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Big Data Analysis Identifies New Cancer Risk Genes

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South Korean postdoctoral researcher Solip Park working in the Center for Genomic Regulation.

Researches in Spain have developed a new method for analyzing big data to identify genes involved in hereditary cancer risk.

Credit: Center for Genomic Regulation (Spain)

Researchers at the Center for Genomic Regulation (CRG) in Spain have developed a big data analysis method for systematically identifying genes involved in hereditary cancer risk, using existing cancer genome datasets.

The ALFRED (allelic loss featuring rare damaging) statistical technique examines tumor sequencing data for cancer predisposition genes.

CRG's Solip Park notes the method "uses an old idea that cancer genes often require 'two hits' before they cause cancer."

ALFRED was used to identify 13 candidate cancer predisposition genes, including 10 new ones, from the sequences of more than 10,000 patients with 30 distinct tumor types.

"Our results show that the new cancer predisposition genes may have an important role in many types of cancer," says CRG's Ben Lehner. "For example, they were associated with 14% of ovarian tumors, 7% of breast tumors, and to about one in 50 of all cancers."

From Center for Genomic Regulation (Spain)
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Abstracts Copyright © 2018 Information Inc., Bethesda, Maryland, USA


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