mslp: a comprehensive pipeline in predicting cancer mutations specific synthetic lethal partners

Abstract

BackgroundMutation specific synthetic lethal partners (SLPs) offer significant insights in identifying novel targets and designing personalized treatments in cancer studies. Large scale genetic screens in cell lines and model organisms provide crucial resources for mining SLPs, yet those experiments are expensive and might be difficult to set up. Various computational methods have been proposed to predict the potential SLPs from different perspectives. However, those efforts are hampered by the low signal-to-noise ratio in simple correlation based approaches, or incomplete reliable training sets in supervised approaches. ResultsHere we present mslp, a comprehensive pipeline to identify potential SLPs via integrating genomic and transcriptomic datasets from both patient tumours and cancer cell lines. Leveraging cuttingedges algorithms, we identify a broad spectrum of primary SLPs for mutations presented in patient tumours. Further, for mutations detected in cell lines, we develop the idea of consensus SLPs which are also identified as screen hits, and show consistency impact on cell viability. Applied in real datasets, we successfully identified known synthetic lethal gene pairs. Remarkably, genetic screen results suggested that consensus SLPs have a significant impact on cell viability compared to common hits. ConclusionsMslp is a powerful and flexible pipeline to identify potential SLPs in a cancer context-specific manner, which might aid in drug developments and precise medicines in cancer treatments. The pipeline is implemented in R and freely available in github.