vbac084.pdf (1.74 MB)
Biological network topology features predict gene dependencies in cancer cell-lines
journal contribution
posted on 2023-06-10, 05:11 authored by Graeme Benstead-Hume, Sarah Wooller, Joanna Renault, Samantha DiasSamantha Dias, Lisa Woodbine, Tony Carr, Frances PearlFrances PearlMotivation: Protein-protein interaction (PPI) networks have been shown to successfully predict essential proteins. However, such networks are derived generically from experiments on many thousands of different cells. Consequently, conventional PPI networks cannot capture the variation of genetic dependencies that exists across different cell types, let alone those that emerge as a result of the massive cell restructuring that occurs during carcinogenesis. Predicting cell-specific dependencies is of considerable therapeutic benefit, facilitating the use of drugs to inhibit those pro-teins on which the cancer cells have become specifically dependent. In order to go beyond the limitations of the generic PPI, we have attempted to personalise PPI networks to reflect cell-specific patterns of gene expression and mutation. By using twelve topological features of the resulting PPIs, together with matched gene dependency data from DepMap, we trained random-forest clas-sifiers (DependANT) to predict novel gene dependencies. Results: We found that DependANT improves the power of the baseline generic PPI models in predicting common gene dependencies, by up to 10.8% and is more sensitive than the baseline generic model when predicting genes on which only a small number of cell types are dependent. Availability: Software available at https://bitbucket.org/bioinformatics_lab_sussex/dependant2
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- Published
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- Published version
Journal
Bioinformatics AdvancesISSN
2635-0041Publisher
Oxford University PressExternal DOI
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vbac084 1-8Department affiliated with
- Biochemistry Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2022-10-25First Open Access (FOA) Date
2022-11-18First Compliant Deposit (FCD) Date
2022-10-25Usage metrics
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