Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality.

ABSTRACT:

Background

Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells. CRISPR viability screens have been widely employed to identify cancer vulnerabilities. However, an approach to systematically infer genetic interactions from viability screens is missing.

Methods

Here we describe PAn-canceR Inferred Synthetic lethalities (PARIS), a machine learning approach to identify cancer vulnerabilities. PARIS predicts synthetic lethal (SL) interactions by combining CRISPR viability screens with genomics and transcriptomics data across hundreds of cancer cell lines profiled within the Cancer Dependency Map.

SUBMITTER: Benfatto S 

PROVIDER: S-EPMC8401190 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature