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

Author

Listed:
  • Sumana Srivatsa

    (ETH Zurich
    SIB Swiss Institute of Bioinformatics)

  • Hesam Montazeri

    (University of Tehran)

  • Gaia Bianco

    (University of Basel)

  • Mairene Coto-Llerena

    (University of Basel
    University Hospital Basel)

  • Mattia Marinucci

    (University of Basel)

  • Charlotte K. Y. Ng

    (SIB Swiss Institute of Bioinformatics
    University of Bern)

  • Salvatore Piscuoglio

    (University of Basel
    University Hospital Basel)

  • Niko Beerenwinkel

    (ETH Zurich
    SIB Swiss Institute of Bioinformatics)

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.

Suggested Citation

  • Sumana Srivatsa & Hesam Montazeri & Gaia Bianco & Mairene Coto-Llerena & Mattia Marinucci & Charlotte K. Y. Ng & Salvatore Piscuoglio & Niko Beerenwinkel, 2022. "Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35378-z
    DOI: 10.1038/s41467-022-35378-z
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    1. Yimiao Feng & Yahui Long & He Wang & Yang Ouyang & Quan Li & Min Wu & Jie Zheng, 2024. "Benchmarking machine learning methods for synthetic lethality prediction in cancer," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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