Skip to main content
Log in

Benchmark Database for Process Optimization and Quality Control of Clinical Cancer Panel Sequencing

  • Research Paper
  • Biomedical Engineering
  • Published:
Biotechnology and Bioprocess Engineering Aims and scope Submit manuscript

Abstract

With advances in Next Generation Sequencing (NGS) technology, individual institutes and research consortia have publicly released large-scale accumulated genomic data obtained in various projects. NGS technology has also been rapidly adopted in clinical practice, and many governments and research organizations have established a regulatory framework and guidelines to ensure the accuracy and reliability of NGS-based testing. These guidelines are essential for the safe use of NGS-based testing, but do not provide enough details that can be specifically applied to the various applications of NGS. In the clinical setting of NGS technology, clinical laboratories should optimize the NGS workflow for their specific uses and performance should be thoroughly evaluated through numerous experiments. However, process optimization and performance evaluation are a great burden to the laboratory in terms of cost and time because of the technical characteristics of NGS technology. The Samsung Medical Center (SMC) has developed and utilized cancer panel sequencing, namely CancerSCAN, which is approved for clinical use by the Ministry of Food and Drug Safety (MFDS) in Korea. SMC has performed various experiments to optimize and evaluate the process of CancerSCAN. In this study, we developed a benchmark database for integrating and sharing these data for process optimization of cancer panel sequencing. This benchmark database contains information on data production and provides functionalities for searching, browsing, and downloading experimental data and raw data files. This benchmark database will be beneficial to researchers, laboratory staff, or potential stakeholders. Database URL: http://129.150.178.10:8080/qms/nqbp/nqbp_home.do

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Savage, N. (2017) Getting data sharing right to help fulfill the promise of cancer genomics. Cell. 168: 551–554.

    Article  CAS  Google Scholar 

  2. Lawler, M., D. Haussler, L. L. Siu, M. A. Haendel, J. A. McMurry, B. M. Knoppers, S. J. Chanock, F. Calvo, B. T. The, G. Walia, I. Banks, P. P. Yu, L. M. Staudt, and C. L. Sawyers (2017) Sharing clinical and genomic data on cancer — the need for global solutions. N Engl. J. Med. 376: 2006–2009.

    Article  Google Scholar 

  3. Grossman, R. L., A. P. Heath, V. Ferretti, H. E. Varmus, D. R. Lowy, W. A. Kibbe, and L. M. Staudt (2016) Toward a shared vision for cancer genomic data. N Engl. J. Med. 375: 1109–1112.

    Article  Google Scholar 

  4. Tomczak, K., P. Czerwinska, and M. Wiznerowicz (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. (Pozn). 19: A68–A77.

    PubMed  PubMed Central  Google Scholar 

  5. Zhang, J., J. Baran, A. Cros, J. M. Guberman, S. Haider, J. Hsu, Y. Liang, E. Rivkin, J. Wang, B. Whitty, M. Wong-Erasmus, L. Yao, and A. Kasprzyk (2011) International cancer genome consortium data portal—a one-stop shop for cancer genomics data. Database (Oxford). 2011: bar026.

    PubMed  PubMed Central  Google Scholar 

  6. Lek, M., K. J. Karczewski, E. V. Minikel, K. E. Samocha, E. Banks, T. Fennell, A. H. O’Donnell-Luria, J. S. Ware, A. J. Hill, B. B. Cummings, T. Tukiainen, D. P. Birnbaum, J. A. Kosmicki, L. E. Duncan, K. Estrada, F. Zhao, J. Zou, E. Pierce-Hoffman, J. Berghout, D. N. Cooper, N. Deflaux, M. DePristo, R. Do, J. Flannick, M. Fromer, L. Gauthier, J. Goldstein, N. Gupta, D. Howrigan, A. Kiezun, M. I. Kurki, A. L. Moonshine, P. Natarajan, L. Orozco, G. M. Peloso, R. Poplin, M. A. Rivas, V. Ruano-Rubio, S. A. Rose, D. M. Ruderfer, K. Shakir, P. D. Stenson, C. Stevens, B. P. Thomas, G. Tiao, M. T. Tusie-Luna, B. Weisburd, H. H. Won, D. Yu, D. M. Altshuler, D. Ardissino, M. Boehnke, J. Danesh, S. Donnelly, R. Elosua, J. C. Florez, S. B. Gabriel, G. Getz, S. J. Glatt, C. M. Hultman, S. Kathiresan, M. Laakso, S. McCarroll, M. I. McCarthy, D. McGovern, R. McPherson, B. M. Neale, A. Palotie, S. M. Purcell, D. Saleheen, J. M. Scharf, P. Sklar, P. F. Sullivan, J. Tuomilehto, M. T. Tsuang, H. C. Watkins, J. G. Wilson, M. J. Daly, D. G. MacArthur, and Exome Aggregation Consortium (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature. 536: 285–291.

    Article  CAS  Google Scholar 

  7. Adams, D. R. and C. M. Eng (2018) Next-generation sequencing to diagnose suspected genetic disorders. N Engl. J. Med. 379: 1353–1362.

    Article  CAS  Google Scholar 

  8. Green, E. D., M. S. Guyer, and National Human Genome Research (2011) Charting a course for genomic medicine from base pairs to bedside. Nature. 470: 204–213.

    Article  CAS  Google Scholar 

  9. U.S. Food and Drug Administration (FDA), Considerations for Design, Development, and Analytical Validation of Next Generation Sequencing-Based In Vitro Diagnostics Intended to Aim in the Diagnosis of Suspected Germline Diseases. https://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/UCM509838.pdf.

  10. Richards, S., N. Aziz, S. Bale, D. Bick, S. Das, J. Gastier-Foster, W. W. Grody, M. Hegde, E. Lyon, E. Spector, K. Voelkerding, H. L. Rehm, and ACGM Laboratory Quality Assurance Committee (2015) Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17: 405–424.

    Article  Google Scholar 

  11. Rehm, H. L., S. J. Bale, P. Bayrak-Toydemir, J. S. Berg, K. K. Brown, J. L. Deignan, M. J. Friez, B. H. Funke, M. R. Hegde, E. Lyon, Working Group of the American College of Medical Genetics, and Genomics Laboratory Quality Assurance Commitee (2013) ACMG clinical laboratory standards for next-generation sequencing. Genet. Med. 15: 733–747.

    Article  Google Scholar 

  12. Gargis, A. S., L. Kalman, D. P. Bick, C. da Silva, D. P. Dimmock, B. H. Funke, S. Gowrisankar, M. R. Hegde, S. Kulkarni, C. E. Mason, R. Nagarajan, K. V. Voelkerding, E. A. Worthey, N. Aziz, J. Barnes, S. F. Bennett, H. Bisht, D. M. Church, Z. Dimitrova, S. R. Gargis, N. Hafez, T. Hambuch, F. C. Hyland, R. A. Luna, D. MacCannell, T. Mann, M. R. McCluskey, T. K. McDaniel, L. M. Ganova-Raeva, H. L. Rehm, J. Reid, D. S. Campo, R. B. Resnick, P. G. Ridge, M. L. Salit, P. Skums, L. J. Wong, B. A. Zehnbauer, J. M. Zook, and I. M. Lubin (2015) Good laboratory practice for clinical next-generation sequencing informatics pipelines. Nat. Biotechnol. 33: 689–693.

    Article  CAS  Google Scholar 

  13. Jennings, L. J., M. E. Arcila, C. Corless, S. Kamel-Reid, I. M. Lubin, J. Pfeifer, R. L. Temple-Smolkin, K. V. Voelkerding, and M. N. Nikiforova (2017) Guidelines for validation of next-generation sequencing-based oncology panels: a joint consensus recommendation of the association for molecular pathology and college of American pathologists. J. Mol. Diagn. 19: 341–365.

    Article  Google Scholar 

  14. National Institutes of Health (NIH) National Human Genome Research Institute, The Cost of Sequencing a Human Genome. https://www.genome.gov/27565109/the-cost-of-sequencing-a-human-genome/.

  15. Chung, J., D. S. Son, H. J. Jeon, K. M. Kim, G. Park, G. H. Ryu, W. Y. Park, and D. Park (2016) The minimal amount of starting DNA for Agilent’s hybrid capture-based targeted massively parallel sequencing. Sci. Rep. 6: 26732.

    Article  CAS  Google Scholar 

  16. Kim, J., W. Y. Park, N. K. D. Kim, S. J. Jang, S. M. Chun, C. O. Sung, J. Choi, Y. H. Ko, Y. L. Choi, H. S. Shim, J. K. Won, and Molecular Pathology Study Group of Korean Society of Pathologists (2017) Good laboratory standards for clinical next-generation sequencing cancer panel tests. J. Pathol. Transl. Med. 51: 191–204.

    Article  Google Scholar 

  17. Kim, S. T., K. M. Kim, N. K. D. Kim, J. O. Park, S. Ahn, J. W. Yun, K. T. Kim, S. H. Park, P. J. Park, H. C. Kim, T. S. Sohn, D. I. Choi, J. H. Cho, J. S. Heo, W. Kwon, H. Lee, B. H. Min, S. N. Hong, Y. S. Park, H. Y. Lim, W. K. Kang, W. Y. Park, and J. Lee (2017) Clinical application of targeted deep sequencing in solid-cancer patients and utility for biomarker-selected clinical trials. Oncologist. 22: 1169–1177.

    Article  CAS  Google Scholar 

  18. Spencer, D. H., J. K. Sehn, H. J. Abel, M. A. Watson, J. D. Pfeifer, and E. J. Duncavage (2013) Comparison of clinical targeted next-generation sequence data from formalin-fixed and fresh-frozen tissue specimens. J. Mol. Diagn. 15: 623–633.

    Article  CAS  Google Scholar 

  19. Bairoch, A. (2018) The cellosaurus, a cell-line knowledge resource. J. Biomol. Tech. 29: 25–38.

    Article  Google Scholar 

  20. ISO/TS 20428:2017 Health informatics — Data elements and their metadata for describing structured clinical genomic sequence information in electronic health records.

  21. ISO/DTS 22692 Health informatics — Quality control metrics for DNA sequencing.

Download references

Acknowledgement

This work was supported by grants from the Ministry of Food & Drug Safety [16173MFDS004] and the National Research Foundation [NRF-2017M3A9A7050803 to W.P. and NRF-2017M3A9G5060264 to D.P.] by the Korean Government (MIST).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Byoung-Kee Yi, Woong-Yang Park or Dae-Soon Son.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic Supplementary Material (ESM)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Seong, D., Chung, J., Lee, KW. et al. Benchmark Database for Process Optimization and Quality Control of Clinical Cancer Panel Sequencing. Biotechnol Bioproc E 24, 793–798 (2019). https://doi.org/10.1007/s12257-019-0202-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12257-019-0202-7

Keywords

Navigation