Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality

• Verfasst von: Benfatto, Salvatore [VerfasserIn]
• Verfasst von: Serçin, Özdemirhan [VerfasserIn]
• Verfasst von: Dejure, Francesca Romana [VerfasserIn]
• Verfasst von: Abdollahi, Amir [VerfasserIn]
• Verfasst von: Zenke, Frank T. [VerfasserIn]
• Verfasst von: Mardin, Balca [VerfasserIn]
• Titel: Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality
• Verf.angabe: Salvatore Benfatto, Özdemirhan Serçin, Francesca R. Dejure, Amir Abdollahi, Frank T. Zenke and Balca R. Mardin
• E-Jahr: 2021
• Jahr: 28 August 2021
• Umfang: 22 S.
• Fussnoten: Gesehen am 13.10.2021
• Titel Quelle: Enthalten in: Molecular cancer
• Ort Quelle: London : Biomed Central, 2002
• Jahr Quelle: 2021
• Band/Heft Quelle: 20(2021), Artikel-ID 111, Seite 1-22
• ISSN Quelle: 1476-4598
• DOI: doi:10.1186/s12943-021-01405-8
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: endogenous aldehydes
• Sach-SW: feature-selection
• Sach-SW: kinase
• Sach-SW: resistance
• Sach-SW: target
• Sach-SW: thymidine phosphorylase
• Sach-SW: thymidylate synthase
• K10plus-PPN: 1773546953

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. Results: Using 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 similar to 30% of all tumors, which express low levels of ALDH2. Conclusions: PARIS 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.