Skip to main content

Advertisement

SpringerLink
Log in

Multiplex ddPCR assay for screening copy number variations in BRCA1 gene

  • Preclinical study
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Purpose

Germinal and somatic rearrangements in BRCA1 gene play a significant role in carcinogenesis of breast and ovarian cancer. The present study is dedicated to the development of multiplex droplet digital PCR (ddPCR) assay for detecting large deletions and duplications in the BRCA1 gene.

Methods

In-house tetraplex ddPCR assay for BRCA1 gene analysis was used for testing of DNA samples with BRCA1 status.

Results

DNA specimens were purified from 24 individuals. The presence of BRCA1 rearrangements in samples was confirmed by a commercial MLPA-based kit. An amplitude-based multiplex ddPCR assay was developed: 8 multiplexes, each containing primers and probes to amplify 3 BRCA1 exons and 1 reference gene (ALB or RPP30). A novel assay demonstrated 100% concordance with the commercial MLPA-based kit, identifying 9 specimens with different deletions in BRCA1, 1 with duplication, and 14 with the wild-type BRCA1.

Conclusions

We have designed a simple, precise, and cost-effective assay for BRCA1 rearrangement testing, based on ddPCR. The developed assay is the first multiplex ddPCR-based test that provides results in accordance with MLPA and can be used for routine clinical screening.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Miki Y, Swensen J, Shattuck-Eidens D et al (1994) A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 266:66–71

    Article  CAS  Google Scholar 

  2. Ledermann J, Harter P, Gourley C et al (2014) Olaparib maintenance therapy in patients with platinum-sensitive relapsed serous ovarian cancer: a preplanned retrospective analysis of outcomes by BRCA status in a randomised phase 2 trial. Lancet Oncol 15:852–861. https://doi.org/10.1016/S1470-2045(14)70228-1

    Article  CAS  PubMed  Google Scholar 

  3. Maistro S, Teixeira N, Encinas G et al (2016) Germline mutations in BRCA1 and BRCA2 in epithelial ovarian cancer patients in Brazil. BMC Cancer 16:934. https://doi.org/10.1186/s12885-016-2966-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Meisel C, Sadowski CE, Kohlstedt D et al (2017) Spectrum of genetic variants of BRCA1 and BRCA2 in a German single center study. Arch Gynecol Obstet 295:1227–1238. https://doi.org/10.1007/s00404-017-4330-z

    Article  CAS  PubMed  Google Scholar 

  5. Winter C, Nilsson MP, Olsson E et al (2016) Targeted sequencing of BRCA1 and BRCA2 across a large unselected breast cancer cohort suggests that one-third of mutations are somatic. Ann Oncol 27:1532–1538. https://doi.org/10.1093/annonc/mdw209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Nik-Zainal S, Davies H, Staaf J et al (2016) Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534:47–54. https://doi.org/10.1038/nature17676

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Moschetta M, George A, Kaye SB, Banerjee S (2016) BRCA somatic mutations and epigenetic BRCA modifications in serous ovarian cancer. Ann Oncol 27:1449–1455. https://doi.org/10.1093/annonc/mdw142

    Article  CAS  PubMed  Google Scholar 

  8. Kechin AA, Boyarskikh UA, Ermolenko NA et al (2018) Loss of heterozygosity in BRCA1 and BRCA2 genes in patients with ovarian cancer and probability of its use for clinical classification of variations. Bull Exp Biol Med 165:94–100. https://doi.org/10.1007/s10517-018-4107-9

    Article  CAS  PubMed  Google Scholar 

  9. Huggett JF, Foy CA, Benes V et al (2013) The digital MIQE guidelines: minimum information for publication of quantitative digital PCR experiments. Clin Chem 59:892–902. https://doi.org/10.1373/clinchem.2013.206375

    Article  CAS  PubMed  Google Scholar 

  10. Bogožalec Košir A, Demšar T, Štebih D et al (2019) Digital PCR as an effective tool for GMO quantification in complex matrices. Food Chem 294:73–78. https://doi.org/10.1016/j.foodchem.2019.05.029

    Article  CAS  PubMed  Google Scholar 

  11. Wang M, Yang J, Gai Z et al (2018) Comparison between digital PCR and real-time PCR in detection of Salmonella typhimurium in milk. Int J Food Microbiol 266:251–256. https://doi.org/10.1016/j.ijfoodmicro.2017.12.011

    Article  CAS  PubMed  Google Scholar 

  12. Rutsaert S, Bosman K, Trypsteen W et al (2018) Digital PCR as a tool to measure HIV persistence. Retrovirology 15:16. https://doi.org/10.1186/s12977-018-0399-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhang R, Chen B, Tong X et al (2018) Diagnostic accuracy of droplet digital PCR for detection of EGFR T790 M mutation in circulating tumor DNA. Cancer Manag Res 10:1209–1218. https://doi.org/10.2147/CMAR.S161382

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Demuth C, Spindler K-LG, Johansen JS et al (2018) Measuring KRAS mutations in circulating tumor DNA by droplet digital PCR and next-generation sequencing. Transl Oncol 11:1220–1224. https://doi.org/10.1016/j.tranon.2018.07.013

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hwang VJ, Maar D, Regan J et al (2014) Mapping the deletion endpoints in individuals with 22q11.2 deletion syndrome by droplet digital PCR. BMC Med Genet 15:106. https://doi.org/10.1186/s12881-014-0106-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lee SY, Kim SJ, Han S-H et al (2018) A new approach of digital PCR system for non-invasive prenatal screening of trisomy 21. Clin Chim Acta 476:75–80. https://doi.org/10.1016/j.cca.2017.11.015

    Article  CAS  PubMed  Google Scholar 

  17. Preobrazhenskaya EV, Bizin IV, Kuligina ES et al (2017) Detection of BRCA1 gross rearrangements by droplet digital PCR. Breast Cancer Res Treat 165:765–770. https://doi.org/10.1007/s10549-017-4357-7

    Article  CAS  PubMed  Google Scholar 

  18. Jones M, Williams J, Gärtner K et al (2014) Low copy target detection by droplet digital PCR through application of a novel open access bioinformatic pipeline, “definetherain”. J Virol Methods 202:46–53. https://doi.org/10.1016/j.jviromet.2014.02.020

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Della Starza I, Nunes V, Cavalli M et al (2016) Comparative analysis between RQ-PCR and digital-droplet-PCR of immunoglobulin/T-cell receptor gene rearrangements to monitor minimal residual disease in acute lymphoblastic leukaemia. Br J Haematol 174:541–549. https://doi.org/10.1111/bjh.14082

    Article  CAS  PubMed  Google Scholar 

  20. Hindson BJ, Ness KD, Masquelier DA et al (2011) High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal Chem 83:8604. https://doi.org/10.1021/AC202028G

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lin H-T, Okumura T, Yatsuda Y et al (2016) Application of droplet digital PCR for estimating vector copy number states in stem cell gene therapy. Hum Gene Ther Methods 27:197–208. https://doi.org/10.1089/hgtb.2016.059

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. McDowell DG, Burns NA, Parkes HC (1998) Localised sequence regions possessing high melting temperatures prevent the amplification of a DNA mimic in competitive PCR. Nucleic Acids Res 26:3340–3347

    Article  CAS  Google Scholar 

  23. Benita Y, Oosting RS, Lok MC et al (2003) Regionalized GC content of template DNA as a predictor of PCR success. Nucleic Acids Res 31:e99. https://doi.org/10.1093/nar/gng101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mallona I, Weiss J, Egea-Cortines M (2011) pcrEfficiency: a web tool for PCR amplification efficiency prediction. BMC Bioinform 12:404. https://doi.org/10.1186/1471-2105-12-404

    Article  Google Scholar 

  25. Minucci A, De Paolis E, Concolino P et al (2017) Competitive PCR-high resolution melting analysis (C-PCR-HRMA) for large genomic rearrangements (LGRs) detection: a new approach to assess quantitative status of BRCA1 gene in a reference laboratory. Clin Chim Acta 470:83–92. https://doi.org/10.1016/j.cca.2017.04.026

    Article  CAS  PubMed  Google Scholar 

  26. Germani A, Libi F, Maggi S et al (2018) Rapid detection of copy number variations and point mutations in BRCA1/2 genes using a single workflow by ion semiconductor sequencing pipeline. Oncotarget 9:33648–33655. https://doi.org/10.18632/oncotarget.26000

    Article  PubMed  PubMed Central  Google Scholar 

  27. Whale AS, Huggett JF, Tzonev S (2016) Fundamentals of multiplexing with digital PCR. Biomol Detect Quantif 10:15–23. https://doi.org/10.1016/j.bdq.2016.05.002

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Alcaide M, Cheung M, Bushell K et al (2019) A novel multiplex droplet digital PCR assay to identify and quantify KRAS mutations in clinical specimens. J Mol Diagn 21:214–227. https://doi.org/10.1016/j.jmoldx.2018.09.007

    Article  CAS  PubMed  Google Scholar 

  29. Pretto D, Maar D, Yrigollen CM et al (2015) Screening newborn blood spots for 22q11.2 deletion syndrome using multiplex droplet digital PCR. Clin Chem 61:182–190. https://doi.org/10.1373/clinchem.2014.230086

    Article  CAS  PubMed  Google Scholar 

  30. Dobnik D, Štebih D, Blejec A et al (2016) Multiplex quantification of four DNA targets in one reaction with bio-rad droplet digital PCR system for GMO detection. Sci Rep 6:35451. https://doi.org/10.1038/srep35451

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Alcaide M, Yu S, Bushell K et al (2016) Multiplex droplet digital PCR quantification of recurrent somatic mutations in diffuse large B-cell and follicular lymphoma. Clin Chem 62:1238–1247. https://doi.org/10.1373/CLINCHEM.2016.255315

    Article  CAS  PubMed  Google Scholar 

  32. Lievens A, Jacchia S, Kagkli D et al (2016) Measuring digital PCR quality: performance parameters and their optimization. PLoS ONE 11:e0153317. https://doi.org/10.1371/journal.pone.0153317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Funding

The work was supported by Russian State funded budget Project of ICBFM SB RAS # AAAA-A17-117020210025-5.

Author information

Authors and Affiliations

  1. Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia, 630090

    Igor Oscorbin, Andrey Kechin, Uljana Boyarskikh & Maxim Filipenko

  2. Novosibirsk State University, Novosibirsk, Russia, 630090

    Igor Oscorbin, Andrey Kechin & Maxim Filipenko

Authors
  1. Igor Oscorbin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrey Kechin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Uljana Boyarskikh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maxim Filipenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Igor Oscorbin.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All patients signed an informed written consent to participate and to have their biological specimens analyzed. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Additional information

Publisher's Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 8364 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Oscorbin, I., Kechin, A., Boyarskikh, U. et al. Multiplex ddPCR assay for screening copy number variations in BRCA1 gene. Breast Cancer Res Treat 178, 545–555 (2019). https://doi.org/10.1007/s10549-019-05425-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-019-05425-3

Keywords

Search

Navigation