Integrative Bayesian Analysis Identifies Rhabdomyosarcoma Disease Genes

Lin Xu, Yanbin Zheng, Jing Liu, Dinesh Rakheja, Sydney Singleterry, Theodore W Laetsch, Jack F. Shern, Javed Khan, Timothy J. Triche, Douglas S. Hawkins, James F Amatruda, Stephen X Skapek

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Identifying oncogenic drivers and tumor suppressors remains a challenge in many forms of cancer, including rhabdomyosarcoma. Anticipating gene expression alterations resulting from DNA copy-number variants to be particularly important, we developed a computational and experimental strategy incorporating a Bayesian algorithm and CRISPR/Cas9 “mini-pool” screen that enables both genome-scale assessment of disease genes and functional validation. The algorithm, called iExCN, identified 29 rhabdomyosarcoma drivers and suppressors enriched for cell-cycle and nucleic-acid-binding activities. Functional studies showed that many iExCN genes represent rhabdomyosarcoma line-specific or shared vulnerabilities. Complementary experiments addressed modes of action and demonstrated coordinated repression of multiple iExCN genes during skeletal muscle differentiation. Analysis of two separate cohorts revealed that the number of iExCN genes harboring copy-number alterations correlates with survival. Our findings highlight rhabdomyosarcoma as a cancer in which multiple drivers influence disease biology and demonstrate a generalizable capacity for iExCN to unmask disease genes in cancer.

Original languageEnglish (US)
Pages (from-to)238-251
Number of pages14
JournalCell Reports
Issue number1
StatePublished - Jul 3 2018


  • Bayesian algorithm
  • CRISPR/Cas9
  • childhood cancer
  • integrative genomic analysis
  • oncogene
  • rhabdomyosarcoma
  • tumor suppressor gene

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)


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