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SB Driver Analysis: a Sleeping Beauty cancer driver analysis framework for identifying and prioritizing

Justin Y Newberg1, Michael A Black2, Nancy A Jenkins3

  • 1Department of Molecular Oncology, Moffitt Cancer Center, Tampa, FL, USA.

Nucleic Acids Research
|May 31, 2018
PubMed

Insights

A new computational method, SB Driver Analysis, prioritizes cancer driver genes from Sleeping Beauty insertional mutagenesis data. This approach enhances understanding of cancer genetics and identifies potential therapeutic targets by defining distinct driver classes.

Area of Science:

  • Genomics
  • Computational Biology
  • Cancer Research

Background:

  • Prioritizing cancer driver genes for therapeutic targeting is challenging, especially for genes with non-recurrent mutations.
  • Meta-analyses of human cancer mutation data have identified major cancer genes but struggle with less common mutations.
  • Sleeping Beauty (SB) insertional mutagenesis in mouse models is a valuable tool for discovering cancer driver genes.

Purpose of the Study:

  • To develop an in-silico method for prioritizing cancer driver genes from population-level SB insertion data.
  • To computationally define cancer drivers that promote tumor initiation and progression.
  • To identify distinct classes of drivers and predict their functions in tumorigenesis.

Main Methods:

  • Development of SB Driver Analysis, an in-silico computational method.
  • Analysis of population-level Sleeping Beauty insertional mutagenesis datasets.
  • Computational prioritization and classification of cancer driver genes.

Main Results:

  • SB Driver Analysis successfully prioritizes cancer driver genes from SB datasets.
  • The method defines distinct driver classes from end-stage tumors.
  • These driver classes predict putative functions during tumorigenesis.

Conclusions:

  • SB Driver Analysis significantly improves the analysis and interpretation of SB cancer datasets.
  • This method enhances the ability to identify and prioritize cancer drivers.
  • The approach contributes to a deeper understanding of the genetic basis of cancer.

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