Advertisement

Validation Strategy for Ultrasensitive Mutation Detection

  • Marija Debeljak
  • Michael Noë
  • Stacy L. Riel
  • Lisa M. Haley
  • Alexis L. Norris
  • Derek A. Anderson
  • Emily M. Adams
  • Masaya Suenaga
  • Katie F. Beierl
  • Ming-Tseh Lin
  • Michael G. Goggins
  • Christopher D. Gocke
  • James R. Eshleman
Original Research Article
  • 65 Downloads

Abstract

Background

Ultrasensitive detection of low-abundance DNA point mutations is a challenging molecular biology problem, because nearly identical mutant and wild-type molecules exhibit crosstalk. Reliable ultrasensitive point mutation detection will facilitate early detection of cancer and therapeutic monitoring of cancer patients.

Objective

The objective of this study was to develop a method to correct errors in low-level cell line mixes.

Materials and Methods

We tested sample mixes with digital-droplet PCR (ddPCR) and next-generation sequencing.

Results

We introduced two corrections: baseline variant allele frequency (VAF) in the parental cell line was used to correct for copy number variation; and haplotype counting was used to correct errors in cell counting and pipetting. We found ddPCR to have better correlation for detecting low-level mutations without applying any correction (R2 = 0.80) and be more linear after introducing both corrections (R2 = 0.99).

Conclusions

The VAF correction was found to be more significant than haplotype correction. It is imperative that various technologies be evaluated against each other and laboratories be provided with defined quality control samples for proficiency testing.

Notes

Acknowledgements

We thank Dr. Zhen Zhang for his insight and expert statistical analysis. We acknowledge Drs. Lori Sokoll, Jun Yu, Maria Bettinotti, Annette Jackson, Bo Song (Bio-Rad), and Kenneth Pienta, in addition to Brian Iglehart and Don Vindivich, for helpful discussions.

Compliance with Ethical Standards

Conflict of interest

Marija Debeljak, Stacy L. Riel, Lisa M. Haley, Alexis L. Norris, Derek A. Anderson, Emily M. Adams, Masaya Suenaga, Katie F. Beierl, Ming-Tseh Lin, Michael G. Goggins, Christopher D. Gocke, James R. Eshleman, and Michael Noë have no conflicts of interest that are directly relevant to the content of this work.

Funding

This work was funded in part by The Sol Goldman Pancreatic Cancer Research Center, the Stringer Foundation, the Michael Rolfe Pancreatic Cancer Foundation, Mary Lou Wootton Pancreatic Cancer Research Fund, and the Institute for Clinical and Translational Research (ICTR) Accelerated Translational Incubator Pilot (ATIP) Program.

Ethical Approval and Informed Consent

No patients were enrolled and nor was protected health information used in this study.

Supplementary material

40291_2018_350_MOESM1_ESM.pdf (633 kb)
Supplementary material 1 (PDF 633 kb)

References

  1. 1.
    Samuel N, Hudson TJ. The molecular and cellular heterogeneity of pancreatic ductal adenocarcinoma. Nat Rev Gastroenterol Hepatol. 2012;9:77–87.CrossRefGoogle Scholar
  2. 2.
    Network Cancer Genome Atlas. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490:61–70.CrossRefGoogle Scholar
  3. 3.
    Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511:543–50.CrossRefGoogle Scholar
  4. 4.
    Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455:1061–8.CrossRefGoogle Scholar
  5. 5.
    Network Cancer Genome Atlas. Comprehensive molecular characterization of human colon and rectal cancer. Nature. 2012;487:330–7.CrossRefGoogle Scholar
  6. 6.
    Taylor BS, Schultz N, Hieronymus H, Gopalan A, Xiao Y, Carver BS, et al. Integrative genomic profiling of human prostate cancer. Cancer Cell. 2010;18:11–22.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Diehl F, Schmidt K, Choti MA, Romans K, Goodman S, Li M, et al. Circulating mutant DNA to assess tumor dynamics. Nat Med. 2008;14:985–90.CrossRefPubMedGoogle Scholar
  8. 8.
    Sausen M, Phallen J, Adleff V, Jones S, Leary RJ, Barrett MT, et al. Clinical implications of genomic alterations in the tumour and circulation of pancreatic cancer patients. Nat Commun. 2015;6:7686.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014; 6:224ra24.Google Scholar
  10. 10.
    Tie J, Kinde I, Wang Y, Wong HL, Roebert J, Christie M, et al. Circulating tumor DNA as an early marker of therapeutic response in patients with metastatic colorectal cancer. Ann Oncol. 2015;26:1715–22.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Nowell PC, Hungerford DA. Chromosome studies on normal and leukemic human leukocytes. J Natl Cancer Inst. 1960;25:85–109.PubMedGoogle Scholar
  12. 12.
    Rowley JD. Letter: a new consistent chromosomal abnormality in chronic myelogenous leukaemia identified by quinacrine fluorescence and Giemsa staining. Nature. 1973;243:290–3.CrossRefPubMedGoogle Scholar
  13. 13.
    Groffen J, Stephenson JR, Heisterkamp N, de Klein A, Bartram CR, Grosveld G. Philadelphia chromosomal breakpoints are clustered within a limited region, bcr, on chromosome 22. Cell. 1984;36:93–9.CrossRefPubMedGoogle Scholar
  14. 14.
    Do H, Wong SQ, Li J, Dobrovic A. Reducing sequence artifacts in amplicon-based massively parallel sequencing of formalin-fixed paraffin-embedded DNA by enzymatic depletion of uracil-containing templates. Clin Chem. 2013;59:1376–83.CrossRefPubMedGoogle Scholar
  15. 15.
    Lin MT, Mosier SL, Thiess M, Beierl KF, Debeljak M, Tseng LH, et al. Clinical validation of KRAS, BRAF, and EGFR mutation detection using next-generation sequencing. Am J Clin Pathol. 2014;141:856–66.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Chen G, Mosier S, Gocke CD, Lin MT, Eshleman JR. Cytosine deamination is a major cause of baseline noise in next-generation sequencing. Mol Diagn Ther. 2014;18:587–93.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Kinde I, Wu J, Papadopoulos N, Kinzler KW, Vogelstein B. Detection and quantification of rare mutations with massively parallel sequencing. Proc Natl Acad Sci USA. 2011;108:9530–5.CrossRefPubMedGoogle Scholar
  18. 18.
    Schmitt MW, Kennedy SR, Salk JJ, Fox EJ, Hiatt JB, Loeb LA. Detection of ultra-rare mutations by next-generation sequencing. Proc Natl Acad Sci U S A. 2012;109:14508–13.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Jabara CB, Jones CD, Roach J, Anderson JA, Swanstrom R. Accurate sampling and deep sequencing of the HIV-1 protease gene using a Primer ID. Proc Natl Acad Sci USA. 2011;108:20166–71.CrossRefPubMedGoogle Scholar
  20. 20.
    Fletcher RH. Carcinoembryonic antigen. Ann Intern Med. 1986;104:66–73.CrossRefPubMedGoogle Scholar
  21. 21.
    Garcia-Murillas I, Schiavon G, Weigelt B, Ng C, Hrebien S, Cutts RJ, et al. Mutation tracking in circulating tumor DNA predicts relapse in early breast cancer. Sci Transl Med. 2015;7:302ra133.Google Scholar
  22. 22.
    Jobbagy Z, van Atta R, Murphy KM, Eshleman JR, Gocke CD. Evaluation of the Cepheid GeneXpert BCR-ABL assay. J Mol Diagn. 2007;9:220–7.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Amant F, Verheecke M, Wlodarska I, Dehaspe L, Brady P, Brison N, et al. Presymptomatic identification of cancers in pregnant women during noninvasive prenatal testing. JAMA Oncol. 2015;1:814–9.CrossRefPubMedGoogle Scholar
  24. 24.
    Bianchi DW, Chudova D, Sehnert AJ, Bhatt S, Murray K, Prosen TL, et al. Noninvasive prenatal testing and incidental detection of occult maternal malignancies. JAMA. 2015;314:162–9.CrossRefPubMedGoogle Scholar
  25. 25.
    Jones S, Chen WD, Parmigiani G, Diehl F, Beerenwinkel N, Antal T, et al. Comparative lesion sequencing provides insights into tumor evolution. Proc Natl Acad Sci USA. 2008;105:4283–8.CrossRefPubMedGoogle Scholar
  26. 26.
    Yachida S, Jones S, Bozic I, Antal T, Leary R, Fu B, et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature. 2010;467:1114–7.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Katz MH, Fleming JB, Bhosale P, Varadhachary G, Lee JE, Wolff R, et al. Response of borderline resectable pancreatic cancer to neoadjuvant therapy is not reflected by radiographic indicators. Cancer. 2012;118:5749–56.CrossRefPubMedGoogle Scholar
  28. 28.
    Freidin MB, Freydina DV, Leung M, Montero Fernandez A, Nicholson AG, Lim E. Circulating tumor DNA outperforms circulating tumor cells for KRAS mutation detection in thoracic malignancies. Clin Chem. 2015;61:1299–304.CrossRefPubMedGoogle Scholar
  29. 29.
    Shinozaki M, O’Day SJ, Kitago M, Amersi F, Kuo C, Kim J, et al. Utility of circulating B-RAF DNA mutation in serum for monitoring melanoma patients receiving biochemotherapy. Clin Cancer Res. 2007;13:2068–74.CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Tsao SC, Weiss J, Hudson C, Christophi C, Cebon J, Behren A, et al. Monitoring response to therapy in melanoma by quantifying circulating tumour DNA with droplet digital PCR for BRAF and NRAS mutations. Sci Rep. 2015;5:11198.CrossRefPubMedGoogle Scholar
  31. 31.
    Debeljak M, Freed DN, Welch JA, Haley L, Beierl K, Iglehart BS, et al. Haplotype counting by next-generation sequencing for ultrasensitive human DNA detection. J Mol Diagn. 2014;16:495–503.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Efron B, Tibshirani R. An introduction to the bootstrap. New York: Chapman & Hall; 1993. pp. xvi, 436.Google Scholar
  33. 33.
    Hancock DK, Tully LA, Levin BC. A standard reference material to determine the sensitivity of techniques for detecting low-frequency mutations, SNPs, and heteroplasmies in mitochondrial DNA. Genomics. 2005;86:446–61.CrossRefPubMedGoogle Scholar
  34. 34.
    Liu C, Zhou W, Zhang T, Jiang K, Li H, Dong W. An automated approach to classification of duplex assay for digital droplet PCR. J Bioinform Comput Biol. 2018;16:1850003.  https://doi.org/10.1142/S0219720018500038.CrossRefPubMedGoogle Scholar
  35. 35.
    Brink BG, Meskas J, Brinkman RR. ddPCRclust—an R package and Shiny app for automated analysis of multiplexed ddPCR data. Bioinformatics. 2018.  https://doi.org/10.1093/bioinformatics/bty136.PubMedGoogle Scholar
  36. 36.
    Boonstra JJ, van Marion R, Beer DG, Lin L, Chaves P, Ribeiro C, et al. Verification and unmasking of widely used human esophageal adenocarcinoma cell lines. J Natl Cancer Inst. 2010;102:271–4.CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Christensen TM, Jama M, Ponek V, Lyon E, Wilson JA, Hoffmann ML, et al. Design, development, validation, and use of synthetic nucleic acid controls for diagnostic purposes and application to cystic fibrosis testing. J Mol Diagn. 2007;9:315–9.CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Cohen JD, Javed AA, Thoburn C, Wong F, Tie J, Gibbs P, et al. Combined circulating tumor DNA and protein biomarker-based liquid biopsy for the earlier detection of pancreatic cancers. Proc Natl Acad Sci U S A. 2017;114:10202–7.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Lang AH, Drexel H, Geller-Rhomberg S, Stark N, Winder T, Geiger K, et al. Optimized allele-specific real-time PCR assays for the detection of common mutations in KRAS and BRAF. J Mol Diagn. 2011;13:23–8.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Dressman D, Yan H, Traverso G, Kinzler KW, Vogelstein B. Transforming single DNA molecules into fluorescent magnetic particles for detection and enumeration of genetic variations. Proc Natl Acad Sci USA. 2003;100:8817–22.CrossRefPubMedGoogle Scholar
  41. 41.
    Newman AM, Bratman SV, To J, Wynne JF, Eclov NC, Modlin LA, et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med. 2014;20:548–54.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Shi C, Eshleman SH, Jones D, Fukushima N, Hua L, Parker AR, et al. LigAmp for sensitive detection of single-nucleotide differences. Nat Methods. 2004;1:141–7.CrossRefPubMedGoogle Scholar
  43. 43.
    Milbury CA, Zhong Q, Lin J, Williams M, Olson J, Link DR, et al. Determining lower limits of detection of digital PCR assays for cancer-related gene mutations. Biomol Detect Quantif. 2014;1:8–22.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Sykes PJ, Neoh SH, Brisco MJ, Hughes E, Condon J, Morley AA. Quantitation of targets for PCR by use of limiting dilution. Biotechniques. 1992;13:444–9.PubMedGoogle Scholar
  45. 45.
    Lou DI, Hussmann JA, McBee RM, Acevedo A, Andino R, Press WH, et al. High-throughput DNA sequencing errors are reduced by orders of magnitude using circle sequencing. Proc Natl Acad Sci USA. 2013;110:19872–7.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Marija Debeljak
    • 1
  • Michael Noë
    • 1
    • 2
  • Stacy L. Riel
    • 1
  • Lisa M. Haley
    • 1
  • Alexis L. Norris
    • 1
  • Derek A. Anderson
    • 1
  • Emily M. Adams
    • 1
  • Masaya Suenaga
    • 1
  • Katie F. Beierl
    • 1
  • Ming-Tseh Lin
    • 1
  • Michael G. Goggins
    • 1
    • 2
    • 3
    • 4
  • Christopher D. Gocke
    • 1
    • 2
  • James R. Eshleman
    • 1
    • 2
    • 4
  1. 1.Department of PathologyJohns Hopkins University, Johns Hopkins Medical InstitutionsBaltimoreUSA
  2. 2.Department of OncologyJohns Hopkins University, Johns Hopkins Medical InstitutionsBaltimoreUSA
  3. 3.Department of MedicineJohns Hopkins University, Johns Hopkins Medical InstitutionsBaltimoreUSA
  4. 4.The Sol Goldman Pancreatic Cancer Research CenterJohns Hopkins University School of MedicineBaltimoreUSA

Personalised recommendations