CSSLdb: Discovery of cancer-specific synthetic lethal interactions based on machine learning and statistic inference

Abstract

Synthetic lethality (SL) occurs when the inactivation of two genes results in cell death while the inactivation of either gene alone is non-lethal. SL-based therapy has become a promising anti-cancer treatment option with the increasing researches and applications in clinical practice, while the specific therapeutic opportunities for various cancers have not yet been comprehensively investigated. Herein, we described a computational approach based on machine learning and statistical inference to discover the cancer-specific synthetic lethal interactions. First, Random Forest and One-Class SVM were used to perform cancer unbiased prediction of synthetic lethality. Then, two strategies, including mutual exclusivity and differential expression, were used to screen cancer-specific synthetic lethal interactions, resulting in 14,582 SL gene pairs in 33 cancer types. Finally, we developed a freely available database of CSSLdb (Cancer Specific Synthetic Lethality Database, http://www.tmliang.cn/CSSL/) to present cancer-specific synthetic lethal genetic interactions, which would enrich the relevant research and contribute to underlying therapy strategies based on synthetic lethality.

Keywords: Cancer; Database; Prediction; Synthetic lethality (SL).