Integrative Bayesian Analysis Identifies Rhabdomyosarcoma Disease Genes

Abstract

Summary: 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. : Xu et al. use an integrative computational pipeline (iExCN) to identify 25 candidate oncogenic driver genes and 4 tumor suppressors in rhabdomyosarcoma, and many are validated in a CRISPR/Cas9 mini-pool screen. Functional assays and correlation with survival indicate that cooperative interactions across iExCN genes contribute to disease biology, including differentiation arrest. Keywords: rhabdomyosarcoma, Bayesian algorithm, CRISPR/Cas9, oncogene, tumor suppressor gene, integrative genomic analysis, childhood cancer