New Statistical Model Identifies Rare Tumor Suppressing Genes

NEW YORK (GenomeWeb) – Using a pan-cancer analysis called allele-specific copy number analysis of tumors (ASCAT), researchers at the Francis Crick Institute, the University of Leuven, and their colleagues developed a new type statistical model, which they were able to use to identify 27 new tumor suppressing genes.    

For their study, which was published today in Nature Communications, the researchers screened for homozygous deletions in 2,218 tumors from 12 cancer types including breast, lung, and bowel cancers, in an attempt to identify rare tumor suppressors. They identified 96 genomic regions that were recurrently targeted by homozygous deletions, either over tumor suppressors or over fragile sites, and constructed a statistical model that separated fragile sites from regions showing signatures of positive selection for homozygous deletions in order to identify candidate tumour suppressors within those regions.

“We find 16 established tumor suppressors and propose 27 candidate tumor suppressors,” the authors wrote. “Several of these genes (including MGMT, RAD17, and USP44) show prior evidence of a tumor suppressive function. Other candidate tumor suppressors, such as MAFTRR, KIAA1551, and IGF2BP2, are novel. Our study demonstrates how rare tumor suppressors can be identified through copy number meta-analysis.”

The researchers began by building a

Be the first to comment

Leave a Reply

Your email address will not be published.


*