Synthetic lethal gene pairs: Experimental approaches and predictive models

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

Synthetic lethality (SL) offers a promising avenue for cancer therapy by exploiting vulnerabilities unique to tumor cells and minimizing off-target effects on normal tissue. However, the application of SL-based therapies remains constrained by a limited repertoire of validated targets and biomarkers. Using data from the Cancer Dependency Map (DepMap) and advanced in silico modeling, Evariste’s Frobenius Target platform identified 50 potential SL gene pairs with druggable nodes and rational mechanisms linking targets and biomarkers. From this set, 24 gene pairs were prioritized for validation based on translational relevance and impact on the patient population. Validation was achieved through a collaborative high-throughput workflow with EditCo Bio, leveraging their CRISPR-based cell line engineering platform to generate 72 knockout pools in under three weeks. Validation studies in multiple cell lines demonstrated >10 high-confidence SL pairs, including a novel SL relationship involving focal adhesion kinase (FAK). To further substantiate this relationship, novel inhibitors were developed with best-in-class selectivity over the SL partner gene. Functional assays confirmed these inhibitors’ effectiveness in biomarker-low cell lines, with promising implications for clinical translation. This study highlights the potential of AI-driven approaches to uncover the next generation of SL targets, expanding the therapeutic landscape in cancer research. By focusing on biomarker-low gene expression as a predictor of SL, our work establishes a robust framework for the discovery and validation of clinically actionable SL relationships. Citation Format: Amalia Soenens, Daniel Miller, Oliver Vipond, Florencia Skuras, Alfie Brennan, Anna Hercot. AI driven discovery of novel synthetic lethal gene pairs for targeted cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3643.

MotivationThe integration of genome-scale metabolic and regulatory networks has received significant interest in cancer systems biology. However, the identification of lethal genetic interventions in these integrated models remains challenging due to the combinatorial explosion of potential solutions. To address this, we developed the genetic Minimal Cut Set (gMCS) framework, which computes synthetic lethal interactions—minimal sets of gene knockouts that are lethal for cellular proliferation- in genome-scale metabolic networks with signed directed acyclic regulatory pathways. Here, we present a novel formulation to calculate genetic Minimal Intervention Sets, gMISs, which incorporate both gene knockouts and knock-ins.ResultsWith our gMIS approach, we assessed the landscape of lethal genetic interactions in human cells, capturing interventions beyond synthetic lethality, including synthetic dosage lethality and tumor suppressor gene complexes. We applied the concept of synthetic dosage lethality to predict essential genes in cancer and demonstrated a significant increase in sensitivity when compared to large-scale gene knockout screen data. We also analyzed tumor suppressors in cancer cell lines and identified lethal gene knock-in strategies. Finally, we demonstrate how gMISs can help uncover potential therapeutic targets, providing examples in hematological malignancies.Availability and implementationThe gMCSpy Python package now includes gMIS functionalities. Access: https://github.com/PlanesLab/gMCSpy.

We predicted gene function using synthetic lethal genetic interactions between null alleles in Saccharomyces cerevisiae. Phenotypic and protein interaction data indicate that synthetic lethal gene pairs function in parallel or compensating pathways. Congruent gene pairs, defined as sharing synthetic lethal partners, are in single pathway branches. We predicted benomyl sensitivity and nuclear migration defects using congruence; these phenotypes were uncorrelated with direct synthetic lethality. We also predicted YLL049W as a new member of the dynein–dynactin pathway and provided new supporting experimental evidence. We performed synthetic lethal screens of the parallel mitotic exit network (MEN) and Cdc14 early anaphase release pathways required for late cell cycle. Synthetic lethal interactions bridged genes in these pathways, and high congruence linked genes within each pathway. Synthetic lethal interactions between MEN and all components of the Sin3/Rpd3 histone deacetylase revealed a novel function for Sin3/Rpd3 in promoting mitotic exit in parallel to MEN. These in silico methods can predict phenotypes and gene functions and are applicable to genomic synthetic lethality screens in yeast and analogous RNA interference screens in metazoans.

Synthetic lethality: General principles, utility and detection using genetic screens in human cells

Applications of predictive modelling early in the COVID-19 epidemic

Synthetic lethality represents a novel paradigm in molecular targeted cancer therapy. In synthetic lethality, perturbation of one gene alone does not hinder cell viability, yet simultaneous perturbation of both genes results in a loss of cellular viability. The presence of gene mutations in cancer cells, as opposed to normal cells, provides an opportunity for targeted therapies that mimic the effects of the second genetic mutation, enabling selective eradication of cancer cells. Recent advances in high-throughput screening technologies, such as CRISPR-Cas9 and RNA interference, have significantly enhanced the identification of synthetic lethal interactions, expanding the potential targets for therapeutic intervention. Challenges in exploiting synthetic lethality for cancer treatment include the complexities of tumor biology, limited comprehension of synthetic lethal interactions, drug resistance, and impediments in screening and clinical translation. Emerging strategies, such as combination therapies and novel drug designs, are being developed to overcome these obstacles. By virtue of its selective lethality towards cancer cells bearing specific genetic alterations, targeting synthetic lethal genes holds the promise to provide wider therapeutic windows compared to traditional cytotoxic chemotherapy. This review describes the current state of synthetic lethality applications in cancer treatment, encompassing both biological and methodological perspectives. It highlights the latest advancements in synthetic lethality with emerging interventional strategies. Furthermore, it explores future directions for research and clinical implementation, aiming to refine and expand the therapeutic potential of synthetic lethality in oncology.

ABSTRACTCancer treatment is an ongoing challenge, as directly targeting oncogenic drivers is often unfeasible in many patients due to the lack of druggable targets. This has led to the exploration of alternative strategies, such as exploiting synthetic lethality (SL) relationships between genes. SL facilitates the indirect targeting of oncogenic drivers, as exemplified by the clinical success of PARP inhibitors against BRCA-mutated tumors. Advances in high-throughput perturbation screens and multi-omics technologies have deepened our understanding of SL relationships, while computational models enhance SL predictions to better reflect biological complexity. However, while numerous experimental and computational methods have been developed to identify SL interactions, difficulties remain in translating these findings into clinical applications.This review combines recent progress on SL relationships in cancer with emerging insights into epigenetic regulation, highlighting how epigenetic drugs (epidrugs) can provide new opportunities for targeted interventions, offering a way to minimize off-target effects and enhance therapeutic precision. To advance SL-based therapies, efforts must focus not only on identifying new SL interactions but also on consolidating existing knowledge and integrating experimental and computational approaches to characterize the vulnerabilities of cancer cells. Strengthening this foundation will be critical for the effective development of SL-based cancer treatments.

Cancer is a leading cause of death throughout the World. A limitation of many current chemotherapeutic approaches is that their cytotoxic effects are not restricted to cancer cells, and adverse side effects can occur within normal tissues. Consequently, novel strategies are urgently needed to better target cancer cells. As we approach the era of personalized medicine, targeting the specific molecular defect(s) within a given patient’s tumor will become a more effective treatment strategy than traditional approaches that often target a given cancer type or sub-type. Synthetic genetic interactions are now being examined for their therapeutic potential and are designed to target the specific genetic and epigenetic phenomena associated with tumor formation, and thus are predicted to be highly selective. In general, two complementary approaches have been employed, including synthetic lethality and synthetic dosage lethality, to target aberrant expression and/or function associated with tumor suppressor genes and oncogenes, respectively. Here we discuss the concepts of synthetic lethality and synthetic dosage lethality, and explain three general experimental approaches designed to identify novel genetic interactors. We present examples and discuss the merits and caveats of each approach. Finally, we provide insight into the subsequent pre-clinical work required to validate novel candidate drug targets.

Rapid Identification of BCR/ABL1 -like Acute Lymphoblastic Leukemia (ALL) Cases By Quantitative Real Time-PCR (Q-RT-PCR). Generation and Validation of a Predictive Statistical Model

We present novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reserved judgment region. Application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; multistage discriminant analysis of biomarkers for prediction of early atherosclerosis; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis, and prediction of protein localization sites. In all these applications, the predictive model yields correct classification rates ranging from 80% to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

Background: IKKε was previously identified as an oncogene in breast cancer and was associated with poor clinical outcome in ovarian cancer. Recently, we demonstrated that IKKε is a key regulator of invasion and metastasis programs in ovarian cancer. Therefore, we developed a rapid and robust dual shRNA technique to create IKKε-matched cell line pairs and performed synthetic lethality screens to identify the key molecular targets influenced by the loss of IKKε expression in ovarian cancer. Material and methods: To create an IKKε-matched cell line pair, we utilized a retroviral bicistronic vector containing the transmembrane and extracellular domains of the mouse T-cell surface glycoprotein CD8 alpha, co-expressed with either IKKε or negative control shRNAs. The positively transduced cells were isolated using mouse CD8a-specific microbeads. After recovery from beads purification, the human kinome shRNA library, containing a puromycin selectable marker fused to GFP, was introduced. To identify shRNAs that sensitized ovarian cancer cells to IKKε depletion, we harvested each shRNA library infected control and IKKε- depleted cells at two time points. This screening was performed in two different ovarian cancer cell lines, one with and one without p53 mutation. Results: We prioritized the genes consistently depleted at two different time points, depending on their doubling times (A2780 at Days 4 and 7, Ovcar5 at Days 7 and 14). In order to generalize this synthetic lethality to ovarian cancers expressing a high level of IKKε, we first focused on the genes identified in both ovarian cancer cell lines, yielding 29 candidate genes. We examined the expression levels of these 29 candidate genes in The Cancer Genome Atlas data portal containing more than 500 ovarian serous cystadenocarcinoma. Most of these 29 genes were overexpressed in a subset of TCGA ovarian cancers. Strikingly, CHEK1 was overexpressed by more than 2 fold in 98% of TCGA ovarian cancers. The combination knockdown with IKKε and CHEK1 resulted in a further decrease in cellular proliferation and a further increase in G0/G1 arrest compared to individual depletion of either gene. Consistently, we observed the same biological effect using pharmacological inhibitors of IKKε (BX795) and CHEK1 (PF00477736). Conclusion: Our dual shRNA technique efficiently identified CHEK1 as an IKKε synthetic lethal gene in ovarian cancer. Identification of IKKε dependent signaling will lead to development of context-specific therapeutics in the poor prognostic group of patients with a high level of IKKε expression. Citation Format: Marianne K H Kim, George Wright, Louis Staudt, Christina Annunziata. shRNA library screening by dual shRNA technique identifies CHEK1 as a synthetic lethal gene in the absence of IKKε in ovarian cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3144. doi:10.1158/1538-7445.AM2013-3144

2598 Background: Synthetic lethal targets are proteins that are contextually vulnerable. Inhibitors of PARP1, for example, selectively produce a lethal phenotype in the context of cancer cells which have lost BRCA1 or BRCA2 function. As a high mutation rate is a hallmark of many cancers, targeting synthetic lethal interactions to selectively inhibit cancer cells with altered genetic backgrounds may increase the specificity and efficacy of therapeutics. Recently, clinical trials have targeted synthetic lethal pairs such as EGFR and BRAF, TP53 and BCL2, and PTEN and CHD1. Previous attempts to identify synthetic lethal targets have relied on empirical results from published studies of biological pathways perturbed in cancer cells. Developing strategies to rapidly identify synthetic lethals by combining multiple experimental and computational approaches would result in a new class of potential cancer drug targets beyond the existing efforts that rely on single experimental or computational methods alone. Methods: Here we present Expansive AI, an artificial intelligence augmented knowledge network that enables rapid hypothesis generation for accelerated discovery research. Using a purpose-built, hypergraph database of massive, integrated genomic and biomedical data, we can query all synthetic lethals and their component genes, as well as a wealth of data related to these genes. The database of biological data includes 11,000+ cancer genomes from TCGA, prior knowledge resources such as gene ontology and pathway resources, and experimental data including chemical and protein interaction and patent data. The hypergraph’s architecture allows for linking and nesting data, enabling efficient extraction of biologically-relevant features. Results: Using these features, a neural network classified 540 new candidate pairs that have previously not been reported. The candidate pairs were filtered to include only known oncogenes and least-studied genes. This produced a list of gene pairs which may represent the most novel class of synthetic lethal target candidates identified to date. Conclusions: We highlight the results of this AI-based approach and discuss validation efforts of the predicted interactions in specific cancer contexts.

This paper analyzes public data sources and their requirements for the transition from school to vocational training and career of people with disabilities in the context of Article 31 of the UN-Convention on Rights of People with Disabilities. Different focuses of the public data sources within the involved systems and challenges in data analysis will be presented. These manifest themselves as cross-system interface problems when it comes to the identification and whereabouts of young people with disabilities at the transition from school to vocational training and employment. With these challenges public data sources on the transition from school to vocational training and employment are especially under scrutiny when it comes to developing and implementing policies in respect to the Convention on Rights for People with disabilities and the provision of adequate planning data.

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.