Software Offers Analysis Alternative for Cancer Studies Lacking Matched Controls

NEW YORK (GenomeWeb) – Researchers from the Informatics and Biocomputing arm of the Ontario Institute for Cancer Research and elsewhere have published an algorithm that they claim can distinguish between somatic and germline single nucleotide variants in next-generation sequencing data from tumor tissue in the absence of normal controls.

According to the OICR researchers, when presented with data from roughly 1,600 samples across six different cancer types, the so-called Identification of Somatic Mutations Without Matching Normal Tissues, or ISOWN, software correctly classified between 95 and 98 percent of somatic mutations with F1-measure ranges from 75.9 to 98.6 percent. They published their results in Genome Medicine last week.

Irina Kalatskaya, a project manager and computational biologist in OICR’s Informatics and Bio-computing arm and the lead author of the paper, and her colleagues began developing ISOWN roughly four years ago. At the time, they were looking to analyze 1,500 samples from the Tamoxifen and Exemestane Adjuvant Multinational (TEAM) clinical trial, which compared the effects of the drugs in women with hormone-sensitive early breast cancer in order to identify a biomarker that could predict which patients would respond well to the treatment.

“One of the challenges of this project was that we got access to the

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