ArticleNature communications2017
An in-silico approach to predict and exploit synthetic lethality in cancer metabolism.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 64 citations in OpenAlex.
- Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism.Briefings in bioinformatics · 2024Pooled it
- gMISpy: integration of complex regulatory networks and genome scale metabolic models.Bioinformatics (Oxford, England) · 2026Article
- Synthetic lethality in cancer drug discovery: challenges and opportunities.Nature reviews. Drug discovery · 2026Review
- Beyond synthetic lethality in large-scale metabolic and regulatory network models via genetic minimal intervention set.Bioinformatics advances · 2026Article
- Constraint based modeling of drug induced metabolic changes in a cancer cell line.NPJ systems biology and applications · 2025Article
- Accurate prediction of gene deletion phenotypes with Flux Cone Learning.Nature communications · 2025Article
- Exploring weighting schemes for the discovery of informative generalized between pathway models to uncover pathways in genetic interaction networks.Scientific reports · 2025Article
- Minimal cut sets in metabolic networks: from conceptual foundations to applications in metabolic engineering and biomedicine.Briefings in bioinformatics · 2025Review
- A fast method for extracting essential and synthetic lethality genes in GEM models.Bioinformatics advances · 2025Article
- An automated network-based tool to search for metabolic vulnerabilities in cancer.Nature communications · 2024Article
- Adding metabolic tasks to human GEM models to improve the study of gene targets and their associated toxicities.Scientific reports · 2024Article
- New Horizons of Synthetic Lethality in Cancer: Current Development and Future Perspectives.Journal of medicinal chemistry · 2024Review
- gMCSpy: efficient and accurate computation of genetic minimal cut sets in Python.Bioinformatics (Oxford, England) · 2024Article
- Integrating Genome-Scale Metabolic Models with Patient Plasma Metabolome to Study Endothelial Metabolism In Situ.International journal of molecular sciences · 2024Article
- Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections.NPJ systems biology and applications · 2024Article
- SL-scan identifies synthetic lethal interactions in cancer using metabolic networks.Scientific reports · 2023Article
- Synthetic lethality in large-scale integrated metabolic and regulatory network models of human cells.NPJ systems biology and applications · 2023Article
- Multi-level characteristics recognition of cancer core therapeutic targets and drug screening for a broader patient population.Frontiers in pharmacology · 2023Article
- ELIMINATOR: essentiality analysis using multisystem networks and integer programming.BMC bioinformatics · 2022Article
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic lethality is a promising concept in cancer research, potentially opening new possibilities for the development of more effective and selective treatments. Here, we present a computational method to predict and exploit synthetic lethality in cancer metabolism. Our approach relies on the concept of genetic minimal cut sets and gene expression data, demonstrating a superior performance to previous approaches predicting metabolic vulnerabilities in cancer. Our genetic minimal cut set computational framework is applied to evaluate the lethality of ribonucleotide reductase catalytic subunit M1 (RRM1) inhibition in multiple myeloma. We present a computational and experimental study of the effect of RRM1 inhibition in four multiple myeloma cell lines. In addition, using publicly available genome-scale loss-of-function screens, a possible mechanism by which the inhibition of RRM1 is effective in cancer is established. Overall, our approach shows promising results and lays the foundation to build a novel family of algorithms to target metabolism in cancer.Exploiting synthetic lethality is a promising approach for cancer therapy. Here, the authors present an approach to identifying such interactions by finding genetic minimal cut sets (gMCSs) that block cancer proliferation, and apply it to study the lethality of RRM1 inhibition in multiple myeloma.
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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.