Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies
© 2022. The Author(s)..
BACKGROUND: 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.
RESULTS: 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.
CONCLUSIONS: 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
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| Enthalten in: |
Genome biology - 23(2022), 1 vom: 13. Dez., Seite 255 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Talsania, Keyur [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 15.12.2022 Date Revised 19.01.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s13059-022-02816-6 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM350261296 |
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| 245 | 1 | 0 | |a Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies |
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| 500 | |a Date Revised 19.01.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2022. The Author(s). | ||
| 520 | |a BACKGROUND: 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 | ||
| 520 | |a RESULTS: 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 | ||
| 520 | |a CONCLUSIONS: 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Intramural | |
| 650 | 4 | |a Research Support, U.S. Gov't, P.H.S. | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Cancer | |
| 650 | 4 | |a Multiple platforms | |
| 650 | 4 | |a Next-generation sequencing technology | |
| 650 | 4 | |a Reference call set | |
| 650 | 4 | |a Structural variant calling algorithm | |
| 650 | 4 | |a Structural variation | |
| 700 | 1 | |a Shen, Tsai-Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Xiongfong |e verfasserin |4 aut | |
| 700 | 1 | |a Jaeger, Erich |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Zhipan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Zhong |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Wanqiu |e verfasserin |4 aut | |
| 700 | 1 | |a Tran, Bao |e verfasserin |4 aut | |
| 700 | 1 | |a Kusko, Rebecca |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Limin |e verfasserin |4 aut | |
| 700 | 1 | |a Pang, Andy Wing Chun |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Zhaowei |e verfasserin |4 aut | |
| 700 | 1 | |a Choudhari, Sulbha |e verfasserin |4 aut | |
| 700 | 1 | |a Colgan, Michael |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Li Tai |e verfasserin |4 aut | |
| 700 | 1 | |a Carroll, Andrew |e verfasserin |4 aut | |
| 700 | 1 | |a Shetty, Jyoti |e verfasserin |4 aut | |
| 700 | 1 | |a Kriga, Yuliya |e verfasserin |4 aut | |
| 700 | 1 | |a German, Oksana |e verfasserin |4 aut | |
| 700 | 1 | |a Smirnova, Tatyana |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Tiantain |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Kellman, Ben |e verfasserin |4 aut | |
| 700 | 1 | |a Hong, Karl |e verfasserin |4 aut | |
| 700 | 1 | |a Hastie, Alex R |e verfasserin |4 aut | |
| 700 | 1 | |a Natarajan, Aparna |e verfasserin |4 aut | |
| 700 | 1 | |a Moshrefi, Ali |e verfasserin |4 aut | |
| 700 | 1 | |a Granat, Anastasiya |e verfasserin |4 aut | |
| 700 | 1 | |a Truong, Tiffany |e verfasserin |4 aut | |
| 700 | 1 | |a Bombardi, Robin |e verfasserin |4 aut | |
| 700 | 1 | |a Mankinen, Veronnica |e verfasserin |4 aut | |
| 700 | 1 | |a Meerzaman, Daoud |e verfasserin |4 aut | |
| 700 | 1 | |a Mason, Christopher E |e verfasserin |4 aut | |
| 700 | 1 | |a Collins, Jack |e verfasserin |4 aut | |
| 700 | 1 | |a Stahlberg, Eric |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Chunlin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Charles |e verfasserin |4 aut | |
| 700 | 1 | |a Xiao, Wenming |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yongmei |e verfasserin |4 aut | |
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