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Rhabdomyosarcoma Genes Revealed by Statistical Analysis of Gene Expression, CNV Data

NEW YORK (GenomeWeb) – Researchers led by a team at the University of Texas Southwestern Medical Center have used a new computational analysis of genomic data to identify 29 genes involved in rhabdomyosarcoma.

The approach, published this week in Cell Reports, could be used more generally to discover genes involved in the development of other cancer types, the authors noted. 

Rhabdomyosarcoma is an aggressive childhood cancer of developing skeletal muscle and the most common soft tissue cancer in children. Recurring single nucleotide variants are rare in these cancers, which is why the researchers decided to focus on copy number variants and gene expression.

"We came up with the idea that the altered expression of key cancer genes may be driven by genomic copy-number amplifications or losses," Stephen Skapek, the study's senior author and chief of the Division of Pediatric Hematology-Oncology at the Harold C. Simmons Comprehensive Cancer Center at UT Southwestern, said in a statement. "We then developed a new computational algorithm called iExCN to predict cancer genes based on genome-wide copy-number and gene expression data."

The algorithm uses Bayesian statistics to integrate these data types. According to Lin Xu, one of the study's lead author and an instructor in the departments of clinical sciences and pediatrics at UT Southwestern, this type of algorithm often provides more accurate estimates of statistical associations, though it requires more complex computation and longer processing times.

Applying their algorithm to genomic data for 290 rhabdomyosarcoma samples, the researchers came up with 29 candidate genes — 25 potential oncogenic drivers and four tumor suppressors. Of those, only eight had previously been linked to rhabdomyosarcoma.

They went on to validate the functional importance of these genes, using a competitive assay with a mini-pool of CRISPR/Cas9 lentiviral vectors and additional functional analyses. The results indicated that more than half of the genes "represent true shared or line-specific vulnerabilities in the tested [rhabdomyosarcoma] models," they wrote.

Several of the validated genes might be worth further study because there are already drugs available to target them, according to Yanbin Zheng, the study's other lead author and an assistant professor of pediatrics at UT Southwestern.

"We are exploring new strategies for targeted therapies that zero in on these genes," Skapek added. "More important, our study represents a general approach that can be applied to identify oncogenic drivers and tumor suppressor genes in other cancer types for which we have previously failed to uncover targetable vulnerabilities."