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CBNA: A control theory based method for identifying coding and non-coding cancer drivers.

Vu V H Pham1, Lin Liu1, Cameron P Bracken2,3

  • 1School of Information Technology and Mathematical Sciences, University of South Australia, Mawson Lakes, Australia.

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|December 3, 2019
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Summary
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This study introduces a new framework, Controllability based Biological Network Analysis (CBNA), to identify both coding and non-coding cancer driver genes. CBNA effectively detects breast cancer drivers and predicts novel microRNA drivers for targeted cancer interventions.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying cancer driver genes is crucial for developing targeted cancer therapies.
  • Existing methods primarily focus on coding drivers, neglecting the role of non-coding RNAs in cancer progression.
  • Novel approaches are needed to identify both coding and non-coding cancer drivers.

Purpose of the Study:

  • To develop a novel framework, Controllability based Biological Network Analysis (CBNA), for uncovering both coding and non-coding cancer drivers.
  • To assess the efficacy of CBNA in identifying cancer drivers, including microRNA (miRNA) drivers.
  • To apply CBNA to subtype-specific cancer drivers and epithelial-mesenchymal transition (EMT) drivers.

Main Methods:

  • CBNA integrates multiple genomic data types: gene expression, gene networks, and mutation data.
  • The framework employs a two-stage process: network construction for a specific condition and driver identification.
  • CBNA was applied to the BRCA dataset for breast cancer driver identification and to identify EMT drivers.

Main Results:

  • CBNA demonstrated superior performance in detecting coding cancer drivers compared to existing methods on the BRCA dataset.
  • CBNA predicted 17 potential miRNA drivers for breast cancer, with some validated by existing literature.
  • The framework successfully identified subtype-specific drivers and EMT drivers, including 7 coding and 6 miRNA drivers found in known EMT gene lists.

Conclusions:

  • CBNA is an effective framework for identifying both coding and non-coding cancer drivers, including novel miRNA drivers.
  • The predicted miRNA drivers offer promising candidates for future wet-lab validation and therapeutic development.
  • CBNA's ability to detect subtype-specific and EMT-related drivers highlights its versatility in cancer genomics research.