Bioinformatics Lab, School of Life Sciences, University of Sussex, Falmer, Brighton, United Kingdom.
Division of Cancer Biology, Institute of Cancer Research, Chester Beatty Laboratories, London, United Kingdom.
PLoS Comput Biol. 2019 Apr 17;15(4):e1006888. doi: 10.1371/journal.pcbi.1006888. eCollection 2019 Apr.
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.
为了满足改进治疗的需求，正在开发许多有前途的新型靶向癌症疗法，这些疗法利用了人类合成致死相互作用。这为特定肿瘤抑制因子失活的癌症中的个性化医疗策略提供了便利。主要由于实验程序的限制，已经确定的人类合成致死相互作用相对较少。在这里，我们描述了 SLant（通过网络拓扑结构进行合成致死分析），这是一种预测人类合成致死相互作用的计算系统方法，通过识别和利用种内和种间蛋白质相互作用网络拓扑结构中的保守模式来实现。SLant 的性能优于以前尝试对人类 SSL 相互作用进行分类的方法，对模型预测的实验验证表明，它可能为未来的 SSL 筛选提供有用的指导，并最终有助于靶向癌症治疗的发展。
