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Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies

  1. Author:
    Talsania,Keyur
    Shen,Tsai-Wei
    Chen, Xiongfong
    Jaeger, Erich
    Li, Zhipan
    Chen, Zhong
    Chen, Wanqiu
    Tran,Bao
    Kusko, Rebecca [ORCID]
    Wang, Limin
    Pang, Andy Wing Chun
    Yang, Zhaowei
    Choudhari, Sulbha
    Colgan, Michael
    Fang, Li Tai
    Carroll, Andrew
    Shetty,Jyoti
    Kriga,Yuliya
    German,Oksana
    Smirnova,Tatyana
    Liu, Tiantain
    Li, Jing
    Kellman, Ben
    Hong, Karl
    Hastie, Alex R
    Natarajan, Aparna
    Moshrefi, Ali
    Granat, Anastasiya
    Truong, Tiffany
    Bombardi, Robin
    Mankinen, Veronnica
    Meerzaman, Daoud
    Mason, Christopher E
    Collins, Jack
    Stahlberg, Eric
    Xiao, Chunlin
    Wang, Charles [ORCID]
    Xiao, Wenming
    Zhao,Yongmei [ORCID]

  2. Author Address

    Sequencing Facility Bioinformatics Group, Advanced Biomedical and Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA., Bioinformatics and Computational Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA., Illumina Inc, Foster City, CA, USA., Sentieon Inc, Mountain View, CA, USA., Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA., Sequencing Facility, Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA., Immuneering Corp, Cambridge, MA, USA., Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA., Bionano Genomics, San Diego, CA92121, USA., Department of Allergy and Clinical Immunology, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China., Center for Drug Evaluation and Research, FDA, Silver Spring, MD, USA., Bioinformatics Research & Early Development, Roche Sequencing Solutions Inc, 1301 Shoreway Road, Belmont, CA, 94002, USA., DNAnexus, Mountain View, CA, USA., Dovetail Genomics, Scotts Valley, CA, USA., Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology (CBIIT), National Cancer Institute, Rockville, MD, USA., Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA., National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA., Center for Genomics, Loma Linda University School of Medicine, Loma Linda, CA, USA. chwang@llu.edu., Center for Drug Evaluation and Research, FDA, Silver Spring, MD, USA. Wenming.Xiao@fda.hhs.gov., Sequencing Facility Bioinformatics Group, Advanced Biomedical and Computational Science, Frederick National Laboratory for Cancer Research, Frederick, MD, USA. Yongmei.Zhao@nih.gov., Bioinformatics and Computational Science Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA. Yongmei.Zhao@nih.gov.,
    1. Year: 2022
    2. Date: Dec 13
    3. Epub Date: 2022 12 13
  2. Journal: Genome Biology
    1. 23
    2. 1
    3. Pages: 255
  4. Type of Article: Article
  5. Article Number: 255
  1. Abstract:

    The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples. We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy. A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods. © 2022. The Author(s).

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  2. DOI: 10.1186/s13059-022-02816-6
  3. PMID: 36514120
  4. PMCID: PMC9746098
  5. PII : 10.1186/s13059-022-02816-6

Library Notes

  1. Fiscal Year: FY2022-2023
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