ArticleMolecular therapy. Nucleic acids2019
Genetic Interaction-Based Biomarkers Identification for Drug Resistance and Sensitivity in Cancer Cells.
Article in Molecular therapy. Nucleic acids, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 25 citations in OpenAlex.
- Advanced strategies to overcome multidrug resistance in cancer therapy: progress in P-glycoprotein inhibitors, drug delivery, and personalized medicine.Investigational new drugs · 2025Review
- TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.Briefings in bioinformatics · 2025Article
- R-Based Protocols to Predict Synthetic Lethal Interactions in Cancers Using Machine Learning Tools.Methods in molecular biology (Clifton, N.J.) · 2025Article
- MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.PLoS computational biology · 2024Article
- GOAT: Gene-level biomarker discovery from multi-Omics data using graph ATtention neural network for eosinophilic asthma subtype.Bioinformatics (Oxford, England) · 2023Article
- Synthetic viability induces resistance to immune checkpoint inhibitors in cancer cells.British journal of cancer · 2023Article
- A genetic map of the chromatin regulators to drug response in cancer cells.Journal of translational medicine · 2022Article
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
- Mapping the landscape of synthetic lethal interactions in liver cancer.Theranostics · 2021Article
- Revealing biomarkers associated with PARP inhibitors based on genetic interactions in cancer genome.Computational and structural biotechnology journal · 2021Article
- Identifying Drug Sensitivity Subnetworks with NETPHIX.iScience · 2020Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer cells generally harbor hundreds of alterations in the cancer genomes and act as crucial factors in the development and progression of cancer. Gene alterations in the cancer genome form genetic interactions, which affect the response of patients to drugs. We developed an algorithm that mines copy number alteration and whole-exome mutation profiles from The Cancer Genome Atlas (TCGA), as well as functional screen data generated to identify potential genetic interactions for specific cancer types. As a result, 4,529 synthetic viability (SV) interactions and 10,637 synthetic lethality (SL) interactions were detected. The pharmacogenomic datasets revealed that SV interactions induced drug resistance in cancer cells and that SL interactions mediated drug sensitivity in cancer cells. Deletions of HDAC1 and DVL1, both of which participate in the Notch signaling pathway, had an SV effect in cancer cells, and deletion of DVL1 induced resistance to HDAC1 inhibitors in cancer cells. In addition, patients with low expression of both HDAC1 and DVL1 had poor prognosis. Finally, by integrating current reported genetic interactions from other studies, the Cancer Genetic Interaction database (CGIdb) (http://www.medsysbio.org/CGIdb) was constructed, providing a convenient retrieval for genetic interactions in cancer.
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Registered trials
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