Synthetic Lethal Gene Research Articles

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.

Genome-wide CRISPR Screen Reveals RAB10 as a Synthetic Lethal Gene in Colorectal and Pancreatic Cancers Carrying SMAD4 Loss.

This study identified and validated RAB10 as new synthetic lethal gene with SMAD4. This was achieved by conducting a whole-genome CRISPR screens in different colorectal and pancreatic cell lines. A future RAB10 inhibitors could correspond to a new therapeutic solution for patients with cancer with SMAD4 deletion.

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 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.

Multi-omics characterization of synthetic lethality-related molecular features: implications for SL-based therapeutic target screening.

Synthetic lethality (SL) represents the co-occurrence of two or more non-lethal disordered genes that could lead to cell death. SL-based anticancer therapeutics could specifically kill the cancer cells carrying the targeted mutated gene while leaving normal cells alive. Recent large-scale computational and experimental screenings provide rich resources of SL information while lacking systematic research on molecular features of SL genes. Combined with comprehensive multi-omics data analysis and experimental validation of one SL gene pair, Guo etal. portrayed a systematic layout of cancer-specific SL interactions that could improve understanding of carcinogenesis and potentially assist the subsequent screening of anticancer therapeutic targets.

Disruption of Intracellular Metal Homeostasis As a Vulnerability in SF3B1 Mutated AML

Acute Myeloid Leukemia (AML) is a heterogeneous disease in which outcome is highly dependent on cytogenetics and mutational profile. Adverse risk AML, recently defined and updated in the 2022 European LeukemiaNet (ELN) genetic risk classification, is associated with a poor overall survival. To identify novel therapeutic approaches for AML, we screened a library of 10,000 compounds across 56 genetically diverse primary AML specimens. We then looked for active compounds with increased activity towards adverse risk AML samples and with low cytotoxicity to normal hematopoietic cells. Among the hits identified, we were able to validate that S767 molecule is preferentially active on adverse risk AML samples. Additionally, we found a strong association between SF3B1 mutations and sensitivity to S767. To gain insights into S767 mechanism of action, we performed a CRISPR/Cas9 genome-wide functional screen, and identified multiples genes involved in mitochondrial processes both in synthetic rescue and synthetic lethal genes. In particular, knock-down of enzymes involved in heme biogenesis and iron-sulfur cluster biosynthesis appear to interfere with S767 cytotoxic activity. Given the central role of iron in these two synthetic pathways, and the structure of S767, we hypothesized that our molecule binds and disrupts intracellular iron homeostasis. Using UV-spectrometry analysis, we determined that S767 has metal affinity and binds to Fe2+, but also Cu2+ and Zn2+. We then quantified intracellular metal concentrations by ICP-MS (inductively coupled plasma mass spectrometry) and found that S767 exposure significantly decreases intracellular iron concentrations while increasing that of copper. Since metals are critical cellular cofactors involved in multiple enzymatic reactions, disruption of intracellular metal homeostasis can affect many processes. Mitochondrial respiratory chain is particularly sensitive to these perturbations as copper and iron-sulfur clusters are present in complexes I-IV of the electron transport chain and are essential for mitochondrial complexes function. By measuring oxygen consumption rate in leukemic cells exposed to S767, we found that the molecule indeed disrupts mitochondrial respiration. SF3B1 is a splicing factor recurrently mutated in myeloid malignancies including myelodysplastic syndromes (MDS) and AML. Heterozygous mutations typically affect SF3B1 splicing functions and induces consequently mis-splicing of target mRNAs, including the ABCB7 iron-sulfur cluster transporter. Analysis of the Leucegene transcriptomic data of primary AML specimens revealed mis-splicing and downregulation of ABCB7 in SF3B1 mutated patients. Strikingly, ABCB7 was one of the top synthetic lethal gene identified by chemogenomic CRISPR/Cas9 screen following exposure to S767 molecule. Given that ABCB7 exports iron-sulfur clusters out of the mitochondria to be assembled by cytosolic proteins and plays a role in iron homeostasis, we hypothesized that its aberrant expression in SF3B1 mutated AML participates to the high sensitivity of these leukemias to S767. We thus identified a novel molecule with selective activity in AML. Our data suggest that S767 is not a protein-targeting molecule but rather acts through the disruption of intracellular metal homeostasis, and ultimately affects mitochondrial respiration. Its mechanism of action highlighted a vulnerability in SF3B1 mutated AML which present a defect in ABCB7 expression. Importantly, SF3B1 mutations are now recognized in the 2022 ELN risk classification as an adverse prognostic marker if they are associated with intermediate or adverse risk AML, giving a unique opportunity to develop novel therapeutic approaches for this subgroup of patients with poor clinical outcome. In addition, SF3B1 mutations are found in 25% of MDS cases, which are associated with ring sideroblasts and accumulation of mitochondrial iron, and that could also benefit from such therapeutic approach.

(-)-Epigallocatechin Gallate is a Noncompetitive Inhibitor of NAD Kinase.

Nicotinamide adenine dinucleotide kinase (NADK) controls the intracellular NADPH content and provides reducing power for the synthesis of macromolecules and anti-ROS. Moreover, NADK is considered to be a synthetic lethal gene for KRAS mutations. To discover NADK-targeted probes, a high-throughput screening assay was established and optimized with a Z factor of 0.71. The natural product (-)-epigallocatechin gallate (EGCG) was found to be a noncompetitive inhibitor of NADK with K i = 3.28 ± 0.32 μΜ. The direct binding of EGCG to NADK was determined by several biophysical methods, including NMR spectroscopy, surface plasmon resonance (SPR) assay, and hydrogen-deuterium exchange mass spectrometry (HDX-MS). The SPR assay showed a K d of 1.78 ± 1.15 μΜ. The HDX-MS experiment showed that EGCG was bound at the non-substrate-binding sites of NADK. Besides, binding mode prediction and derivative activity analysis revealed a potential structure-activity relationship between EGCG and NADK. Furthermore, EGCG can specifically inhibit the proliferation of KRAS-mutated lung cancer cell lines without affecting KRAS wild-type lung cancer cell lines.

NSF4SL: negative-sample-free contrastive learning for ranking synthetic lethal partner genes in human cancers.

Detecting synthetic lethality (SL) is a promising strategy for identifying anti-cancer drug targets. Targeting SL partners of a primary gene mutated in cancer is selectively lethal to cancer cells. Due to high cost of wet-lab experiments and availability of gold standard SL data, supervised machine learning for SL prediction has been popular. However, most of the methods are based on binary classification and thus limited by the lack of reliable negative data. Contrastive learning can train models without any negative sample and is thus promising for finding novel SLs. We propose NSF4SL, a negative-sample-free SL prediction model based on a contrastive learning framework. It captures the characteristics of positive SL samples by using two branches of neural networks that interact with each other to learn SL-related gene representations. Moreover, a feature-wise data augmentation strategy is used to mitigate the sparsity of SL data. NSF4SL significantly outperforms all baselines which require negative samples, even in challenging experimental settings. To the best of our knowledge, this is the first time that SL prediction is formulated as a gene ranking problem, which is more practical than the current formulation as binary classification. NSF4SL is the first contrastive learning method for SL prediction and its success points to a new direction of machine-learning methods for identifying novel SLs. Our source code is available at https://github.com/JieZheng-ShanghaiTech/NSF4SL. Supplementary data are available at Bioinformatics online.

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.

Pervasive conditional selection of driver mutations and modular epistasis networks in cancer.

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistasis and quantifying its effect on tumor evolution remains a challenge. We develop a method (Coselens) to quantify conditional selection on the excess of nonsynonymous substitutions in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identify 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection affects 25%-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario where gene-specific across-pathway epistasis shapes differentiated cancer subtypes.

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.

Abstract 1805: RAB10, a new target to tackle SMAD4 deficient cancers

Abstract TGFb pathway is an important signaling process that regulates the homeostasis of epithelial, stromal or immune cells. Misregulations of this pathway contribute to different pathologies such as cancer, fibrosis, nash or inflammatory diseases. SMAD4 genetic alterations, either via genomic loss or via mutations, can be found in many cancer types. SMAD4 is mutated or deleted in 10-15% of colorectal cancers and in 20-50% of pancreatic cancer. The poorer prognosis of colorectal cancers and pancreatic cancers patients harboring SMAD4 alterations makes SMAD4 deficiency interesting to harness for therapy. Anticancer opportunity created by loss of SMAD4 function/protein could be addressed by identifying synthetic lethal genes that encode for druggable proteins. CRISPR/CAS9 is a revolutionary discovery to produce gene knockout. CRISPR/CAS9 viability screen is a tool of choice to pinpoint new synthetic lethality interactions. In this work, to extend the genetic vulnerabilities of SMAD4-deficient tumors, we conducted a pooled genome-wide loss of function CRISPR/Cas9 screen in sets of 4 non-isogenic colorectal and pancreatic cancers cancer cell lines harboring wild-type and mutant SMAD4. We identified known and novel genetic alterations that make SMAD4-altered colorectal cancers & pancreatic cancers cells vulnerable. We will present the validation of the Ras-related protein Rab-10 (RAB10) as synthetic lethal gene with SMAD4 alteration. Citation Format: Christophe Marcireau, Hélène Erasimus, Vanessa Kolnik, Frederic Lacroix, Stephane D’agostino, Olivier Lemaitre, Alexandre Rohaut, Isabelle sanchez, Sukhvinder Sidhu, Gilbert Thill, Laurent Debussche, Michel Didier. RAB10, a new target to tackle SMAD4 deficient cancers [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 1805.

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 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.

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.

CRISPR screens uncover protective effect of PSTK as a regulator of chemotherapy-induced ferroptosis in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is among the most common forms of cancer and is associated with poor patient outcomes. The emergence of therapeutic resistance has hampered the efficacy of targeted treatments employed to treat HCC patients to date. In this study, we conducted a series of CRISPR/Cas9 screens to identify genes associated with synthetic lethality capable of improving HCC patient clinical responses.MethodsCRISPR-based loss-of-function genetic screens were used to target 18,053 protein-coding genes in HCC cells to identify chemotherapy-related synthetic lethal genes in these cells. Synergistic effects were analyzed through in vitro and in vivo analyses, while related mechanisms were explored through RNA-seq and metabolomics analyses. Potential inhibitors of identified genetic targets were selected through high-throughput virtual screening.ResultsThe inhibition of phosphoseryl-tRNA kinase (PSTK) was found to increase HCC cell sensitivity to chemotherapeutic treatment. PSTK was associated with the suppression of chemotherapy-induced ferroptosis in HCC cells, and the depletion of PSTK resulted in the inactivation of glutathione peroxidative 4 (GPX4) and the disruption of glutathione (GSH) metabolism owing to the inhibition of selenocysteine and cysteine synthesis, thus enhancing the induction of ferroptosis upon targeted chemotherapeutic treatment. Punicalin, an agent used to treat hepatitis B virus (HBV), was identified as a possible PSTK inhibitor that exhibited synergistic efficacy when applied together with Sorafenib to treat HCC in vitro and in vivo.ConclusionsThese results highlight a key role for PSTK as a mediator of resistance to targeted therapeutic treatment in HCC cells that functions by suppressing ferroptotic induction. PSTK inhibitors may thus represent ideal candidates for overcoming drug resistance in HCC.

Literature-based translation from synthetic lethality screening into therapeutics targets: CD82 is a novel target for KRAS mutation in colon cancer

Synthetic lethality (SL) is an emerging therapeutic paradigm in cancer. We introduced a different approach to prioritize SL gene pairs through literature mining and RAS-mutant high-throughput screening (HTS) data. We matched essential genes from text-mining and mutant genes from the COSMIC and CCLE HTS datasets to build a prediction model of SL gene pairs. CCLE gene expression data were used to enrich the essential-mutant SL gene pairs using Spearman’s correlation coefficient and literature mining. In total, 223 essential trigger terms were extracted and ranked. The threshold of the essential gene score (Sg) was set to 10. We identified 586 genes essential for the SL prediction model of colon cancer. Seven essential RAS-mutant SL gene pairs were identified in our model, including CD82-KRAS/NRAS, PEBP1-NRAS, MT-CO2-HRAS, IFI27-NRAS/KRAS, and SUMO1-HRAS gene pairs. Using RAS-mutant HTS data validation, we identified two potential SL gene pairs, including the CD82 (essential gene)–KRAS (mutant gene) pair and CD82–NRAS pair in the DLD-1 colon cancer cell line (Spearman’s correlation p-values = 0.004786 and 0.00249, respectively). Based on further annotations by PubChem, we observed that digitonin targeted the complex comprising CD82, especially in KRAS-mutated HCT116 cancer cells. Moreover, we experimentally demonstrated that CD82 exhibited selective vulnerability in KRAS-mutant colorectal cancer. We used literature mining and HTS data to identify candidates for SL targets for RAS-mutant colon cancer.