Title |
Detection of low prevalence somatic mutations in solid tumors with ultra-deep targeted sequencing
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Published in |
Genome Biology, December 2011
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DOI | 10.1186/gb-2011-12-12-r124 |
Pubmed ID | |
Authors |
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, Kelly A Frazer |
Abstract |
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 71 kb 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 six 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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 33% |
United States | 1 | 33% |
India | 1 | 33% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 67% |
Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 10 | 5% |
United States | 4 | 2% |
Malaysia | 1 | <1% |
Australia | 1 | <1% |
Sweden | 1 | <1% |
Canada | 1 | <1% |
Chile | 1 | <1% |
Singapore | 1 | <1% |
Belarus | 1 | <1% |
Other | 2 | <1% |
Unknown | 178 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 65 | 32% |
Student > Ph. D. Student | 42 | 21% |
Student > Master | 25 | 12% |
Other | 16 | 8% |
Professor > Associate Professor | 10 | 5% |
Other | 30 | 15% |
Unknown | 13 | 6% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 102 | 51% |
Biochemistry, Genetics and Molecular Biology | 29 | 14% |
Medicine and Dentistry | 28 | 14% |
Computer Science | 10 | 5% |
Engineering | 7 | 3% |
Other | 10 | 5% |
Unknown | 15 | 7% |