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

Sumana Srivatsa, Hesam Montazeri, Gaia Bianco, Mairene Coto-Llerena, Mattia Marinucci, Charlotte K. Y. Ng, Salvatore Piscuoglio () and Niko Beerenwinkel ()
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Sumana Srivatsa: ETH Zurich
Hesam Montazeri: University of Tehran
Gaia Bianco: University of Basel
Mairene Coto-Llerena: University of Basel
Mattia Marinucci: University of Basel
Charlotte K. Y. Ng: SIB Swiss Institute of Bioinformatics
Salvatore Piscuoglio: University of Basel
Niko Beerenwinkel: ETH Zurich

Nature Communications, 2022, vol. 13, issue 1, 1-15

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-35378-z

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