New Algorithm Developed To Identify Childhood Cancer Genes


Researchers from University of Texas’ (UT) Southwestern Medical Center devised a new computational strategy to identify childhood cancer genes

Rhabdomyosarcoma is a soft tissue cancer that develops skeletal muscle in children. Researchers at UT Southwestern Medical Center identified 29 genetic changes responsible for rhabdomyosarcoma, using a new computational strategy. The researchers used statistical inference method of Bayesian analysis, along with screening by CRISPR/Cas9, to confirm the statistical predictions of the aggressive child cancer. The research explains formation of rhabdomyosarcoma and suggests potential treatments and is expected to identify genetic drivers of other cancers. Genes in the cell are always paired to each other. The study focused on genes with only one copy or three or more copies as the altered expression of key cancer genes may be driven by genomic copy-number amplifications or losses. The new computational algorithm created by the team is called iExCN and is tasked to predict cancer genes based on genome wide copy-number and gene expression data. Along with iExCN, CRISPR/Cas9 screening technology was used to verify the function of these predicted cancer genes in rhabdomyosarcoma. Bayesian statistics is the fundamental base of the algorithm that accurately estimates statistical associations involving complicated computation with longer processing time. The research was published in the journal Cell Report on July 3, 2018.

iExCN algorithm analyzed genomic data from 290 rhabdomyosarcoma tumors. It was observed that 29 new genes were linked to rhabdomyosarcoma. Some of the genes such as EZH2, CDK6, and RIPK verified by customized CRISPR/Cas9-based screens require further investigation as there are several FDA-approved or in clinical trials drugs that target these genes. According to Dr. Skapek, Distinguished Chair in Pediatric Oncology Research further research is required to verify the cancer-causing role of the iExCN-identified genes. The research is expected to provide new strategies for targeted therapies on these genes.


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