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At least 19 recordsLinked to original sources
PubMed2025

Genetic mapping and predictive modeling of paralog synthetic lethality.

Paralogs are abundant in the human genome and thought to be a primary source of synthetic lethality, yet the vast paralogome remains largely uncharacterized. A digenic screen of 36,648 paralogous pairs in the human genome revealed that synthetic lethalities were infrequent and varied in penetrance in different tumor backgrounds. We hypothesized that the variable penetrance of synthetic lethalities resulted from complex polygenic interactions with different cellular contexts. A machine learning classifier of a subset of paralog pairs tested across 49 cancer models revealed that endogenous perturbations in related pathways predicted paralog synthetic lethality. Further, predictive modeling of paralog synthetic lethality showed that the strength of synthetic lethal interactions was largely due to the overlap and essentiality of the protein-protein interaction networks shared by the paralog pairs. Collectively, this study tested 36,648 digenic paralog interactions and delineated the key feature classes that underlie the heterogeneity of paralog synthetic lethalities.

Humans↗
PubMed2026

Integrative multi-omics and single-cell analysis identifies EGFR pathway activation and metabolic reprogramming as potential synthetic lethal vulnerabilities in resistance to the FGFR inhibitor AZD4547.

BACKGROUND: Although fibroblast growth factor receptor (FGFR) inhibitors (FGFRi) have demonstrated clinical promise, the inevitable emergence of acquired resistance remains a critical bottleneck, severely compromising their long-term clinical efficacy. The pan-cancer molecular landscape and heterogeneous mechanisms driving this resistance, ranging from genetic alterations to dynamic network rewiring, remain poorly understood. METHODS: We integrated large-scale pharmacogenomic profiling of the FGFR inhibitor AZD4547 from the GDSC2 and PRISM databases with single-cell RNA sequencing to dissect the multi-omics landscape of FGFRi resistance across 312 cell lines from 8 cancer types. This multi-omics framework was further extended by machine learning modeling and systematic synthetic lethality screening to uncover actionable therapeutic targets. In vitro viability assays and western blot analysis were subsequently conducted to experimentally evaluate the predicted FGFR-EGFR synthetic lethality. RESULTS: Our dual-database analysis unveiled a multi-dimensional atlas of FGFRi resistance. We identified cancer-specific genomic drivers, such as ELF4 amplification in glioblastoma, alongside key transcriptomic markers including UCP2 and FSCN1, highlighting a shift towards metabolic reprogramming and epithelial-mesenchymal transition (EMT). Single-cell analysis unveiled that resistance is linked to the heterogeneous enrichment of baseline subpopulations characterized by distinct metaprograms, including cell-cycle dysregulation. Furthermore, a random forest model built on a LASSO-derived transcriptomic signature was constructed, demonstrating promising predictive capability for AZD4547 sensitivity (mean test-set AUC = 0.73, 95% CI [0.63, 0.80]); the signature generalized well to erdafitinib but showed limited transferability to some other FGFR inhibitors (e.g. pemigatinib, BGJ398). Most notably, our synthetic lethal screening revealed a convergent reliance on compensatory RTK signaling (specifically EGFR pathway enrichment) and downstream MAPK/PI3K cascades in resistant phenotypes, providing converging computational evidence for EGFR pathway activation as an adaptive bypass mechanism. This predicted synthetic lethality was experimentally supported in two FGFR-dependent cell line models (RT112 and CCLP1), in which combined FGFR-EGFR inhibition produced marked synergistic antiproliferative effects. CONCLUSIONS: This study establishes a comprehensive multi-omics atlas of resistance to the FGFR inhibitor AZD4547, delineating convergent mechanisms of metabolic reprogramming and EGFR-mediated bypass signaling. Our findings characterize the resistance as a dynamic network rewiring and nominate rational combination strategies to overcome this therapeutic bottleneck. While FGFR-EGFR co-inhibition is experimentally supported, metabolic co-targeting remains a computationally derived, hypothesis-generating strategy.

Benzamides↗
PubMed2026

Genome-wide CRISPR screens map synthetic lethal interactions across recurrent cancer driver alterations.

Synthetic lethality (SL) provides a treatment paradigm for targeting cancer with alterations in driver genes that are not conventionally druggable, including tumor suppressor genes. We execute a series of genome-wide CRISPR screens using functionally validated isogenic cell lines and conduct a large-scale SL analysis using data from the cancer dependency map (DepMap). We chart SL interactions across 15 driver alterations: FBXW7, CCNE1, CDK12, ARID1A, KMT2D, DNMT3A, TET2, KEAP1, STK11, IDH1, SF3B1, SRSF2, U2AF1, chromosome 18q loss, and chromosome 13q loss. We show validation of several SL interactions, including ARID1A and the hexosamine biosynthetic pathway aminotransferase GFPT1, STK11 with CAMK protein kinase MARK2, FBXW7 and the CDK1 regulatory kinase PKMYT1, and CCNE1 amplification and the anaphase-promoting complex or cyclosome (APC/C). In summary, this study offers a rich resource of genetic interactions across cancer drivers enabling the discovery of biological insights and drug targets for future therapeutic development.

CP: cancer↗
PubMed2026

GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions.

Genetic interactions (GIs) underpin the functional connectivity of genes and pathways, and are important for dissecting genotype-phenotype relationships and identifying therapeutic targets for diseases. However, the scale of the human genome restricts systematic experimental interrogation of GIs. Existing computational tools focus on predicting synthetic lethality (SL) and synthetic viability (SV), the two primary forms of GIs, yet their accuracy and biological interpretability are compromised by inadequate modeling of the molecular mechanisms behind positive and negative interactions, as well as the limitation of negative samples. To overcome these challenges, we developed Genetic Interaction Graph Convolutional Network (GiGCN), a signed network modeling framework for the joint identification of gene pairs with SL and SV. We built a high-confidence signed genetic network by integrating verified GIs, and non-interacting gene pairs, together with gene semantic similarity derived from biological processes. By leveraging disentangled subspace decomposition, this framework separately models distinct functional dimensions within gene networks, enabling robust representation of context-dependent regulatory relationships and accurate discrimination of SL and SV events. Benchmark experiments demonstrate that GiGCN outperforms state-of-the-art approaches (area under receiver operating-characteristic curve: 0.978, and area under precision-recall curve: 0.944). Further analyses reveal biologically meaningful insights, including known and novel SL interactions centered on the oncogene MYC Proto-Oncogene (MYC), as well as SV interactions linked to autophagy and mitophagy pathways. This study provides a robust and interpretable network-based strategy for systematically exploring GIs. The GiGCN framework not only improves the precision of SL and SV prediction, but also offers mechanistic insights into gene functional relationships, thereby supporting the discovery of actionable therapeutic targets for cancer and other human diseases.

Humans↗
PubMed2026

Deficiency in POLE Exonuclease Causes Synthetic Lethality in Highly Aneuploid Cancer Cells.

UNLABELLED: Aneuploidy is a hallmark of cancer and is associated with drug resistance and poor clinical outcomes across diverse cancer types. However, no therapies have been clinically established to target highly aneuploid tumors. By analyzing nearly half a million tumor samples subjected to comprehensive genomic profiling, we identified a striking mutual exclusivity between POLE exonuclease domain mutations and high aneuploidy burden. This observation was independently validated using data from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE). Probabilistic modeling revealed that the elevated quantity and unique spectrum of mutations induced by POLE exonuclease deficiency increase the likelihood of inactivating essential genes on chromosome arms harboring losses, leading to a synthetic lethal phenotype in highly aneuploid cells. Functional experiments demonstrated that POLE exonuclease activity is essential for the viability of highly aneuploid cancer cell lines but dispensable in diploid cells. These findings suggest that selective inhibition of POLE exonuclease activity may represent a promising therapeutic strategy for targeting highly aneuploid tumors. SIGNIFICANCE: An integrated approach using large-scale genomic analyses, probabilistic modeling and functional validation identified POLE exonuclease as a potential synthetic lethal target to overcome cancer aneuploidy.

Humans↗
PubMed2026

Changes in the transcriptome and synthetic lethal dependencies following KRAS mutant expression reveal profound tissue specificity.

Oncogenic KRAS mutations exhibit a striking tissue-restricted tropism, occurring with high frequency in pancreatic, colorectal, and lung adenocarcinomas while remaining rare in other lineages. The molecular basis for why these specific tissues are uniquely permissive to KRAS transformation, and how this context shapes therapeutic vulnerabilities, remains poorly defined. Here, we utilized CRISPR-mediated genome engineering to generate endogenous, conditional KRAS-mutant isogenic cell line models across three primary permissive lineages (lung, colon, and pancreas) and the nonpermissive breast lineage. Integrated genome-wide CRISPR fitness screens and comparative transcriptome analyses revealed that KRAS-driven synthetic lethal (SL) dependencies are profoundly shaped by their tissue of origin. Strikingly, we observed minimal overlap in SL hits across lineages, with only three genes shared among the permissive lines, suggesting that the KRAS oncogene operates through divergent, context-specific genetic networks. Mechanistically, we show that KRAS activation induces a universal MYC-driven metabolic signature, but the specific machinery required to sustain this state is lineage-restricted. We identified a dependency on the diphthamide synthesis pathway to maintain translational fidelity amid a KRAS-induced hypertranslational state. These findings demonstrate that even when driven by the same oncogene, tumors exhibit distinct regulatory landscapes and unique genetic vulnerabilities. Our results provide a framework for developing lineage-aware therapeutic strategies, moving beyond universal KRAS inhibition toward targeted interventions tailored to a tumor's specific tissue context.

Proto-Oncogene Proteins p21(ras)↗
PubMed2026

The transposable element-PARP axis underpins synthetic lethality and immunogenic vulnerability in blood cancer.

Transposable elements (TEs) are emerging regulators of hematopoiesis and leukemia, creating vulnerabilities exploitable for therapy. Recent evidence shows that TE reactivation induces innate immune signaling, DNA damage responses, and dependence on poly(ADP-ribose) polymerase (PARP)-mediated protection, enabling synthetic lethality with PARP inhibition even in homologous recombination-proficient leukemias with epigenetic gene mutations. In this article, we highlight the biology underpinning this novel TE-PARP axis, its therapeutic implications, and strategies to expand PARP inhibition beyond HR-deficient cancers through rational combinations with immunotherapy and refined patient stratification.

Humans↗
PubMed2025

Mutations in tumor signaling, metastases, and synthetic lethality establish distinct patterns.

Effective identification of oncogenic mutations is essential for diagnosis, forecasting resistance, and metastasis in remission. It is required for an optimal drug regimen. We develop a framework to discover mutations that co-exist in different oncoproteins, and those that are excluded, likely encoding oncogene-induced senescence. First, mapping the proteins onto pathways assists combinatorial drug selections and helps to detect metastases. Second, it provides the molecular basis for synthetic lethality, to date investigated at the genome level. Our pan-cancer profiles of ~60,000 tumor sequences, detect 3424 co-existing tumor-specific mutations. Mapping them onto pathways indicates that they preferentially promote specific primary tumors. We uncover metastatic mutations and provide metastatic breast-cancer markers. This work not only clarifies the mechanistic basis of intratumor mutational diversity but usefully reveals markers for metastasis in patients' genomes and introduces a novel computational framework for detecting metastasis based on tumor mutational profiles. Mapping the mutations onto pathways provides an invaluable metastasis-targeting resource, guiding drug combinations.

Humans↗
PubMed2026

VHL synthetic lethality screens uncover CBF-β as a negative regulator of STING.

Clear cell renal cell carcinoma (ccRCC) represents the most common form of kidney cancer and is typified by biallelic inactivation of the von Hippel-Lindau (VHL) tumour suppressor gene. Here, we undertake genome-wide CRISPR/Cas9 screening to reveal synthetic lethal interactors of VHL, and uncover that loss of Core Binding Factor β (CBF-β) causes cell death in VHL-null ccRCC cell lines and impairs tumour establishment and growth in vivo. This synthetic relationship is independent of the elevated activity of hypoxia inducible factors (HIFs) in VHL-null cells, but does involve the RUNX transcription factors that are known binding partners of CBF-β. Mechanistically, CBF-β loss leads to upregulation of type I interferon signalling, and we uncover a direct inhibitory role for CBF-β at the STING locus controlling Interferon Stimulated Gene expression. Targeting CBF-β in kidney cancer both selectively induces tumour cell lethality and promotes activation of type I interferon signalling.

Humans↗
PubMed2026

Using cancer profiles to identify synthetic lethal therapeutic targets and predictive biomarkers in cancer gene dependency data.

MOTIVATION: Large scale loss-of-function screens utilising CRISPR or siRNA can provide profound insights into the importance of individual genes for the survival of a cancer cell and can drive the identification of therapeutic targets and biomarkers, and the development of targeted drugs. However, the analysis of these data and the substantial bodies of metadata that relate to them, is technically challenging and typically requires substantial expertise in data science and computer coding. RESULTS: To facilitate the analysis of cancer gene dependency data by cancer biologists and clinical scientists, we have developed DepMine-a computational toolkit providing a powerful system for framing complex queries relating cancer gene dependency to the underlying genetic changes that occur in cancer cells. DepMine identifies synthetic lethal relationships between putative target genes and complex 'cancer profiles' built from user-specified combinations of mutations, copy-number variation, and expression levels, and can refine these to optimal biomarker definitions for target dependency. AVAILABILITY: The Python implementation of DepMine and associated data files can be obtained at https://github.com/UOSbioinformaticslab/depmine and is free to academics and Not-For-Profit organisations. The DepMine release referenced in this paper is archived as DOI: 10.5281/zenodo.19570601.

Humans↗
PubMed2024

Targeting the transcription factor YY1 is synthetic lethal with loss of the histone demethylase KDM5C.

An understanding of the enzymatic and scaffolding functions of epigenetic modifiers is important for the development of epigenetic therapies for cancer. The H3K4me2/3 histone demethylase KDM5C has been shown to regulate transcription. The diverse roles of KDM5C are likely determined by its interacting partners, which are still largely unknown. In this study, we screen for KDM5C-binding proteins and show that YY1 interacts with KDM5C. A synergistic antitumor effect is exerted when both KDM5C and YY1 are depleted, and targeting YY1 appears to be a vulnerability in KDM5C-deficient cancer cells. Mechanistically, KDM5C promotes global YY1 chromatin recruitment, especially at promoters. Moreover, an intact KDM5C JmjC domain but not KDM5C histone demethylase activity is required for KDM5C-mediated YY1 chromatin binding. Transcriptional profiling reveals that dual inhibition of KDM5C and YY1 increases transcriptional repression of cell cycle- and apoptosis-related genes. In summary, our work demonstrates a synthetic lethal interaction between YY1 and KDM5C and suggests combination therapies for cancer treatments.

YY1 Transcription Factor↗
PubMed2026

Biallelic loss of RB1 in hepatocellular carcinoma as synthetic lethal target for artificial intelligence-guided therapy.

The retinoblastoma (RB1) gene is a critical tumor suppressor that regulates cell cycle progression and genomic stability. Although RB1 alterations have been reported in hepatocellular carcinoma (HCC), the biological and clinical consequences of biallelic RB1 inactivation (RB1-Bi) remain poorly defined. We performed a comprehensive allele-specific genomic analysis of HCC patients from the TCGA-LIHC (n&#x2009;=&#x2009;355) and in-house AMC (n&#x2009;=&#x2009;206) cohorts, collectively comprising the AMC-TCGA discovery cohort. In this combined cohort, RB1-Bi was identified in 14.6% of tumors, was enriched in poorly differentiated HCCs and was independently associated with significantly reduced overall survival (adjusted hazard ratio 3.32, 95% CI 1.93-5.72, p&#x2009;<&#x2009;0.001). Additionally, a deep learning-based histopathology model using hematoxylin and eosin-stained slides (i.e., FR-MIL model) accurately predicted RB1-Bi status (F1 score 84.39% [95% CI, &#xb1;0.02]), making it readily identifiable in routine clinical practice. The prevalence and prognostic impact of RB1-Bi, as well as FR-MIL model performance, were consistent across independent validation cohorts, including advanced-stage tumors and external institutions. High-throughput drug screening in isogenic HCC models revealed that RB1-Bi HCC cells were particularly sensitive to inhibitors targeting mitotic regulators (e.g., AURKA, PLK1, KSP) and DNA damage response pathways (e.g., PARP inhibitors). Synthetic lethal interactions between RB1-Bi and these compounds were demonstrated in vitro and in vivo, and combination treatment with mitotic and PARP inhibitors had synergistic effects with acceptable tolerability. We conclude that RB1-Bi represents a clinically actionable biomarker that identifies a high-risk HCC subtype with specific therapeutic vulnerabilities, offering new opportunities for precision medicine.

Humans↗
PubMed1975

[Report of "lethal factor" synthetized by Bacillus cereus (author's transl)].

Bacillus cereus growing in nutrition broth produces during logarithmic growth and in stationary phase a lethal factor extremely toxic for rodents. This toxin is only produced in the presence of oxygen and depends on bacterial replication. The highest titers of toxin are obtained using bacteria in a concentration of 10(7) to 10(9) per ml of nutrition broth. During regression phase of Bacillus cereus the titer of toxin declines to zero. Toxicity of the lethal factor becomes not altered in an alcaline or acidic medium. Temperatures exceeding 65 degrees C inactivate the toxin indicating its thermolability. Oral and rectal dispension of large quantities of the lethal factor does not induce toxic symptoms in rodents. However, when applicating toxin containing cultural filtrate of Bacillus cereus as an i.v., i.p., i.n. or s.c. injection it becomes highly toxic for mice, rats, hamsters, guinea pigs and rabbits. All animals injected by the different ways parenterally died.

Administration, Intranasal↗
PubMed2017

Small-Molecule Inhibitors Targeting DNA Repair and DNA Repair Deficiency in Research and Cancer Therapy.

To maintain stable genomes and to avoid cancer and aging, cells need to repair a multitude of deleterious DNA lesions, which arise constantly in every cell. Processes that support genome integrity in normal cells, however, allow cancer cells to develop resistance to radiation and DNA-damaging chemotherapeutics. Chemical inhibition of the key DNA repair proteins and pharmacologically induced synthetic lethality have become instrumental in both dissecting the complex DNA repair networks and as promising anticancer agents. The difficulty in capitalizing on synthetically lethal interactions in cancer cells is that many potential targets do not possess well-defined small-molecule binding determinates. In this review, we discuss several successful campaigns to identify and leverage small-molecule inhibitors of the DNA repair proteins, from PARP1, a paradigm case for clinically successful small-molecule inhibitors, to coveted new targets, such as RAD51 recombinase, RAD52 DNA repair protein, MRE11 nuclease, and WRN DNA helicase.

DNA Helicases↗
PubMed2025

CDK12 inhibition reveals melanoma dependence on the RUNX1/CBF&#x3b2; complex for genomic stability.

Cutaneous melanoma is the deadliest form of skin cancer, frequently driven by hyperactivation of the RAS/mitogen-activated protein kinase (MAPK) pathway. Cyclin-dependent kinase 12 (CDK12), a downstream effector of MAPK signaling, has emerged as a therapeutic target due to its essential role in transcriptional regulation and DNA damage repair. To identify vulnerabilities associated with CDK12 inhibition, we performed a genome-wide CRISPR-Cas9 screen and identified the Runt-related transcription factor RUNX1 and its cofactor CBF&#x3b2; as synthetic lethal partners of CDK12. RUNX1 inhibition enhanced melanoma sensitivity to CDK12 inhibitors in a p53-independent manner, resulting in DNA damage accumulation and impaired repair capacity. Combined inhibition of CDK12 and RUNX1 suppressed melanoma growth in vivo. These findings identify RUNX1/CBF&#x3b2; as a compensatory mechanism in CDK12-inhibited melanoma and define a synthetic lethal interaction with translational potential for combinatorial therapy.

Core Binding Factor Alpha 2 Subunit↗
PubMed2026

The role of CHAMP1 in chromatin-mediated DNA damage repair.

Defects in the replication stress response are major drivers of cancer development and present key targetable vulnerabilities that can be exploited for anti-cancer therapy. Recent studies have identified CHAMP1 as a novel DNA damage repair factor with roles in double-strand break repair and the replication stress response. Mutations in CHAMP1 are associated with the neurodevelopmental disorder CHAMP1 Syndrome. More recently, children with CHAMP1 Syndrome have developed leukemia, suggesting that CHAMP1 mutations are a potential cancer risk factor. CHAMP1 is part of two DNA damage repair complexes: CHAMP1-POGZ-REV7 (Complex I) and CHAMP1-POGZ-HP1&#x3b1; (Complex II). Complex I promotes homologous recombination by removing the Shieldin complex from the ends of double strand breaks and allowing DSB end resection to occur. Complex II enriches heterochromatin content through the recruitment of the methyltransferase SETDB1 to DNA damage sites. Increased heterochromatin at stalled forks is associated with proper fork stability and restart, demonstrating the importance of CHAMP1 in maintaining genomic integrity. Loss of CHAMP1 leads to increased sensitivity to DNA damaging agents and increased dependence on other DNA damage repair pathways, such as the DNA damage checkpoint and the Fanconi Anemia pathway. CHAMP1 is overexpressed in breast and ovarian cancer cells with high levels of replication stress, providing a molecular mechanism for the tolerance of replication stress. These new findings on the relationship of CHAMP1 with well-established DNA damage repair pathways, suggest that targeting CHAMP1 could present a new synthetic lethality opportunity for cancer cells with high levels of replication stress.

CHAMP1↗
PubMed2025

De novo pyrimidine synthesis is a collateral metabolic vulnerability in NF2-deficient mesothelioma.

Pleural mesothelioma (PM) is one of the deadliest cancers, with limited therapeutic options due to its therapeutically intractable genome, which is characterized by the functional inactivation of tumor suppressor genes (TSGs) and high tumor heterogeneity, including diverse metabolic adaptations. However, the molecular mechanisms underlying these metabolic alterations remain poorly understood, particularly how TSG inactivation rewires tumor metabolism to drive tumorigenesis and create metabolic dependencies. Through integrated multi-omics analysis, we identify for the first time that NF2 loss of function defines a distinct PM subtype characterized by enhanced de novo pyrimidine synthesis, which NF2-deficient PM cells are critically dependent on for sustained proliferation in vitro and in vivo. Mechanistically, NF2 loss activates YAP, a downstream proto-oncogenic transcriptional coactivator in the Hippo signalling pathway, which in turn upregulates CAD and DHODH, key enzymes in the de novo pyrimidine biosynthesis pathway. Our findings provide novel insights into metabolic reprogramming in PM, revealing de novo pyrimidine synthesis as a synthetic lethal vulnerability in NF2-deficient tumors. This work highlights a potential therapeutic strategy for targeting NF2-deficient mesothelioma through metabolic intervention.

Pyrimidines↗