title: Performance characteristics of the AmpliSeq Cancer Hotspot panel v2 in combination with the Ion Torrent Next Generation Sequencing Personal Genome Machine.
abstract: Next-Generation Sequencing is a rapidly advancing technology that has research and clinical applications. For many cancers, it is important to know the precise mutation(s) present, as specific mutations could indicate or contra-indicate certain treatments as well as be indicative of prognosis. Using the Ion Torrent Personal Genome Machine and the AmpliSeq Cancer Hotspot panel v2, we sequenced two pancreatic cancer cell lines, BxPC-3 and HPAF-II, alone or in mixtures, to determine the error rate, sensitivity, and reproducibility of this system. The system resulted in coverage averaging 2000× across the various amplicons and was able to reliably and reproducibly identify mutations present at a rate of 5%. Identification of mutations present at a lower rate was possible by altering the parameters by which calls were made, but with an increase in erroneous, low-level calls. The panel was able to identify known mutations in these cell lines that are present in the COSMIC database. In addition, other, novel mutations were also identified that may prove clinically useful. The system was assessed for systematic errors such as homopolymer effects, end of amplicon effects and patterns in NO CALL sequence. Overall, the system is adequate at identifying the known, targeted mutations in the panel.
authors.affiliation: U.S. Food and Drug Administration, Office of Medical Products and Tobacco, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Biology, Chemistry and Materials Science, 10903 New Hampshire Ave, Silver Spring, MD 20993, United States.
authors.forename: Kimberly S
authors.initials: KS
authors.lastname: Butler
authors.affiliation: U.S. Food and Drug Administration, Office of Medical Products and Tobacco, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Biology, Chemistry and Materials Science, 10903 New Hampshire Ave, Silver Spring, MD 20993, United States.
authors.forename: Megan Y L
authors.initials: MY
authors.lastname: Young
authors.affiliation: U.S. Food and Drug Administration, Office of Medical Products and Tobacco, Center for Drug Evaluation and Research, Office of Translational Sciences, Office of Clinical Pharmacology, Division of Applied Regulatory Science, 10903 New Hampshire Ave, Silver Spring, MD 20993, United States.
authors.forename: Zhihua
authors.initials: Z
authors.lastname: Li
authors.affiliation: U.S. Food and Drug Administration, Office of Medical Products and Tobacco, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Biology, Chemistry and Materials Science, 10903 New Hampshire Ave, Silver Spring, MD 20993, United States.
authors.forename: Rosalie K
authors.initials: RK
authors.lastname: Elespuru
authors.affiliation: U.S. Food and Drug Administration, Office of Medical Products and Tobacco, Center for Devices and Radiological Health, Office of Science and Engineering Laboratories, Division of Biology, Chemistry and Materials Science, 10903 New Hampshire Ave, Silver Spring, MD 20993, United States. Electronic address: steven.wood@fda.hhs.gov.
authors.forename: Steven C
authors.initials: SC
authors.lastname: Wood
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D045744:Cell Line, Tumor; D019295:Computational Biology; D004252:DNA Mutational Analysis; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D009154:Mutation; D020411:Oligonucleotide Array Sequence Analysis; D010190:Pancreatic Neoplasms / Q000235:genetics* / Q000473:pathology; D010641:Phenotype; D015203:Reproducibility of Results; D012984:Software
chemical_list: D014408:Biomarkers, Tumor
keywords: Cancer Hotspot panel; Ion Torrent Personal Genome Machine; Mutation detection; Next generation sequencing; Performance analysis; Systematic error analysis