Predicting synthetic lethal interactions using conserved patterns in protein interaction networks

Benstead-Hume, Graeme, Chen, Xiangrong, Hopkins, Suzi, Lane, Karen A, Downs, Jessica and Pearl, Frances M G (2019) Predicting synthetic lethal interactions using conserved patterns in protein interaction networks. PLoS Computational Biology, 15 (4). e1006888. ISSN 1553-7358

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In response to a need for improved treatments, a number of promising novel targeted cancer therapies are being developed that exploit human synthetic lethal interactions. This is facilitating personalised medicine strategies in cancers where specific tumour suppressors have become inactivated. Mainly due to the constraints of the experimental procedures, relatively few human synthetic lethal interactions have been identified. Here we describe SLant (Synthetic Lethal analysis via Network topology), a computational systems approach to predicting human synthetic lethal interactions that works by identifying and exploiting conserved patterns in protein interaction network topology both within and across species. SLant out-performs previous attempts to classify human SSL interactions and experimental validation of the models predictions suggests it may provide useful guidance for future SSL screenings and ultimately aid targeted cancer therapy development.

Item Type: Article
Keywords: Cancer, Drug, Protein-Protein Interactions, Algorithm, Synthetic lethality.
Schools and Departments: School of Life Sciences > Biochemistry
Subjects: Q Science > Q Science (General) > Q0179.9 Research
Depositing User: Frances Pearl
Date Deposited: 28 Mar 2019 12:10
Last Modified: 01 Jul 2019 15:46

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Project NameSussex Project NumberFunderFunder Ref
Genome Damage and Stability Centre - studentshipsG1673MRC-MEDICAL RESEARCH COUNCILMR/N50189X/1
Chromatin remodelling complexes in the maintenance of genome stabilityG1178CANCER RESEARCH UKC7905/A16417