New Tools Used to Identify Childhood Cancer Genes

Using a new computational strategy, researchers at the University of Texas (UT) Southwestern Medical Center have identified 29 genetic changes that can contribute to rhabdomyosarcoma, an aggressive childhood cancer. The group used Bayesian analysis, a method for statistical inference, in conjunction with screening using CRISPR/Cas9, the much-heralded gene-editing tool, to confirm the statistical predictions. The work helps to explain "the engine" driving formation of rhabdomyosarcoma and suggests potential treatments. Furthermore, their research method can be used to identify genetic drivers of other cancers. Nearly all genes occur in cells as pairs. This research focused on genes for which there was only one copy or for which there were three or more copies. "We came up with the idea that the altered expression of key cancer genes may be driven by genomic copy-number amplifications or losses. We then developed a new computational algorithm called iExCN to predict cancer genes based on genome-wide copy-number and gene expression data," said Dr. Stephen Skapek, Chief of the Division of Pediatric Hematology-Oncology and with the Harold C. Simmons Comprehensive Cancer Center. The work also used several new experimental tools, including CRISPR/Cas9 screening technology, to verify the function of these predicted cancer genes in rhabdomyosarcoma. "The iExCN algorithm was developed based on Bayesian statistics, which is fundamentally different from commonly used statistics methodologies, and usually provides more accurate estimation of statistical associations, though it involves more complicated computation and longer processing time," said Dr. Lin Xu, Instructor in the Departments of Clinical Sciences and Pediatrics and with the Quantitative Biomedical Research Center.
Login Or Register To Read Full Story