle cancer. However, the costly investment of time and labor associated with wet-lab experimental screenings to discover potential SL relationships motivates the development of computational methods. Although graph neural network (GNN) models have performed well in the prediction of SL gene pairs, existing GNN-based models are not designed for predicting cancer cell-specific SL interactions that are more relevant to experimental validation in vitro. Besides, neither have existing methods fully utilized diverse graph representations of biological features to improve prediction performance. In this work, we propose MVGCN-iSL, a novel multi-view graph convolutional network (GCN) model to predict cancer cell-specific SL gene pairs, by incorporating five biological graph features and multi-omics data. Max pooling operation is applied to integrate five graph-specific representations obtained from GCN models. Afterwards, a deep neural network (DNN) model serves as the prediction module to predict the SL interactions in individual cancer cells (iSL). Extensive experiments have validated the model's successful integration of the multiple graph features and state-of-the-art performance in the prediction of potential SL gene pairs as well as generalization ability to novel genes.

DSCN: Double-target selection guided by CRISPR screening and network.

Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The ‘diffusion-path’ method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN’s computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.

Abstract 1918: A novel computational framework predicts synthetic lethal interactions between key regulators of the DNA damage response and chromatin modifiers

Abstract The concept of synthetic lethality (SL) has made a pivotal impact for the development of the anti-cancer drug olaparib, the first approved agent targeting the DNA Damage Response (DDR). Typically, SL is described as the interaction of two genes, whereby simultaneous inactivation of both genes results in cell death whereas loss of one gene can be tolerated. Given the importance of SL to develop highly selective anti-cancer therapeutics, large efforts have been undertaken to identify these interactions, both experimentally and computationally. Here, we present a novel computational framework harnessing large-scale cell line gene inactivation screens (DepMap, Project Score), as well as patient data (TCGA), to discover known and novel SL gene pairs. Overall, we implemented six statistical tests considering gene dependency scores, genomic profiles, gene expression and patient survival as parameters. We further utilized data from public drug screening consortia to validate our top-ranking pairs. We applied our framework to a defined target space covering genes relating to DDR, chromatin binding, cell cycle and druggable genes (overall > 2.5 M. pairs were tested). When focusing on SL partners for three DDR genes with promising inhibitors in early clinical development: ATM, ATR and DNA-PK; we noticed that chromatin modifiers were enriched in the top ranking pairs. In particular, SL interactions were predicted with histone (de)methylases, histone (de)acetyltransferases and members of SWI/SNF family. For instance, we observed that loss of function mutations in several members of the KMT2 family, also known as MLL family, would render cancers cells more dependent on ATR or ATM. Similarly, drug sensitivity was increased for selected DDR inhibitors in cell lines with KMT2 mutations. Members of the KMT2 family play essential roles in transcription, but have recently also been shown to be recruited to DNA damage sites and may be mediators for PARP inhibitor sensitivity. The KMT2 family is frequently mutated in several cancers types such as endometrial and bladder, thus underpinning their suitability as potential selection biomarkers as well as relevance to cancer. Furthermore, we observed that patients with mutations in EP300, a histone lysine acetyl transferase, exhibited an increase in ATR expression which may comprise a compensatory mechanism. In addition, patients with ATR and EP300 double mutation tend to have a better probability of survival, suggesting decreased tumor fitness. In summary, we present a modular framework to predict SL gene pairs to a defined target space. Here, we focused on discovering novel SL relationships for the key DDR regulators ATM, ATR and DNA-PK. Our results provide not only new biomarker hypotheses for further validation, but also suggest that cancers with a high mutation rate in chromatin modifying genes may be efficiently targeted by DDRi. Citation Format: Anna M. Coenen-Stass, Magda Markowska, Magdalena Budzinska-Zaniewska, Krzysztof Kolmus, Ewa Szczurek, Eike Staub. A novel computational framework predicts synthetic lethal interactions between key regulators of the DNA damage response and chromatin modifiers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1918.

Abstract 2359: Identifying synergistic master regulators of pancreatic ductal adenocarcinoma tumorigenesis through CRISPR/Cas9 combinatorial genetic screens

Abstract Pancreatic ductal adenocarcinoma (PDAC) is the third-leading cause of cancer-related mortality and is highly resistant to cytotoxic, targeted, and immune therapies. Compared to other cancers, PDAC is remarkable for its relatively uniform set of DNA alterations; unfortunately, these hallmark events are not currently targetable and other mutations known to confer sensitivity to specific drugs are uncommon. Consequently, cytotoxic combinations remain the standard of care, with most patients quickly exhibiting primary or secondary chemoresistance. Previous studies have shown that transcriptional regulatory proteins can form small, highly interconnected and autoregulated modules that can initiate and maintain malignant tumor cell state, and that the Master Regulator (MR) protein they comprise represent a novel class of non-oncogene dependencies. We used the VIPER algorithm to identify MR proteins representing mechanistic regulators of the transcriptional state for 200 laser microdissected PDAC epithelial compartment samples, collected at Columbia University Irving Medical Center (CUIMC). To systematically identify synergistic pairs of MRs, we designed a combinatorial CRISPR-Cas9 screen based on the Big Papi dual SaCas9/SpCas9 system which was used to systematically target all possible pairs of the top 50 MRs identified by VIPER analysis across 6 PDAC cell lines (KP4, PATU8988S, PANC1, HPAFII, BXPC3, MIAPACA2) with three guide-RNAs per gene, thus differentiating between potentially idiosyncratic, cell line-specific and highly conserved genetic interactions. We also developed a novel nonparametric statistical method for inferring genetic interactions from this data based on the use of conditional Mann-Whitney U tests; our new analytical method significantly improved over a previously developed parametric model of synergy inference, demonstrating increased correlation between genetic interactions inferred from biological replicates of the same cell line while simultaneously prioritizing four established synthetic lethal gene pairs in the top 10 gene pairs following integration across all 6 cell lines: BCL2L1-MCL1, BCL2L1-BCL2L2, MAPK1-MAPK3, and SEC23A-SEC23B. The methods also identified 314 additional highly significant (FDR < 0.05), yet never previously reported synthetic lethal interactions that are being validated using an array-based, low-throughput screening platform. We expect that the results of this screen will help elucidate mechanisms controlling the state of PDAC tumor cells and identify potentially druggable gene pairs. Citation Format: Aaron T. Griffin, Mikko Turunen, Lorenzo Tomassoni, Kenneth P. Olive, Pasquale Laise, Andrea Califano. Identifying synergistic master regulators of pancreatic ductal adenocarcinoma tumorigenesis through CRISPR/Cas9 combinatorial genetic screens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2359.

Abstract 5062: Identification of synthetic lethal gene interactions to discover novel cancer vulnerabilities and guide drug development

Abstract Poor treatment responses in patients and drug resistance are main challenges in the clinic which can arise due an incomplete understanding of genetic dependencies within cancers and an ill defined patient population. Identifying synthetic lethal (SL) relationships between a gene pair, where loss of either of the participating genes has no effect on cancer cells, however simultaneous disruption of both genes leads to cancer cell death, can uncover major survival mechanisms and expose vulnerabilities within cancers. In spite of advances in CRISPR or RNAi based loss of function screening it is difficult to identify these gene interactions solely by experimentation due to the vast mutational landscape of cancers, inability of cell lines to capture clinically relevant mutations, and large number of possible SL gene combinations. Furthermore, large scale CRISPR/RNAi screens are prone to false positive results, leading to erroneous identification of gene interactions. We developed a machine learning model to identify SL genetic interactions by integrating pathway, ontology, genomic, and loss of function features on all gene pairs. This included features, which have been shown to be predictive of SL pairs such as, involvement in important cancer hallmark processes or signaling pathways, co-expression profile in patient cancers, and many more. The model was trained using SL pairs from published large scale experimental screens across several cancer types, ultimately enabling the prediction of cancer agnostic SL pairs. Non-SL pairs had to be computationally defined using post-perturbation gene expression profiles due to the lack of published data annotating non-SL pairs. The model showed significant predictive performance (AUROC = 0.94, AUPRC = 0.93, Specificity = 0.91), additionally, when tested on a gold standard, biologically verified set of SL gene pairs across several cancer types, the model showed good generalizability. We leveraged the model to identify key clinically relevant SL relationships and uncover complementary mechanisms tied to pro-survival processes. By identifying SL gene interacting pairs, our platform can help gain a better understanding of dominant mechanisms driving cancers and guide the discovery of novel therapies, biomarkers, and combination therapies early on during drug development. Citation Format: Mukti Parikh, Manuel García-Quismondo, Neel S. Madhukar, Coryandar Gilvary. Identification of synthetic lethal gene interactions to discover novel cancer vulnerabilities and guide drug development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5062.

Roche licenses Repare small molecule

Roche has agreed to license Repare Therapeutics’ camonsertib (also known as RP-3500) for $125 million and up to $1.2 billion in milestone payments. The small molecule is being developed to treat solid tumors on its own as well as in combination with other agents. Repare says camonsertib is the product of its CRISPR-based screening platform for identifying synthetic lethal gene pairs. The company launched in 2017 and went public in 2020.

SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery.

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14 100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs). Database URL: https://synlethdb.sist.shanghaitech.edu.cn/v2.