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Detection of low prevalence somatic mutations in solid tumors with ultra-deep targeted sequencing
Olivier Harismendy, Richard B Schwab, Lei Bao, Jeff Olson, Sophie Rozenzhak, Steve K Kotsopoulos, Stephanie Pond, Brian Crain, Mark S Chee, Karen Messer, Darren R Link and Kelly A Frazer
Genome Biology 2011, 12:R124 doi:10.1186/gb-2011-12-12-r124
Published: 20 December 2011
Abstract (provisional)
Ultra-deep targeted sequencing (UDT-Seq) can identify subclonal somatic mutations in tumor samples. Early assays' limited breadth and depth restrict their clinical utility. Here, we target 71kb of mutational hotspots in 42 cancer genes. We present novel methods enhancing both laboratory workflow and mutation detection. We evaluate UDT-Seq true sensitivity and specificity (>94% and >99%, respectively) for low prevalence mutations in a mixing experiment and demonstrate its utility using 6 tumor samples. With an improved performance when run on the Illumina MiSeq, the UDT-Seq assay is well suited for clinical applications to guide therapy and study clonal selection in heterogeneous samples.
Olivier Harismendy, Richard B Schwab, Lei Bao, Jeff Olson, Sophie Rozenzhak, Steve K Kotsopoulos, Stephanie Pond, Brian Crain, Mark S Chee, Karen Messer, Darren R Link and Kelly A Frazer
Genome Biology 2011, 12:R124 doi:10.1186/gb-2011-12-12-r124
Published: 20 December 2011
Abstract (provisional)
Ultra-deep targeted sequencing (UDT-Seq) can identify subclonal somatic mutations in tumor samples. Early assays' limited breadth and depth restrict their clinical utility. Here, we target 71kb of mutational hotspots in 42 cancer genes. We present novel methods enhancing both laboratory workflow and mutation detection. We evaluate UDT-Seq true sensitivity and specificity (>94% and >99%, respectively) for low prevalence mutations in a mixing experiment and demonstrate its utility using 6 tumor samples. With an improved performance when run on the Illumina MiSeq, the UDT-Seq assay is well suited for clinical applications to guide therapy and study clonal selection in heterogeneous samples.