Genome Sequencing, Artificial Intelligence Quickly ID Clinically Actionable Cancer Mutations

NEW YORK (GenomeWeb) – By combining whole-genome sequencing and artificial intelligence-based analysis, researchers have reported that they were able to quickly identify clinically actionable mutations within a brain tumor sample.

Researchers from the New York Genome Center, Rockefeller University, and IBM analyzed a glioblastoma sample through panel testing as well as whole-genome sequencing. That sequencing data was then analyzed by a team of bioinformaticians and oncologists at the center as well as by IBM’s Watson for Genomics.

As they reported in Neurology: Genetics yesterday, the researchers found that sequencing uncovered more clinically actionable mutations than panel testing and that relying on Watson for Genomics rather than human analysis reduced the time it took to identify those mutations.

“This study documents the strong potential of Watson for Genomics to help clinicians scale precision oncology more broadly,” Vanessa Michelini, study co-author from IBM Watson Health, said in a statement. “Clinical and research leaders in cancer genomics are making tremendous progress in the opportunity to bring precision medicine to more cancer patients, but genomic data interpretation is a significant obstacle, and that’s where Watson can help.”

For their study, the researchers collected tumor and normal samples from a 76-year-old man with glioblastoma. At

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