An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage

Abstract

Circulating tumor DNA (ctDNA) is a promising biomarker for noninvasive assessment of cancer burden, but existing ctDNA detection methods have insufficient sensitivity or patient coverage for broad clinical applicability. Here we introduce cancer personalized profiling by deep sequencing (CAPP-Seq), an economical and ultrasensitive method for quantifying ctDNA. We implemented CAPP-Seq for non–small-cell lung cancer (NSCLC) with a design covering multiple classes of somatic alterations that identified mutations in >95% of tumors. We detected ctDNA in 100% of patients with stage II–IV NSCLC and in 50% of patients with stage I, with 96% specificity for mutant allele fractions down to 0.02%. Levels of ctDNA were highly correlated with tumor volume and distinguished between residual disease and treatment-related imaging changes, and measurement of ctDNA levels allowed for earlier response assessment than radiographic approaches. Finally, we evaluated biopsy-free tumor screening and genotyping with CAPP-Seq. We envision that CAPP-Seq could be routinely applied clinically to detect and monitor diverse malignancies, thus facilitating personalized cancer therapy.

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Figure 1: Development of CAPP-Seq.
Figure 2: Analytical performance.
Figure 3: Sensitivity and specificity analysis.
Figure 4: Noninvasive detection and monitoring of ctDNA.

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References

  1. 1

    Taniguchi, K. et al. Quantitative detection of EGFR mutations in circulating tumor DNA derived from lung adenocarcinomas. Clin. Cancer Res. 17, 7808–7815 (2011).

  2. 2

    Rosell, R. et al. Screening for epidermal growth factor receptor mutations in lung cancer. N. Engl. J. Med. 361, 958–967 (2009).

  3. 3

    Kuang, Y. et al. Noninvasive detection of EGFR T790M in gefitinib or erlotinib resistant non-small cell lung cancer. Clin. Cancer Res. 15, 2630–2636 (2009).

  4. 4

    Gautschi, O. et al. Origin and prognostic value of circulating KRAS mutations in lung cancer patients. Cancer Lett. 254, 265–273 (2007).

  5. 5

    Leary, R.J. et al. Development of personalized tumor biomarkers using massively parallel sequencing. Sci. Transl. Med. 2, 20ra14 (2010).

  6. 6

    McBride, D.J. et al. Use of cancer-specific genomic rearrangements to quantify disease burden in plasma from patients with solid tumors. Genes Chromosom. Cancer 49, 1062–1069 (2010).

  7. 7

    He, J. et al. IgH gene rearrangements as plasma biomarkers in non-Hodgkin's lymphoma patients. Oncotarget 2, 178–185 (2011).

  8. 8

    Forshew, T. et al. Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci. Transl. Med. 4, 136ra168 (2012).

  9. 9

    Leary, R.J. et al. Detection of chromosomal alterations in the circulation of cancer patients with whole-genome sequencing. Sci. Transl. Med. 4, 162ra154 (2012).

  10. 10

    Narayan, A. et al. Ultrasensitive measurement of hotspot mutations in tumor DNA in blood using error-suppressed multiplexed deep sequencing. Cancer Res. 72, 3492–3498 (2012).

  11. 11

    Dawson, S.J. et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N. Engl. J. Med. 368, 1199–1209 (2013).

  12. 12

    Murtaza, M. et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497, 108–112 (2013).

  13. 13

    Crowley, E., Di Nicolantonio, F., Loupakis, F. & Bardelli, A. Liquid biopsy: monitoring cancer-genetics in the blood. Nat. Rev. Clin. Oncol. 10, 472–484 (2013).

  14. 14

    Forbes, S.A. et al. COSMIC (the Catalogue of Somatic Mutations in Cancer): a resource to investigate acquired mutations in human cancer. Nucleic Acids Res. 38, D652–D657 (2010).

  15. 15

    Ding, L. et al. Somatic mutations affect key pathways in lung adenocarcinoma. Nature 455, 1069–1075 (2008).

  16. 16

    Youn, A. & Simon, R. Identifying cancer driver genes in tumor genome sequencing studies. Bioinformatics 27, 175–181 (2011).

  17. 17

    Bergethon, K. et al. ROS1 rearrangements define a unique molecular class of lung cancers. J. Clin. Oncol. 30, 863–870 (2012).

  18. 18

    Kwak, E.L. et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N. Engl. J. Med. 363, 1693–1703 (2010).

  19. 19

    Pao, W. & Hutchinson, K.E. Chipping away at the lung cancer genome. Nat. Med. 18, 349–351 (2012).

  20. 20

    Imielinski, M. et al. Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell 150, 1107–1120 (2012).

  21. 21

    Govindan, R. et al. Genomic landscape of non-small cell lung cancer in smokers and never-smokers. Cell 150, 1121–1134 (2012).

  22. 22

    Koivunen, J.P. et al. EML4-ALK fusion gene and efficacy of an ALK kinase inhibitor in lung cancer. Clin. Cancer Res. 14, 4275–4283 (2008).

  23. 23

    Rikova, K. et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131, 1190–1203 (2007).

  24. 24

    Fan, H.C., Blumenfeld, Y.J., Chitkara, U., Hudgins, L. & Quake, S.R. Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA from maternal blood. Proc. Natl. Acad. Sci. USA 105, 16266–16271 (2008).

  25. 25

    Su, Z. et al. A platform for rapid detection of multiple oncogenic mutations with relevance to targeted therapy in non-small-cell lung cancer. J. Mol. Diagn. 13, 74–84 (2011).

  26. 26

    Kobayashi, S. et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 352, 786–792 (2005).

  27. 27

    Iyengar, P. & Timmerman, R.D. Stereotactic ablative radiotherapy for non-small cell lung cancer: rationale and outcomes. J. Natl. Compr. Canc. Netw. 10, 1514–1520 (2012).

  28. 28

    Nesbitt, J.C., Putnam, J.B. Jr., Walsh, G.L., Roth, J.A. & Mountain, C.F. Survival in early-stage non-small cell lung cancer. Ann. Thorac. Surg. 60, 466–472 (1995).

  29. 29

    Aberle, D.R. et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365, 395–409 (2011).

  30. 30

    Diehl, F. et al. Detection and quantification of mutations in the plasma of patients with colorectal tumors. Proc. Natl. Acad. Sci. USA 102, 16368–16373 (2005).

  31. 31

    Diehl, F. et al. Analysis of mutations in DNA isolated from plasma and stool of colorectal cancer patients. Gastroenterology 135, 489–498 (2008).

  32. 32

    Chan, K.C. et al. Cancer genome scanning in plasma: detection of tumor-associated copy number aberrations, single-nucleotide variants, and tumoral heterogeneity by massively parallel sequencing. Clin. Chem. 59, 211–224 (2013).

  33. 33

    Heitzer, E. et al. Tumor associated copy number changes in the circulation of patients with prostate cancer identified through whole-genome sequencing. Genome Med. 5, 30 (2013).

  34. 34

    Schmitt, M.W. et al. Detection of ultra-rare mutations by next-generation sequencing. Proc. Natl. Acad. Sci. USA 109, 14508–14513 (2012).

  35. 35

    Shiroguchi, K., Jia, T.Z., Sims, P.A. & Xie, X.S. Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes. Proc. Natl. Acad. Sci. USA 109, 1347–1352 (2012).

  36. 36

    Quail, M.A. et al. Optimal enzymes for amplifying sequencing libraries. Nat. Methods 9, 10–11 (2012).

  37. 37

    Oyola, S.O. et al. Optimizing Illumina next-generation sequencing library preparation for extremely AT-biased genomes. BMC Genomics 13, 1 (2012).

  38. 38

    Fisher, S. et al. A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries. Genome Biol. 12, R1 (2011).

  39. 39

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

  40. 40

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

  41. 41

    Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

  42. 42

    Koboldt, D.C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).

  43. 43

    Fisher, R.A. Statistical Methods for Research Workers (Oliver and Boyd, 1925).

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Acknowledgements

We thank S. Quake and members of his lab for suggestions and N. Neff for technical assistance. This work was supported by the US Department of Defense (M.D., A.A.A., A.M.N.), the US National Institutes of Health Director's New Innovator Award Program (M.D.; 1-DP2-CA186569), the Ludwig Institute for Cancer Research (M.D., A.A.A.), the Radiological Society of North America (S.V.B.; #RR1221), an Association of American Cancer Institutes Translational Cancer Research Fellowship (S.V.B.) and a grant from both the Siebel Stem Cell Institute and the Thomas and Stacey Siebel Foundation (A.M.N.). A.A.A. and M.D. are supported by Doris Duke Clinical Scientist Development Awards.

Author information

A.M.N., S.V.B., A.A.A. and M.D. developed the concept, designed the experiments, analyzed the data and wrote the manuscript. S.V.B. performed the molecular biology experiments, and A.M.N. performed the bioinformatics analyses. C.L.L. helped develop analytical pipeline software. S.V.B., J.T., J.F.W., N.C.W.E., L.A.M., J.W.N., H.A.W., R.E.M., J.B.S., B.W.L. Jr. and M.D. provided patient specimens. A.A.A. and M.D. contributed equally as senior authors. All authors commented on the manuscript at all stages.

Correspondence to Ash A Alizadeh or Maximilian Diehn.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Methods (PDF 2806 kb)

Supplementary Table 1

NSCLC selector design and coordinates (XLSX 115 kb)

Supplementary Table 2

Quality control metrics of NGS libraries (XLSX 69 kb)

Supplementary Table 3

Clinical history of patients in this study and somatic variants discovered by CAPP-Seq (XLSX 31 kb)

Supplementary Table 4

Detection of somatic variants in plasma DNA from patients with NSCLC (XLSX 78 kb)

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Newman, A., Bratman, S., To, J. et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat Med 20, 548–554 (2014) doi:10.1038/nm.3519

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