Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Genome-wide cell-free DNA fragmentation in patients with cancer

Abstract

Cell-free DNA in the blood provides a non-invasive diagnostic avenue for patients with cancer1. However, characteristics of the origins and molecular features of cell-free DNA are poorly understood. Here we developed an approach to evaluate fragmentation patterns of cell-free DNA across the genome, and found that profiles of healthy individuals reflected nucleosomal patterns of white blood cells, whereas patients with cancer had altered fragmentation profiles. We used this method to analyse the fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric or bile duct cancer and 245 healthy individuals. A machine learning model that incorporated genome-wide fragmentation features had sensitivities of detection ranging from 57% to more than 99% among the seven cancer types at 98% specificity, with an overall area under the curve value of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation-based cell-free DNA analyses detected 91% of patients with cancer. The results of these analyses highlight important properties of cell-free DNA and provide a proof-of-principle approach for the screening, early detection and monitoring of human cancer.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Schematic of DELFI approach.
Fig. 2: Aberrant cfDNA fragmentation profiles in patients with cancer.
Fig. 3: cfDNA fragmentation profiles in healthy individuals and patients with cancer.
Fig. 4: Detection of cancer using DELFI.

Data availability

Sequence data used in this study have been deposited at the database of Genotypes and Phenotypes (dbGaP, study ID 34536).

Code availability

Code for analyses is available at http:github.com/Cancer-Genomics/delfi_scripts.

References

  1. Wan, J. C. M. et al. Liquid biopsies come of age: towards implementation of circulating tumour DNA. Nat. Rev. Cancer 17, 223–238 (2017).

    Article  CAS  Google Scholar 

  2. Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018).

    Article  Google Scholar 

  3. World Health Organization. Guide to Cancer Early Diagnosis https://www.who.int/cancer/publications/cancer_early_diagnosis/en/ (WHO, 2017).

  4. National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology https://www.nccn.org/professionals/physician_gls/default.aspx (accessed 16 April 2019).

  5. Phallen, J. et al. Direct detection of early-stage cancers using circulating tumor DNA. Sci. Transl. Med. 9, eaan2415 (2017).

    Article  Google Scholar 

  6. Cohen, J. D. et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 359, 926–930 (2018).

    Article  ADS  CAS  Google Scholar 

  7. Newman, A. M. et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 20, 548–554 (2014).

    Article  CAS  Google Scholar 

  8. Bettegowda, C. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med. 6, 224ra24 (2014).

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. 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).

    Article  Google Scholar 

  11. Chan, K. C. et al. Noninvasive detection of cancer-associated genome-wide hypomethylation and copy number aberrations by plasma DNA bisulfite sequencing. Proc. Natl Acad. Sci. USA 110, 18761–18768 (2013).

    Article  ADS  CAS  Google Scholar 

  12. Jiang, P. et al. Lengthening and shortening of plasma DNA in hepatocellular carcinoma patients. Proc. Natl Acad. Sci. USA 112, E1317–E1325 (2015).

    Article  CAS  Google Scholar 

  13. Wang, B. G. et al. Increased plasma DNA integrity in cancer patients. Cancer Res. 63, 3966–3968 (2003).

    CAS  PubMed  Google Scholar 

  14. Umetani, N. et al. Prediction of breast tumor progression by integrity of free circulating DNA in serum. J. Clin. Oncol. 24, 4270–4276 (2006).

    Article  CAS  Google Scholar 

  15. Chan, K. C., Leung, S. F., Yeung, S. W., Chan, A. T. & Lo, Y. M. Persistent aberrations in circulating DNA integrity after radiotherapy are associated with poor prognosis in nasopharyngeal carcinoma patients. Clin. Cancer Res. 14, 4141–4145 (2008).

    Article  CAS  Google Scholar 

  16. Mouliere, F. et al. High fragmentation characterizes tumour-derived circulating DNA. PLoS ONE 6, e23418 (2011).

    Article  ADS  CAS  Google Scholar 

  17. Mouliere, F. et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci. Transl. Med. 10, eaat4921 (2018).

    Article  Google Scholar 

  18. Snyder, M. W., Kircher, M., Hill, A. J., Daza, R. M. & Shendure, J. Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell 164, 57–68 (2016).

    Article  CAS  Google Scholar 

  19. Underhill, H. R. et al. Fragment length of circulating tumor DNA. PLoS Genet. 12, e1006162 (2016).

    Article  Google Scholar 

  20. Ulz, P. et al. Inferring expressed genes by whole-genome sequencing of plasma DNA. Nat. Genet. 48, 1273–1278 (2016).

    Article  CAS  Google Scholar 

  21. Ivanov, M., Baranova, A., Butler, T., Spellman, P. & Mileyko, V. Non-random fragmentation patterns in circulating cell-free DNA reflect epigenetic regulation. BMC Genomics 16 (Suppl. 13), S1 (2015).

    Article  Google Scholar 

  22. Jiang, P. et al. Preferred end coordinates and somatic variants as signatures of circulating tumor DNA associated with hepatocellular carcinoma. Proc. Natl Acad. Sci. USA 115, E10925–E10933 (2018).

    Article  CAS  Google Scholar 

  23. Shen, S. Y. et al. Sensitive tumour detection and classification using plasma cell-free DNA methylomes. Nature 563, 579–583 (2018).

    Article  ADS  CAS  Google Scholar 

  24. Corces, M. R. et al. The chromatin accessibility landscape of primary human cancers. Science 362, eaav1898 (2018).

    Article  ADS  Google Scholar 

  25. Polak, P. et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518, 360–364 (2015).

    Article  ADS  CAS  Google Scholar 

  26. Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).

    Article  ADS  CAS  Google Scholar 

  27. Fortin, J. P. & Hansen, K. D. Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data. Genome Biol. 16, 180 (2015).

    Article  Google Scholar 

  28. Diehl, F. et al. Circulating mutant DNA to assess tumor dynamics. Nat. Med. 14, 985–990 (2008).

    Article  CAS  Google Scholar 

  29. Phallen, J. et al. Early noninvasive detection of response to targeted therapy in non-small cell lung cancer. Cancer Res. 79, 1204–1213 (2019).

    Article  CAS  Google Scholar 

  30. Burnham, P. et al. Single-stranded DNA library preparation uncovers the origin and diversity of ultrashort cell-free DNA in plasma. Sci. Rep. 6, 27859 (2016).

    Article  ADS  CAS  Google Scholar 

  31. Sanchez, C., Snyder, M. W., Tanos, R., Shendure, J. & Thierry, A. R. New insights into structural features and optimal detection of circulating tumor DNA determined by single-strand DNA analysis. NPJ Genom. Med. 3, 31 (2018).

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

    Article  Google Scholar 

  33. Jones, S. et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci. Transl. Med. 7, 283ra53 (2015).

    Article  Google Scholar 

  34. Benjamini, Y. & Speed, T. P. Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res. 40, e72 (2012).

    Article  CAS  Google Scholar 

  35. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  Google Scholar 

  36. Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).

    Article  MathSciNet  Google Scholar 

  37. Friedman, J. H. Stochastic gradient boosting. Comput. Stat. Data Anal. 38, 367–378 (2002).

    Article  MathSciNet  Google Scholar 

  38. Efron, B. & Tibshirani, R. Improvements on cross-validation: the 632+ bootstrap method. J. Am. Stat. Assoc. 92, 548–560 (1997).

    MathSciNet  MATH  Google Scholar 

  39. Zurbenko, I. G. The Spectral Analysis of Time Series (Elsevier, 1986).

  40. Robin, X. et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12, 77 (2011).

    Article  Google Scholar 

Download references

Acknowledgements

We thank members of our laboratories for critical review of the manuscript. This work was supported, in part, by the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, the Stand Up to Cancer–Dutch Cancer Society International Translational Cancer Research Dream Team Grant (SU2C-AACR-DT1415), the Commonwealth Foundation, the Cigarette Restitution Fund, the Burroughs Wellcome Fund and the Maryland Genetics, Epidemiology and Medicine Training Program, the AACR-Janssen Cancer Interception Research Fellowship, the Mark Foundation for Cancer Research, US NIH (grants CA121113, CA006973, and CA180950), the Danish Council for Independent Research (11-105240), the Danish Council for Strategic Research (1309-00006B), the Novo Nordisk Foundation (NNF14OC0012747 and NNF17OC0025052), and the Danish Cancer Society (R133-A8520-00-S41 and R146-A9466-16-S2). Stand Up To Cancer is a program of the Entertainment Industry Foundation administered by the American Association for Cancer Research.

Reviewer information

Nature thanks Daniel De Carvalho, Ellen Heitzer and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Authors and Affiliations

Authors

Contributions

S.C., A.L., J.P., J.F., V. Adleff, R.B.S. and V.E.V. designed and planned the study, and developed and optimized experimental protocols. A.L., J.P., V. Adleff, J.E.M. and D.N.P. performed experiments. S.Ø.J., V. Anagnostou, P.F., J.N., K.M., J.B., B.D.W., H.H., K.L.v.R., M.-B.W.Ø., A.H.M., C.J.H.v.d.V., M.V., A.C., C.J.A.P., G.R.V., N.C.T.v.G., M.K., R.J.A.F., J.S.J., H.J.N., G.A.M. and C.L.A. organized patient enrolment, sample collection, and clinical data curation. S.C., A.L., J.P., J.F., V. Adleff, D.C.B., J.E.M., J.R.W., N.N., G.A.M., C.L.A., R.B.S. and V.E.V. analysed and interpreted data. S.C., A.L., J.P., J.F., R.B.S. and V.E.V. wrote the manuscript and incorporated feedback from all authors. S.C., A.L., J.P. and J.F. contributed equally to this study.

Corresponding authors

Correspondence to Robert B. Scharpf or Victor E. Velculescu.

Ethics declarations

Competing interests

S.C., A.L., J.P., J.F., V. Adleff, R.B.S. and V.E.V. are inventors on patent applications (62/673,516 and 62/795,900) submitted by Johns Hopkins University related to cell-free DNA for cancer detection. V.E.V. is a founder of Delfi Diagnostics and Personal Genome Diagnostics, a member of their Scientific Advisory Boards and Boards of Directors, and owns Delfi Diagnostics and Personal Genome Diagnostics stock, which are subject to certain restrictions under university policy. Within the last five years, V.E.V. has been an advisor to Daiichi Sankyo, Janssen Diagnostics, Ignyta, and Takeda Pharmaceuticals. The terms of these arrangements are managed by Johns Hopkins University in accordance with its conflict of interest policies.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Simulations of non-invasive cancer detection based on number of alterations analysed and tumour-derived cfDNA fragment distributions.

a, Monte Carlo simulations were performed using different numbers of tumour-specific alterations to evaluate the probability of detecting cancer alterations in cfDNA at the indicated fraction of tumour-derived molecules. The simulations were performed assuming an average of 2,000 genome equivalents of cfDNA and the requirement of five or more observations of any alteration. These analyses indicate that increasing the number of tumour-specific alterations improves the sensitivity of detection of circulating tumour DNA. b, Cumulative density functions of cfDNA fragment lengths of 42 loci containing tumour-specific alterations from 30 patients with breast, colorectal, lung, or ovarian cancer are shown with 95% confidence bands (orange). Lengths of mutant cfDNA fragments were significantly different in size from wild-type cfDNA fragments (blue) at these loci. c, GC content was similar for mutated and non-mutated fragments. d, GC content was not correlated to fragment length.

Extended Data Fig. 2 Germline and haematopoietic cfDNA fragment distributions.

a, Cumulative density functions of fragment lengths at 44 loci containing germline alterations (non-tumour derived) from 38 patients with breast, colorectal, lung or ovarian cancer are shown with 95% confidence bands. Fragments with germline mutations (orange) were comparable in length to wild-type cfDNA fragment lengths (blue). b, Cumulative density functions of fragment lengths at 41 loci containing haematopoietic alterations (non-tumour derived) from 28 patients with breast, colorectal, lung or ovarian cancer are shown with 95% confidence bands. After correction for multiple testing, there were no significant differences (α = 0.05) in the size distributions of mutated haematopoietic cfDNA fragments (orange) and wild-type cfDNA fragments (blue).

Extended Data Fig. 3 cfDNA fragmentation in healthy individuals and patients with lung cancer.

a, cfDNA fragment lengths are shown for healthy individuals (n = 30, grey) and patients with lung cancer (n = 8, blue). bd, cfDNA fragmentation profiles from healthy individuals (n = 30) had high correlations, whereas patients with lung cancer (n = 8) had lower correlations to median fragmentation profiles of lymphocytes (b), lymphocyte nucleosome distances (c) and healthy cfDNA (d). Pearson correlations are shown with box plots depicting minimum, 25th percentile, median, 75th percentile, and maximum values. e, High coverage (9×) WGS data were subsampled to 2×, 1×, 0.5×, 0.2× and 0.1×-fold coverage. Mean centred genome-wide fragmentation profiles in 5-Mb bins for 30 healthy individuals and 8 patients with lung cancer are depicted for each subsampled fold coverage with median profiles shown in blue. f, Pearson correlation of subsampled profiles to initial profile at 9× coverage for healthy individuals and patients with lung cancer.

Extended Data Fig. 4 cfDNA fragmentation profiles and sequence alterations during therapy.

Detection and monitoring of cancer in serial blood draws from patients with non-small cell lung cancer (n = 19) undergoing treatment with targeted tyrosine kinase inhibitors (black arrows) was performed using targeted sequencing (top) as previously reported29, and genome-wide fragmentation profiles (bottom). For each case, the vertical axis of the bottom panel displays −1 times the Pearson correlation of each sample to the median healthy cfDNA fragmentation profile. Error bars depict confidence intervals from binomial tests for mutant allele fractions, and confidence intervals calculated using Fisher transformation for genome-wide fragmentation profiles. Although the approaches analyse different aspects of cfDNA (whole genome compared with specific alterations), the targeted sequencing and fragmentation profiles were similar for patients responding to therapy as well as those with stable or progressive disease. As fragmentation profiles reflect both genomic and epigenomic alterations (whereas mutant allele fractions only reflect individual mutations), mutant allele fractions alone may not reflect the absolute level of correlation of fragmentation profiles to healthy individuals.

Extended Data Fig. 5 Profiles of cfDNA fragment lengths in copy neutral regions in healthy individuals and one patient with colorectal cancer.

a, The fragmentation profiles in 211 copy neutral windows in chromosomes 1–6 are shown for 25 randomly selected healthy individuals (grey). For a patient with colorectal cancer (CGCRC291) with an estimated mutant allele fraction of 20%, we diluted the cancer fragment length profile to an approximate 10% tumour contribution (blue). a, b, Although the marginal densities of the fragment profiles for the healthy samples and patient with cancer show substantial overlap (a, right), the fragmentation profiles are different as can be seen through visualization of the fragmentation profiles (a, left) and by the separation of the patient with colorectal cancer from the healthy samples (n = 25) in a principal component analysis (b).

Extended Data Fig. 6 Genome-wide GC correction of cfDNA fragments.

To estimate and control for the effects of GC content on sequencing coverage, we calculated coverage in non-overlapping 100-kb genomic windows across the autosomes. For each window, we calculated the average GC of the aligned fragments. a, LOESS smoothing of raw coverage (top row) for two randomly selected healthy subjects (CGPLH189 and CGPLH380) and two patients with cancer (CGPLLU161 and CGPLBR24) with undetectable aneuploidy (PA score < 2.35). After subtracting the average coverage predicted by the LOESS model, the residuals were rescaled to the median autosomal coverage (bottom row). As fragment length may also result in coverage biases, we performed this GC correction procedure separately for short (≤150 bp) and long (>150 bp) fragments. Although the 100-kb bins on chromosome 19 (blue points) consistently have less coverage than predicted by the LOESS model, we did not implement a chromosome-specific correction as such an approach would remove the effects of chromosomal copy number on coverage. b, Overall, we found a limited correlation between short or long fragment coverage and GC content after correction among healthy individuals (n = 211, interquartile range: −0.03–0.03) and patients with cancer (n = 128, interquartile range: −0.06–0.02) with a PA score < 3. Box plots depict 25th percentile, median, and 75th percentile values.

Extended Data Fig. 7 Machine learning model.

a, We used gradient tree boosting machine learning to examine whether cfDNA can be categorized as having characteristics of a patient with cancer or a healthy individual. The machine learning model included fragmentation size and coverage characteristics in windows throughout the genome, as well as chromosomal arm and mitochondrial DNA copy numbers. We used a tenfold cross-validation approach in which each sample is randomly assigned to a fold, and nine of the folds (90% of the data) are used for training and one fold (10% of the data) is used for testing. The prediction accuracy from a single cross-validation is an average over the ten possible combinations of test and training sets. As this prediction accuracy can reflect bias from the initial randomization of patients, we repeat the entire procedure, including the randomization of patients to folds, ten times. For all cases, feature selection and model estimation were performed on training data and were validated on test data, and the test data were never used for feature selection. Ultimately, we obtained a DELFI score that could be used to classify individuals as likely to be healthy or having cancer. b, Distribution of AUCs across the repeated tenfold cross-validation. The 25th, 50th and 75th percentiles of the 100 AUCs for the cohort of 215 healthy individuals and 208 patients with cancer are indicated by dashed lines.

Extended Data Fig. 8 Whole-genome analyses of chromosomal arm copy number changes and mitochondrial genome representation.

a, Z-scores for each autosome arm are depicted for healthy individuals (n = 215) and patients with cancer (n = 208). The vertical axis depicts normal copy at zero with positive and negative values indicating arm gains and losses, respectively. Z-scores greater than 50 or less than −50 are thresholded at the indicated values. b, The fraction of reads mapping to the mitochondrial genome is depicted for healthy individuals (n = 215) and patients with cancer (n = 208). Box plots depict the minimum, 25th percentile, median, 75th percentile, and maximum values.

Extended Data Fig. 9 DELFI detection of cancer and tissue of origin prediction.

a, Analyses of individual cancer types using DELFI had AUCs ranging from 0.86 to >0.99. b, Receiver operator characteristics for detection of cancer using cfDNA fragmentation profiles and other genome-wide features in a machine learning approach are depicted for a cohort of 215 healthy individuals and each stage of 208 patients with cancer with ≥95% specificity shaded in blue. c, Receiver operator characteristics for DELFI tissue prediction of bile duct, breast, colorectal, gastric, lung, ovarian or pancreatic cancer are depicted. To increase sample sizes within cancer type classes, we included cases detected with a 90% specificity, and the lung cancer cohort was supplemented with the addition of baseline cfDNA data from 18 patients with lung cancer with prior treatment36. d, DELFI tissue of origin prediction.

Extended Data Fig. 10 Detection of cancer using DELFI and mutation-based cfDNA approaches.

DELFI (green) and targeted sequencing10 for mutation identification (blue) were performed independently in a cohort of 126 patients with breast, bile duct, colorectal, gastric, lung or ovarian cancer. The number of individuals detected by each approach and in combination are indicated for DELFI detection with a specificity of 98%, targeted sequencing specificity at >99%, and a combined specificity of 98%. ND, not detected.

Supplementary information

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1-8.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cristiano, S., Leal, A., Phallen, J. et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 570, 385–389 (2019). https://doi.org/10.1038/s41586-019-1272-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-019-1272-6

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer