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Abstract
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.
Highlights
Despite sustained global efforts to develop effective therapies, cancer is responsible for more than 15% of the world’s annual deaths
SLant uses most of the experimental data that we have to identify the patterns in the protein interaction network associated with being part of a synthetic lethal interaction
In this study we introduce SLant (Synthetic Lethal analysis via Network topology), a random forest classifier trained on features extracted from the protein-protein interaction (PPI) networks of five species
Summary
Despite sustained global efforts to develop effective therapies, cancer is responsible for more than 15% of the world’s annual deaths. Standard chemotherapy involves non-selective, cytotoxic agents that often have limited effectiveness and strong side-effects. The current focus in oncology drug discovery has moved towards identifying targeted therapies that promise both improved efficacy and therapeutic selectivity [2]. The development of multi-platform genomic technologies has enabled the identification of many of the genes that drive cancer [3]. These cancer driver genes can be broadly classified either as oncogenes or tumour suppressors. The protein product of an oncogene shows an increase in activity, or a change or gain of function when mutated, whereas mutations or epigenetic silencing in tumor suppressors result in an inactivation or loss of function (LOF) of the protein product [4]
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