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Transcriptome sequencing to detect gene fusions in cancer

Author

Listed:
  • Christopher A. Maher

    (Michigan Center for Translational Pathology,
    Department of Pathology,)

  • Chandan Kumar-Sinha

    (Michigan Center for Translational Pathology,
    Department of Pathology,)

  • Xuhong Cao

    (Michigan Center for Translational Pathology,
    Howard Hughes Medical Institute,)

  • Shanker Kalyana-Sundaram

    (Michigan Center for Translational Pathology,
    Department of Pathology,)

  • Bo Han

    (Michigan Center for Translational Pathology,
    Department of Pathology,)

  • Xiaojun Jing

    (Michigan Center for Translational Pathology,
    Department of Pathology,)

  • Lee Sam

    (Michigan Center for Translational Pathology,
    Department of Pathology,)

  • Terrence Barrette

    (Michigan Center for Translational Pathology,
    Department of Pathology,)

  • Nallasivam Palanisamy

    (Michigan Center for Translational Pathology,
    Department of Pathology,)

  • Arul M. Chinnaiyan

    (Michigan Center for Translational Pathology,
    Howard Hughes Medical Institute,
    Department of Pathology,
    Department of Urology,)

Abstract

Prostate cancer link Recurrent gene fusions, typically associated with haematological malignancies and rare bone and soft-tissue tumours, have recently been described in common solid tumours. Using a combination of new-generation long- and short-read sequencing technologies, Chinnaiyan and colleagues analyse cancer samples for gene fusion transcripts. The approach uncovers transcripts arising from known gene fusions in leukaemia and prostate cancer, as well as novel ones in prostate cancer, including a recurrent transcript SCL45A3-ELK4 that would not have been found using conventional methods.

Suggested Citation

  • Christopher A. Maher & Chandan Kumar-Sinha & Xuhong Cao & Shanker Kalyana-Sundaram & Bo Han & Xiaojun Jing & Lee Sam & Terrence Barrette & Nallasivam Palanisamy & Arul M. Chinnaiyan, 2009. "Transcriptome sequencing to detect gene fusions in cancer," Nature, Nature, vol. 458(7234), pages 97-101, March.
    • Handle: RePEc:nat:nature:v:458:y:2009:i:7234:d:10.1038_nature07638
    DOI: 10.1038/nature07638
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    Citations

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    Cited by:

    1. Farnoosh Abbas-Aghababazadeh & Qian Li & Brooke L Fridley, 2018. "Comparison of normalization approaches for gene expression studies completed with high-throughput sequencing," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-21, October.
    2. Vinícius Diniz Mayrink & Flávio B. Gonçalves, 2020. "Identifying atypically expressed chromosome regions using RNA-Seq data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(3), pages 619-649, September.
    3. Liguo Wang & Yuanxin Xi & Jun Yu & Liping Dong & Laising Yen & Wei Li, 2010. "A Statistical Method for the Detection of Alternative Splicing Using RNA-Seq," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-8, January.
    4. Jungsoo Gim & Sungho Won & Taesung Park, 2016. "LPEseq: Local-Pooled-Error Test for RNA Sequencing Experiments with a Small Number of Replicates," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-15, August.

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