ArticleNature communications2022
Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Synthetic lethal gene pairs: Experimental approaches and predictive models.Frontiers in genetics · 2022Pooled it
- Context-dependent synthetic lethality - an emerging precision therapeutic approach.Nature reviews. Cancer · 2026Review
- PHLPP is neither a Phosphatase nor a Tumor Suppressor.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026Review
- Deep Unsupervised Domain Adaptation for Translating Cancer Dependency Maps From Cell Lines to Breast Cancer Tumor Genomics.Genetic epidemiology · 2026Article
- Using cancer profiles to identify synthetic lethal therapeutic targets and predictive biomarkers in cancer gene dependency data.Bioinformatics (Oxford, England) · 2026Article
- Article
- Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow.Frontiers in oncology · 2026Review
- Synthetic lethality in cancer drug discovery: challenges and opportunities.Nature reviews. Drug discovery · 2026Review
- Challenges and opportunities for oncology drug repurposing informed by synthetic lethality.NPJ systems biology and applications · 2025Article
- Comprehensive investigation of the molecular basis of cancer dependencies suggests therapeutic options for breast cancer.Cancer biology & medicine · 2025Article
- Epigenetic synthetic lethality as a cancer therapeutic strategy: synergy of experimental and computational approaches.Epigenomics · 2025Review
- Accurate prediction of gene deletion phenotypes with Flux Cone Learning.Nature communications · 2025Article
- Synthetic lethality in cancer: a protocol for scoping review of gene interactions from synthetic lethal screens and functional studies.Systematic reviews · 2025Article
- PHLPP2 is a pseudophosphatase that lost activity in the metazoan ancestor.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Advances in Synthetic Lethality in Potential Oncology Therapeutic Approaches.Current topics in medicinal chemistry · 2025Review
- Benchmarking machine learning methods for synthetic lethality prediction in cancer.Nature communications · 2024Article
- New Horizons of Synthetic Lethality in Cancer: Current Development and Future Perspectives.Journal of medicinal chemistry · 2024Review
- Uncovering hidden cancer self-dependencies through analysis of shRNA-level dependency scores.Scientific reports · 2024Article
- Pathway-driven analysis of synthetic lethal interactions in cancer using perturbation screens.Life science alliance · 2024Article
- Harnessing the power of AI in precision medicine: NGS-based therapeutic insights for colorectal cancer cohort.Frontiers in oncology · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions (ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets.
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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 OpenQuestion graph.