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Validation of Customized Cancer Panel for Detecting Somatic Mutations and Copy Number Alterations

Overview of attention for article published in Genomics & Informatics, December 2017
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Title
Validation of Customized Cancer Panel for Detecting Somatic Mutations and Copy Number Alterations
Published in
Genomics & Informatics, December 2017
DOI 10.5808/gi.2017.15.4.136
Pubmed ID
Authors

Su-Hye Choi, Seung-Hyun Jung, Yeun-Jun Chung

Abstract

Accurate detection of genomic alterations, especially druggable hotspot mutations in tumors, has become an essential part of precision medicine. With targeted sequencing, we can obtain deeper coverage of reads and handle data more easily with a relatively lower cost and less time than whole-exome or whole-genome sequencing. Recently, we designed a customized gene panel for targeted sequencing of major solid cancers. In this study, we aimed to validate its performance. The cancer panel targets 95 cancer-related genes. In terms of the limit of detection, more than 86% of target mutations with a mutant allele frequency (MAF) <1% can be identified, and any mutation with >3% MAF can be detected. When we applied this system for the analysis of Acrometrix Oncology Hotspot Control DNA, which contains more than 500 COSMIC mutations across 53 genes, 99% of the expected mutations were robustly detected. We also confirmed the high reproducibility of the detection of mutations in multiple independent analyses. When we explored copy number alterations (CNAs), the expected CNAs were successfully detected, and this result was confirmed by target-specific genomic quantitative polymerase chain reaction. Taken together, these results support the reliability and accuracy of our cancer panel in detecting mutations. This panel could be useful for key mutation profiling research in solid tumors and clinical translation.

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The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 2 50%
Researcher 1 25%
Unknown 1 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 25%
Computer Science 1 25%
Agricultural and Biological Sciences 1 25%
Unknown 1 25%