Predicting synthetic lethal interactions using conserved patterns in protein interaction networks

• Published in: PLoS computational biology Vol. 15; no. 4; p. e1006888
• Main Authors: Benstead-Hume, Graeme, Chen, Xiangrong, Hopkins, Suzanna R, Lane, Karen A, Downs, Jessica A, Pearl, Frances M G
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.04.2019; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Animals; Apoptosis; Artificial Intelligence; Bioinformatics; Biology; Cancer; Cancer therapies; Cancer treatment; Computational Biology; Computer applications; Deoxyribonucleic acid; DNA; Drug Discovery; Drug dosages; Funding; Gene Ontology; Genes; Genes, Essential; Genomes; Humans; Insects; Interactomes; Kinases; Laboratories; Life sciences; Medical research; Models, Biological; Molecular Targeted Therapy; Multigene Family; Mutation; Neoplasms - genetics; Neoplasms - metabolism; Neoplasms - therapy; Network topologies; Precision medicine; Predictions; Protein Interaction Mapping - statistics & numerical data; Protein Interaction Maps - drug effects; Protein Interaction Maps - genetics; Proteins; Species classification; Suppressors; Synthetic Biology; Synthetic Lethal Mutations - genetics; Therapeutics research; Topology; Tumor Suppressor Proteins - genetics; Tumor Suppressor Proteins - metabolism; Tumors; Yeast; United Kingdom > UK
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006888

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.