ArticleMolecular cancer2021
Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality.
Article in Molecular cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 32 citations in OpenAlex.
- GiGCN: a network-based framework for uncovering synthetic lethal and viable genetic interactions.Briefings in bioinformatics · 2026Article
- Synthetic lethality targets in pancreatic ductal adenocarcinoma: prevalence and limitations of liquid biopsy detection in a minority-serving cancer center.Future oncology (London, England) · 2026Article
- Induced pluripotent stem cell-derived models of malignant nerve sheath tumor progression mimic glial to neuro-mesenchymal transition and uncover therapeutic opportunities.Nature communications · 2026Article
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology.Briefings in bioinformatics · 2026Review
- Tumor suppressor network dysregulation in neuroblastoma: molecular mechanisms and precision therapeutic opportunities.Frontiers in cell and developmental biology · 2026Review
- Challenges and opportunities for oncology drug repurposing informed by synthetic lethality.NPJ systems biology and applications · 2025Article
- Mechanistic insights into the monotherapy and combination potential of FEN1 inhibition in cancer therapy.Nucleic acids research · 2025Article
- Global research landscapes of ALDH2 and cancer revealed by bibliometric analysis.Discover oncology · 2025Article
- SLAYER: a computational framework for identifying synthetic lethal interactions through integrated analysis of cancer dependencies.NAR genomics and bioinformatics · 2025Article
- Explainable artificial intelligence of DNA methylation-based brain tumor diagnostics.Nature communications · 2025Article
- Graph based recurrent network for context specific synthetic lethality prediction.Science China. Life sciences · 2025Article
- R-Based Protocols to Predict Synthetic Lethal Interactions in Cancers Using Machine Learning Tools.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Advances in Synthetic Lethality in Potential Oncology Therapeutic Approaches.Current topics in medicinal chemistry · 2025Review
- Struct2SL: Synthetic lethality prediction based on AlphaFold2 structure information and Multilayer Perceptron.Computational and structural biotechnology journal · 2025Article
- Benchmarking machine learning methods for synthetic lethality prediction in cancer.Nature communications · 2024Article
- MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.PLoS computational biology · 2024Article
- Review
- GRETTA: an R package for mapping in silico genetic interaction and essentiality networks.Bioinformatics (Oxford, England) · 2023Article
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSynthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells. CRISPR viability screens have been widely employed to identify cancer vulnerabilities. However, an approach to systematically infer genetic interactions from viability screens is missing.
methodsHere we describe PAn-canceR Inferred Synthetic lethalities (PARIS), a machine learning approach to identify cancer vulnerabilities. PARIS predicts synthetic lethal (SL) interactions by combining CRISPR viability screens with genomics and transcriptomics data across hundreds of cancer cell lines profiled within the Cancer Dependency Map.
resultsUsing PARIS, we predicted 15 high confidence SL interactions within 549 DNA damage repair (DDR) genes. We show experimental validation of an SL interaction between the tumor suppressor CDKN2A, thymidine phosphorylase (TYMP) and the thymidylate synthase (TYMS), which may allow stratifying patients for treatment with TYMS inhibitors. Using genome-wide mapping of SL interactions for DDR genes, we unraveled a dependency between the aldehyde dehydrogenase ALDH2 and the BRCA-interacting protein BRIP1. Our results suggest BRIP1 as a potential therapeutic target in ~ 30% of all tumors, which express low levels of ALDH2.
conclusionsPARIS is an unbiased, scalable and easy to adapt platform to identify SL interactions that should aid in improving cancer therapy with increased availability of cancer genomics data.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.