Year: 2018
Type: article
Abstract: 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.
Source: Cell Reports
Institutions: The University of Texas Southwestern Medical Center, Harold C. Simmons Comprehensive Cancer Center, Children's Medical Center, National Cancer Institute, Center for Cancer Research, University of Southern California, Keck Hospital of USC, Seattle Children's Hospital, University of Washington, Fred Hutch Cancer Center
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $5,620
OpenAlex ID: W2809759406