Benchmarking Genetic Interaction Scoring Methods for Identifying Synthetic Lethality from Combinatorial CRISPR Screens

Abstract

Synthetic lethality (SL) is an extreme form of negative genetic interaction, where simultaneous disruption of two non-essential genes causes cell death. SL can be exploited to develop cancer therapies that target tumour cells with specific mutations, potentially limiting toxicity. Pooled combinatorial CRISPR screens, where two genes are simultaneously perturbed and the resulting impacts on fitness estimated, are now widely used for the identification of SL targets in cancer. Various scoring methods have been developed to infer SL genetic interactions from these screens, but there has been no systematic comparison of these approaches. Here, we performed a comprehensive analysis of 5 scoring methods for SL detection using 5 combinatorial CRISPR datasets. We assessed the performance of each algorithm on each screen dataset using two different benchmarks of paralog synthetic lethality. We find that no single method performs best across all screens but identify two methods that perform well across most datasets. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=178 HEIGHT=200 SRC="FIGDIR/small/645224v1_fig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@b66b64org.highwire.dtl.DTLVardef@44871dorg.highwire.dtl.DTLVardef@102b24borg.highwire.dtl.DTLVardef@77416_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Graphical Abstract. Benchmarking Scoring Methods for Synthetic Lethality Detection from CRISPR screen data. Experimental setup for benchmarking experiments. Five different CRISPR double knockout (DKO) screens are scored for genetic interaction using 5 different scoring methods. The calculated scores are analysed using two different benchmarks (De Kegel Hits and Koferle Hits). Area under the receiver operating characteristic curve (AUROC) and Area under the precision recall curve (AUPR) for each scoring method on each dataset are calculated and compared. C_FIG