Synthetic lethality occurs when the inhibition of two genes is lethal while the inhibition of each single gene is not. It can be harnessed to selectively treat cancer by identifying inactive genes in a given cancer and targeting their synthetic lethal (SL) partners. We present a data-driven computational pipeline for the genome-wide identification of SL… (More)
Genetic screens help infer gene function in mammalian cells, but it has remained difficult to assay complex phenotypes-such as transcriptional profiles-at scale. Here, we develop Perturb-seq, combining single-cell RNA sequencing (RNA-seq) and clustered regularly interspaced short palindromic repeats (CRISPR)-based perturbations to perform many such assays… (More)
We have recently developed a data-mining pipeline that comprehensively identifies cancer unique susceptibilities, following the concept of Synthetic Lethality (SL). The approach enables, for the first time, to identify and harness genome-scale SL-networks to accurately predict gene essentiality, drug response, and clinical prognosis in cancer.