ArticlePloS one2015
Predicting human genetic interactions from cancer genome evolution.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Synthetic lethal gene pairs: Experimental approaches and predictive models.Frontiers in genetics · 2022Pooled it
- MVGAE: A Multi-View Graph Auto-Encoder Model for Drug Prediction of Non-Small Cell Lung Cancer Based on Synthetic Lethality.Current issues in molecular biology · 2026Article
- Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction.Briefings in bioinformatics · 2024Article
- ELISL: early-late integrated synthetic lethality prediction in cancer.Bioinformatics (Oxford, England) · 2024Article
- Overcoming selection bias in synthetic lethality prediction.Bioinformatics (Oxford, England) · 2022Article
- Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction.Frontiers in genetics · 2022Article
- Combination of Niraparib, Cisplatin and Twist Knockdown in Cisplatin-Resistant Ovarian Cancer Cells Potentially Enhances Synthetic Lethality through ER-Stress Mediated Mitochondrial Apoptosis Pathway.International journal of molecular sciences · 2021Article
- Combinatorial CRISPR screen identifies fitness effects of gene paralogues.Nature communications · 2021Article
- G2G: A web-server for the prediction of human synthetic lethal interactions.Computational and structural biotechnology journal · 2020Article
- Review
- Pan-Cancer Analysis of Potential Synthetic Lethal Drug Targets Specific to Alterations in DNA Damage Response.Frontiers in oncology · 2019Article
- Harnessing synthetic lethality to predict the response to cancer treatment.Nature communications · 2018Article
- Synthetic lethality: emerging targets and opportunities in melanoma.Pigment cell & melanoma research · 2017Review
- Multi-omic measurement of mutually exclusive loss-of-function enriches for candidate synthetic lethal gene pairs.BMC genomics · 2016Article
- Review
- Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Prediction of Genetic Interactions Using Machine Learning and Network Properties.Frontiers in bioengineering and biotechnology · 2015Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic Lethal (SL) genetic interactions play a key role in various types of biological research, ranging from understanding genotype-phenotype relationships to identifying drug-targets against cancer. Despite recent advances in empirical measuring SL interactions in human cells, the human genetic interaction map is far from complete. Here, we present a novel approach to predict this map by exploiting patterns in cancer genome evolution. First, we show that empirically determined SL interactions are reflected in various gene presence, absence, and duplication patterns in hundreds of cancer genomes. The most evident pattern that we discovered is that when one member of an SL interaction gene pair is lost, the other gene tends not to be lost, i.e. the absence of co-loss. This observation is in line with expectation, because the loss of an SL interacting pair will be lethal to the cancer cell. SL interactions are also reflected in gene expression profiles, such as an under representation of cases where the genes in an SL pair are both under expressed, and an over representation of cases where one gene of an SL pair is under expressed, while the other one is over expressed. We integrated the various previously unknown cancer genome patterns and the gene expression patterns into a computational model to identify SL pairs. This simple, genome-wide model achieves a high prediction power (AUC = 0.75) for known genetic interactions. It allows us to present for the first time a comprehensive genome-wide list of SL interactions with a high estimated prediction precision, covering up to 591,000 gene pairs. This unique list can potentially be used in various application areas ranging from biotechnology to medical genetics.
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