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Evaluation of somatic copy number variation detection by NGS technologies and bioinformatics tools on a hyper-diploid cancer genome

  1. Author:
    Masood, Daniall
    Ren, Luyao
    Nguyen, Cu
    Brundu, Francesco G
    Zheng, Lily
    Zhao,Yongmei
    Jaeger, Erich
    Li, Yong
    Cha, Seong Won
    Halpern, Aaron
    Truong, Sean
    Virata, Michael
    Yan, Chunhua
    Chen, Qingrong
    Pang, Andy
    Alberto, Reyes
    Xiao, Chunlin
    Yang, Zhaowei
    Chen, Wanqiu
    Wang, Charles
    Cross, Frank
    Catreux, Severine
    Shi, Leming
    Beaver, Julia A
    Xiao, Wenming [ORCID]
    Meerzaman, Daoud M

  2. Author Address

    Office of Oncologic Diseases, Office of New Drug, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, 20993, USA., State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences and Shanghai Cancer Center, Fudan University, Shanghai, China., Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, Rockville, MD, USA., Illumina Inc., San Diego, CA, USA., Sequencing Facility Bioinformatics Group, Biomedical Informatics and Data Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA., Bionano Genomics, San Diego, CA, 20892, USA., National Center for Biotechnology Information, National Librarssy of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA., Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA., Oncology Center of Excellence, Food and Drug Administration, Silver Spring, MD, USA., Office of Oncologic Diseases, Office of New Drug, Center for Drug Evaluation and Research, Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, 20993, USA. wenming.xiao@fda.hhs.gov., Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, Rockville, MD, USA. meerzamd@mail.nih.gov.,
    1. Year: 2024
    2. Date: Jun 20
    3. Epub Date: 2024 06 20
  2. Journal: Genome Biology
    1. 25
    2. 1
    3. Pages: 163
  4. Type of Article: Article
  5. Article Number: 163
  1. Abstract:

    Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome. While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395). NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools. © 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.

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External Sources

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  2. DOI: 10.1186/s13059-024-03294-8
  3. PMID: 38902799
  4. PMCID: PMC11188507
  5. PII : 10.1186/s13059-024-03294-8

Library Notes

  1. Fiscal Year: FY2023-2024
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