Decoding Rhabdomyosarcoma with the iExCN Algorithm

Most genes appear in pairs; when they don’t, it’s a telltale sign of underlying problems. Such is the case with rhabdomyosarcoma, a rare and aggressive childhood cancer characterized by malignant (cancer) cells that form in skeletal muscle tissue.

However, thanks to researchers at UT Southwestern Medical Center who have recently identified 29 contributing genetic that can contribute to the cancer, revealing the disease’s genetic drivers is now more possible, as are, consequently, identifying appropriate potential treatments.

To do this, the researchers used the revolutionary iExCN algorithm, which utilizes Bayesian analysis (a method for statistical inference) in combination with CRISPR/Cas9 (the acclaimed gene-editing tool that screens and confirms the statistical predictions), according to a recent news release.

Stephen Skapek, MD, chief of the Division of Pediatric Hematology-Oncology and with the Harold C. Simmons Comprehensive Cancer Center, explained how the innovation stemmed from the suspicion that altering the expression of key cancer genes may be directed by genomic copy-number amplifications or losses. Running with the idea, the research team created iExCN, a novel computational algorithm, to predict cancer genes based on genomewide copy-number and gene expression data.

“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 Lin Xu, MD, instructor in the Departments of Clinical Sciences and Pediatrics, regarding the innovative technology.

By utilizing the iExCN algorithm, researchers were able to analyze genomic data from 290 rhabdomyosarcoma tumors, which resulted in the identification of 29 associated genes, drivers, and suppressors enriched for cell-cycle and nucleic-acid-binding activities not previously associated with rhabdomyosarcoma.

In the study which assessed the groundbreaking technology, candidate rhabdomyosarcoma driver and suppressor genes were identified by iExCN, which were further validated by the CRISPR/Cas9 mini-pool screen in cultured rhabdomyosarcoma lines. Results also showed that rhabdomyosarcoma growth and differentiation arrest were contributed to by multiple iExCN drivers, and poor survival correlated with a higher number of iExCN genes with copy-number alteration.

Furthermore, among 2 separate cohorts analyzed in the study, the number of iExCN genes harboring copy-number alterations correlative with survival were also revealed, further highlighting rhabdomyosarcoma as a cancer in which multiple drivers are present.

Dr Yanbin Zheng, PhD, assistant professor of Pediatrics, used the customized CRISPR/Cas9-based screens to verify the 29 statistically predicted genetic causes of rhabdomyosarcoma. He highlighted that the EZH2, CDK6, and RIPK2 genes were particularly significant and worth further investigation since drugs that target these genes already exist, whether they are either approved by the US Food and Drug Administration or in clinical trials.

“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,” Dr Skapek explained.

Simply, by helping unearth “the engine driving formation of rhabdomyosarcoma” with the new identification technology, the genetic drivers of other cancers may also be identified, thus creating an influx of oncology knowledge that could hopefully result in more effective treatment practices.