Abstract
BackgroundSynthetic 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.MethodsHere 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.ResultsUsing PARIS, we predicted 15 high confidence SL interactions within 549 DNA damage repair (DDR) genes. We show experimental validation of an SL interaction between the tumor suppressor CDKN2A, thymidine phosphorylase (TYMP) and the thymidylate synthase (TYMS), which may allow stratifying patients for treatment with TYMS inhibitors. Using genome-wide mapping of SL interactions for DDR genes, we unraveled a dependency between the aldehyde dehydrogenase ALDH2 and the BRCA-interacting protein BRIP1. Our results suggest BRIP1 as a potential therapeutic target in ~ 30% of all tumors, which express low levels of ALDH2.ConclusionsPARIS is an unbiased, scalable and easy to adapt platform to identify SL interactions that should aid in improving cancer therapy with increased availability of cancer genomics data.
Highlights
Synthetic lethality occurs when simultaneous perturbation in two or more genes leads to cell death, whereas individual inactivation of single genes is still compatible with cell survival [1]
In order to identify interactions that could represent potential synthetic lethal (SL) interactions or vulnerabilities in cancer cell lines, we focused on positive relationships in the case of dependency/mutation pairs and on negative relationships for dependency/expression pairs, using the Pearson correlation coefficient (PCC) score to retrieve the direction of the relationship
To understand how aldehyde dehydrogenase 2 (ALDH2) and BRCA1 interacting protein C-terminal helicase 1 (BRIP1) levels are regulated in human tumors, we looked at cancer gene expression data obtained from the The Cancer Genome Atlas (TCGA) and compared them to those found in the normal tissue controls derived from the Genotype-Tissue Expression (GTEx) database
Summary
Synthetic lethality occurs when simultaneous perturbation in two or more genes leads to cell death, whereas individual inactivation of single genes is still compatible with cell survival [1] This phenomenon, first described in Drosophila melanogaster [2, 3], has been used as an approach in cancer therapy to exploit vulnerabilities of Studies in human cancer cell lines have accumulated multiple layers of genetic information that can be used to study SL interactions. This includes CRISPR/Cas9-based KO screens, RNAi and drug screens together with gene expression, mutation and copy number variation data. An approach to systematically infer genetic interactions from viability screens is missing
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