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Diagnosis of fusion genes using targeted RNA sequencing

Author

Listed:
  • Erin E. Heyer

    (Garvan Institute of Medical Research)

  • Ira W. Deveson

    (Garvan Institute of Medical Research
    UNSW Australia)

  • Danson Wooi

    (Garvan Institute of Medical Research
    UNSW Australia)

  • Christina I. Selinger

    (Royal Prince Alfred Hospital)

  • Ruth J. Lyons

    (Garvan Institute of Medical Research)

  • Vanessa M. Hayes

    (Garvan Institute of Medical Research
    UNSW Australia
    University of Limpopo, Turfloop Campus
    University of Pretoria)

  • Sandra A. O’Toole

    (UNSW Australia
    Royal Prince Alfred Hospital
    University of Sydney
    Garvan Institute of Medical Research)

  • Mandy L. Ballinger

    (Garvan Institute of Medical Research)

  • Devinder Gill

    (Princess Alexandra Hospital)

  • David M. Thomas

    (Garvan Institute of Medical Research)

  • Tim R. Mercer

    (Garvan Institute of Medical Research
    UNSW Australia
    Altius Institute for Biomedical Sciences)

  • James Blackburn

    (Garvan Institute of Medical Research
    UNSW Australia)

Abstract

Fusion genes are a major cause of cancer. Their rapid and accurate diagnosis can inform clinical action, but current molecular diagnostic assays are restricted in resolution and throughput. Here, we show that targeted RNA sequencing (RNAseq) can overcome these limitations. First, we establish that fusion gene detection with targeted RNAseq is both sensitive and quantitative by optimising laboratory and bioinformatic variables using spike-in standards and cell lines. Next, we analyse a clinical patient cohort and improve the overall fusion gene diagnostic rate from 63% with conventional approaches to 76% with targeted RNAseq while demonstrating high concordance for patient samples with previous diagnoses. Finally, we show that targeted RNAseq offers additional advantages by simultaneously measuring gene expression levels and profiling the immune-receptor repertoire. We anticipate that targeted RNAseq will improve clinical fusion gene detection, and its increasing use will provide a deeper understanding of fusion gene biology.

Suggested Citation

  • Erin E. Heyer & Ira W. Deveson & Danson Wooi & Christina I. Selinger & Ruth J. Lyons & Vanessa M. Hayes & Sandra A. O’Toole & Mandy L. Ballinger & Devinder Gill & David M. Thomas & Tim R. Mercer & Jam, 2019. "Diagnosis of fusion genes using targeted RNA sequencing," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09374-9
    DOI: 10.1038/s41467-019-09374-9
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    Cited by:

    1. Nina G. Xie & Michael X. Wang & Ping Song & Shiqi Mao & Yifan Wang & Yuxia Yang & Junfeng Luo & Shengxiang Ren & David Yu Zhang, 2022. "Designing highly multiplex PCR primer sets with Simulated Annealing Design using Dimer Likelihood Estimation (SADDLE)," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Feng Wang & Yang Xu & Robert Wang & Beatrice Zhang & Noah Smith & Amber Notaro & Samantha Gaerlan & Eric Kutschera & Kathryn E. Kadash-Edmondson & Yi Xing & Lan Lin, 2023. "TEQUILA-seq: a versatile and low-cost method for targeted long-read RNA sequencing," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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