ArticleeLife2020
Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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Who cites it
33 citing papers in PubMed, 52 citations in OpenAlex.
- Article
- Using cancer profiles to identify synthetic lethal therapeutic targets and predictive biomarkers in cancer gene dependency data.Bioinformatics (Oxford, England) · 2026Article
- Review
- Quantitative analysis of genetic interactions in human cells from genome-wide CRISPR-Cas9 screens.bioRxiv : the preprint server for biology · 2026Article
- Synthetic lethality in cancer drug discovery: challenges and opportunities.Nature reviews. Drug discovery · 2026Review
- Collateral lethality of NAD kinase in 1p36 deleted tumors drives vulnerability to NAD kinase 2 depletion.BBA advances · 2026Article
- Genomic innovations in cancer prevention, diagnosis, prognosis and precision therapeutics.Frontiers in genetics · 2026Review
- Challenges and opportunities for oncology drug repurposing informed by synthetic lethality.NPJ systems biology and applications · 2025Article
- Acquired resistance in cancer: towards targeted therapeutic strategies.Nature reviews. Cancer · 2025Review
- Proteomic compensation by paralogs preserves protein interaction networks after gene loss in cancer.Molecular systems biology · 2025Article
- A global genetic interaction map of a human cell reveals conserved principles of genetic networks.bioRxiv : the preprint server for biology · 2025Article
- BaCoN (Balanced Correlation Network) improves prediction of gene buffering.Molecular systems biology · 2025Article
- A mathematical framework for the quantitative analysis of genetic buffering.PLoS genetics · 2025Article
- CREBBP inactivation sensitizes B cell acute lymphoblastic leukemia to ferroptotic cell death upon BCL2 inhibition.Nature communications · 2025Article
- PRODE recovers essential and context-essential genes through neighborhood-informed scores.Genome biology · 2025Article
- Efficacy of CBP/p300 Dual Inhibitors against Derepression of KREMEN2 in cBAF-Deficient Cancers.Cancer research communications · 2025Article
- Building a translational cancer dependency map for The Cancer Genome Atlas.Nature cancer · 2024Article
- Drug target prediction through deep learning functional representation of gene signatures.Nature communications · 2024Article
- Paralog dispensability shapes homozygous deletion patterns in tumor genomes.Molecular systems biology · 2023Article
- Complex synthetic lethality in cancer.Nature genetics · 2023Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
Genetic interactions, including synthetic lethal effects, can now be systematically identified in cancer cell lines using high-throughput genetic perturbation screens. Despite this advance, few genetic interactions have been reproduced across multiple studies and many appear highly context-specific. Here, by developing a new computational approach, we identified 220 robust driver-gene associated genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels. Analysis of these interactions demonstrated that: (i) oncogene addiction effects are more robust than oncogene-related synthetic lethal effects; and (ii) robust genetic interactions are enriched among gene pairs whose protein products physically interact. Exploiting the latter observation, we used a protein-protein interaction network to identify robust synthetic lethal effects associated with passenger gene alterations and validated two new synthetic lethal effects. Our results suggest that protein-protein interaction networks can be used to prioritise therapeutic targets that will be more robust to tumour heterogeneity.
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Registered trials
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.