- A new computational algorithm called iExCN has been developed
- iExCN helps identify 29 genes that contribute to rhabdomyosarcoma, a rare cancer of the skeletal muscle
- Customized CRISPR/Cas9-based screens to verify these statistically predicted genetic causes of rhabdomyosarcoma
A novel computational strategy has been developed that has helped in identifying 29 genetic changes, which contribute to rhabdomyosarcoma, an aggressive childhood cancer, revealed a team of researchers at UT Southwestern Medical Center.
What is Rhabdomyosarcoma?
‘iExCN, a computational algorithm developed helps predict cancer genes based on genome-wide copy-number and gene expression data.’
Rhabdomyosarcoma, a rare cancer which affects children develops in the skeletal muscle and is the most common soft tissue cancer that affects the muscles, connective tissues and bones.
This type of cancer accounts for about 5-8% of childhood cancers. It can also occur in adults.
This study helps to explain "the engine" driving formation of rhabdomyosarcoma and also suggests for the development of potential treatments.
a method for statistical inference was used by the research team in conjunction with screening using CRISPR/Cas9, the gene-editing tool,
to confirm the statistical predictions.
Furthermore, the research method used by the team can help identify genetic drivers of even other types of cancers.
'iExCN' is the New Algorithm
Almost all genes occur as pairs in the cells. This study focused on primarily on genes which had only one copy or those that had 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 genomewide 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.
Several new experimental tools such as CRISPR/Cas9 screening technology
have been used in this study 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.
Genes associated with Rhabdomyosarcoma
The research team used this new algorithm to analyze genomic data from 290 rhabdomyosarcoma tumors.
They were able to identify about 29 genes associated with rhabdomyosarcoma,
of which many have not been previously linked to the disease.
Dr. Yanbin Zheng, Assistant Professor of Pediatrics, used customized CRISPR/Cas9-based screens to verify these statistically predicted genetic causes of rhabdomyosarcoma.
In this study, among the validated rhabdomyosarcoma genes, EZH2, CDK6, and RIPK2 were found to be worthy of further investigation, as there are drugs already existing that target these genes that are either FDA-approved or in clinical trials, said Dr. Zheng.
Need for Further Study
Dr. Skapek, who holds the Distinguished Chair in Pediatric Oncology Research, said that the research team needs to further verify the cancer-causing role of the iExCN-identified genes, but that the research is exciting.
He said, "We are exploring new strategies for targeted therapies that zero in on these genes. 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."
The study was published in Cell Reports.