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Tool Uncovers Cancer-Driving Structural Variations


The researchers developed CSVDriver to analyze datasets of structural variants in cancer genomes to uncover likely cancer drivers.

Credit: Shutterstock

Weill Cornell Medicine researchers created the CSVDriver software to identify cancer-generating structural variants (SVs) from tumor samples via DNA sequence analysis.

The software maps and analyzes SV locations in tumor DNA datasets.

The researchers applied CSVDriver to a dataset of 2,382 genomes from 32 different cancer types, analyzing the cancer genomes from different organ systems independently.

The outcomes verified the likely cancer-producing roles of 47 genes, and suggested 26 other genes as likely cancer drivers.

"The general idea here was to model the distribution of background mutations that we would expect for a given cancer type, and then identify, as candidate driver locations, regions where mutations occur more often than expected in a large fraction of patients," said Weill Cornell Medicine's Alexander Martinez-Fundichely.

From Weill Cornell Medicine Newsroom
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Abstracts Copyright © 2022 SmithBucklin, Washington, DC, USA


 

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