Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens.

Détails

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Etat: Public
Version: Final published version
Licence: CC BY 4.0
ID Serval
serval:BIB_C3372ED961AB
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens.
Périodique
Nature communications
Auteur⸱e⸱s
Srivatsa S., Montazeri H., Bianco G., Coto-Llerena M., Marinucci M., Ng CKY, Piscuoglio S., Beerenwinkel N.
ISSN
2041-1723 (Electronic)
ISSN-L
2041-1723
Statut éditorial
Publié
Date de publication
14/12/2022
Peer-reviewed
Oui
Volume
13
Numéro
1
Pages
7748
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
Publication Status: epublish
Résumé
The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions (ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets.
Mots-clé
Humans, Cell Line, Tumor, Synthetic Lethal Mutations, Neoplasms/drug therapy, Neoplasms/genetics
Pubmed
Open Access
Oui
Création de la notice
27/12/2022 11:49
Dernière modification de la notice
23/01/2024 7:33
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