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A Bayesian model for unsupervised detection of RNA splicing based subtypes in cancers

Author

Listed:
  • David Wang

    (University of Pennsylvania
    Perelman School of Medicine, University of Pennsylvania)

  • Mathieu Quesnel-Vallieres

    (University of Pennsylvania
    Perelman School of Medicine, University of Pennsylvania)

  • San Jewell

    (University of Pennsylvania)

  • Moein Elzubeir

    (University of Pennsylvania)

  • Kristen Lynch

    (University of Pennsylvania
    Perelman School of Medicine, University of Pennsylvania)

  • Andrei Thomas-Tikhonenko

    (Perelman School of Medicine, University of Pennsylvania
    Children’s Hospital of Philadelphia)

  • Yoseph Barash

    (University of Pennsylvania
    University of Pennsylvania)

Abstract

Identification of cancer sub-types is a pivotal step for developing personalized treatment. Specifically, sub-typing based on changes in RNA splicing has been motivated by several recent studies. We thus develop CHESSBOARD, an unsupervised algorithm tailored for RNA splicing data that captures “tiles” in the data, defined by a subset of unique splicing changes in a subset of patients. CHESSBOARD allows for a flexible number of tiles, accounts for uncertainty of splicing quantification, and is able to model missing values as additional signals. We first apply CHESSBOARD to synthetic data to assess its domain specific modeling advantages, followed by analysis of several leukemia datasets. We show detected tiles are reproducible in independent studies, investigate their possible regulatory drivers and probe their relation to known AML mutations. Finally, we demonstrate the potential clinical utility of CHESSBOARD by supplementing mutation based diagnostic assays with discovered splicing profiles to improve drug response correlation.

Suggested Citation

  • David Wang & Mathieu Quesnel-Vallieres & San Jewell & Moein Elzubeir & Kristen Lynch & Andrei Thomas-Tikhonenko & Yoseph Barash, 2023. "A Bayesian model for unsupervised detection of RNA splicing based subtypes in cancers," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-022-35369-0
    DOI: 10.1038/s41467-022-35369-0
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