by Graeme Benstead-Hume, Xiangrong Chen, Suzanna R. Hopkins, Karen A. Lane, Jessica A. Downs, Frances M. G. Pearl
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.
Tratto da: www.plos.org
Note sul Copyright: Articles and accompanying materials published by PLOS on the PLOS Sites, unless otherwise indicated, are licensed by the respective authors of such articles for use and distribution by you subject to citation of the original source in accordance with the Creative Commons Attribution (CC BY) license.