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Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes

An Author Correction to this article was published on 07 September 2020

This article has been updated


Improving early cancer detection has the potential to substantially reduce cancer-related mortality. Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP–seq) is a highly sensitive assay capable of detecting early-stage tumors. We report accurate classification of patients across all stages of renal cell carcinoma (RCC) in plasma (area under the receiver operating characteristic (AUROC) curve of 0.99) and demonstrate the validity of this assay to identify patients with RCC using urine cell-free DNA (cfDNA; AUROC of 0.86).

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Fig. 1: Overview of analytical approach.
Fig. 2: Classification of RCC using cfDNA methylomes.

Data availability

The cfMeDIP–seq next-generation sequencing data for patient samples that support the findings of this study are available upon request from the corresponding author (M.L.F.) to comply with the ethical and patient privacy regulations of the Dana-Farber Cancer Institute. All requests for raw and analyzed data will be promptly reviewed by the Belfer Office for Dana-Farber Innovations to verify whether the request is subject to any intellectual property or confidentiality obligations. Any data and materials that can be shared will be released via a data transfer agreement.

Code availability

All code used to process the data and conduct the analyses described in the Methods is located in a publicly available GitHub repository at This code is made available under an MIT license.

Change history

  • 07 September 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


  1. 1.

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

    CAS  Article  Google Scholar 

  2. 2.

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

    CAS  Article  Google Scholar 

  3. 3.

    Dudley, J. C. et al. Detection and surveillance of bladder cancer using urine tumor DNA. Cancer Discov. 9, 500–509 (2019).

    CAS  Article  Google Scholar 

  4. 4.

    Sina, A. A. I. et al. Epigenetically reprogrammed methylation landscape drives the DNA self-assembly and serves as a universal cancer biomarker. Nat. Commun. 9, 4915 (2018).

    Article  Google Scholar 

  5. 5.

    Shen, S. Y., Burgener, J. M., Bratman, S. V. & De Carvalho, D. D. Preparation of cfMeDIP–seq libraries for methylome profiling of plasma cell-free DNA. Nat. Protoc. 14, 2749–2780 (2019).

    CAS  Article  Google Scholar 

  6. 6.

    Zill, O. A. et al. The landscape of actionable genomic alterations in cell-free circulating tumor DNA from 21,807 advanced cancer patients. Clin. Cancer Res. 24, 3528–3538 (2018).

    CAS  Article  Google Scholar 

  7. 7.

    Pal, S. K. et al. Evolution of circulating tumor DNA profile from first-line to subsequent therapy in metastatic renal cell carcinoma. Eur. Urol. 72, 557–564 (2017).

    CAS  Article  Google Scholar 

  8. 8.

    Hauser, S. et al. Serum DNA hypermethylation in patients with kidney cancer: results of a prospective study. Anticancer Res. 33, 4651–4656 (2013).

    CAS  PubMed  Google Scholar 

  9. 9.

    Skrypkina, I. et al. Concentration and methylation of cell-free DNA from blood plasma as diagnostic markers of renal cancer. Dis. Markers 2016, 3693096 (2016).

    Article  Google Scholar 

  10. 10.

    TCGA Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).

    Article  Google Scholar 

  11. 11.

    Lasseter, K. et al. Plasma cell-free DNA variant analysis compared with methylated DNA analysis in renal cell carcinoma. Genet. Med. (2020).

  12. 12.

    Schoots, I. G., Zaccai, K., Hunink, M. G. & Verhagen, P. C. M. S. Bosniak classification for complex renal cysts reevaluated: a systematic review. J. Urol. 198, 12–21 (2017).

    Article  Google Scholar 

  13. 13.

    Gill, I. S., Aron, M., Gervais, D. A. & Jewett, M. A. Clinical practice. Small renal mass.N. Engl. J. Med. 362, 624–634 (2010).

    CAS  Article  Google Scholar 

  14. 14.

    Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).

    CAS  Article  Google Scholar 

  15. 15.

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

    CAS  Article  Google Scholar 

  16. 16.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  17. 17.

    Lienhard, M., Grimm, C., Morkel, M., Herwig, R. & Chavez, L. MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments. Bioinformatics 30, 284–286 (2014).

    CAS  Article  Google Scholar 

  18. 18.

    Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Article  Google Scholar 

  19. 19.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article  Google Scholar 

  20. 20.

    Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    Article  Google Scholar 

  21. 21.

    Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941 (2005).

    CAS  Article  Google Scholar 

  22. 22.

    Inoshita, M. et al. Sex differences of leukocytes DNA methylation adjusted for estimated cellular proportions. Biol. Sex Differ. 6, 11 (2015).

    Article  Google Scholar 

  23. 23.

    Colaprico, A. et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 44, e71 (2016).

    Article  Google Scholar 

  24. 24.

    Triche, T. J., Weisenberger, D. J., Van Den Berg, D., Laird, P. W. & Siegmund, K. D. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 41, e90 (2013).

    CAS  Article  Google Scholar 

  25. 25.

    Aryee, M. J. et al. Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30, 1363–1369 (2014).

    CAS  Article  Google Scholar 

  26. 26.

    Moss, J. et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat. Commun. 9, 5068 (2018).

    Article  Google Scholar 

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This study was conducted with support from Rebecca and Nathan Milikowsky (M.P.), the Claudia Adams Barr Program for Innovative Cancer Research (M.L.F.), a DFCI Medical Oncology grant award (M.L.F. and M.P.) and the H.L. Snyder Medical Research Foundation (M.L.F.). K.K. is funded by the British Colombia Children’s Hospital Research Institute (BCCHRI) Investigator Grant Award Program and the BCCHRI Establishment Award and acknowledges support from Provincial Health Services Authority, the Children’s & Women’s Health Centre of BC and the BC Children’s Hospital Foundation. A.C. is supported by a Canadian Institutes of Health Research (CIHR) Banting Fellowship. T.K.C. is supported in part by the Dana-Farber/Harvard Cancer Center Kidney SPORE and Program, the Kohlberg Chair at Harvard Medical School and the Trust Family, Michael Brigham, and Loker Pinard Funds for Kidney Cancer Research at DFCI. Funding in part by Department of Defense Award W81XWH1910553/Development and Testing of Circulating-Free Methylation DNA as a Prognostic Biomarker for Recurrent Kidney Cancer (PI: T.K.C.).

Author information




P.V.N., J.E.B., S.S., M.P., T.K.C. and M.L.F. conceived and designed experiments. P.V.N., J.E.B. and S.S. performed experiments. P.V.N., J.E.B., S.S., K.K., S.C.B., R.A.I., M.P., D.D.D.C., T.K.C. and M.L.F. analyzed and interpreted data. M.D.M., S.L.C., S.S.W. and G.S. contributed patient samples. A.H.N., S.A.A., Z.B., J.S., G.B., C.R.C., W.P., G.-S.M.L. and M.D.M. collected patient data and samples. P.V.N., J.E.B., S.S., K.K., A.C., S.Y.S., F.B., S.C.B., J.-H.S., R.A.I., M.P., D.D.D.C., T.K.C. and M.L.F. provided guidance and scientific input. P.V.N., J.E.B., S.S., D.D.D.C., T.K.C. and M.L.F. drafted the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Toni K. Choueiri or Matthew L. Freedman.

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Competing interests

A.C., S.Y.S., D.D.D.C., T.K.C. and M.L.F. are listed as inventors on patents filed that are related to this method. Disclosures (all outside the scope of the submitted manuscript): M.D.M.: advisory board: Pfizer, Exelixis and Eisai. S.S.W. has also received consulting fees from Public Health Advocacy Institute, GSK, CVS, Roth Capital Partners, Kantum, Mallinckrodt, Wolters Kluewer, GE Health Care, Allena Pharmaceuticals, Mass Medical International, JNJ, Venbio, Strataka, Takeda, Cerus and Pfizer, all outside the submitted work. G.S.: advisory board: Pfizer, BMS, Genentech, EMD Serono, Novartis, Merck, Sanofi, Seattle Genetics/Astellas, AstraZeneca, Exelixis, Janssen, Amgen, Eisai and Bicycle Therapeutics; research support to institution: Boehringer-Ingelheim, Bayer, Pfizer, Merck, Sanofi and AstraZeneca; travel costs: BMS and AstraZeneca; speaking fees: Physicians Education Resource (PER), Onclive, Research to Practice and Clinical Care Options; writing fees: UpToDate; steering committee of trials: BMS, Bavarian Nordic, Seattle Genetics, QED (all unpaid) and AstraZeneca and Debiopharm (both paid). D.D.D.C.: research funding: Pfizer and Nektar therapeutics. T.K.C.: research (institutional and personal): AstraZeneca, Alexion, Bayer, Bristol Myers-Squibb/ER Squibb and Sons, Cerulean, Eisai, Foundation Medicine, Exelixis, Ipsen, Tracon, Genentech, Roche, Roche Products, F. Hoffmann-La Roche, GlaxoSmithKline, Lilly, Merck, Novartis, Peloton, Pfizer, Prometheus Labs, Corvus, Calithera, Analysis Group, Sanofi/Aventis and Takeda; honoraria: AstraZeneca, Alexion, Sanofi/Aventis, Bayer, Bristol Myers-Squibb/ER Squibb and Sons, Cerulean, Eisai, Foundation Medicine, Exelixis, Genentech, Roche, Roche Products, F. Hoffmann-La Roche, GlaxoSmithKline, Merck, Novartis, Peloton, Pfizer, EMD Serono, Prometheus Labs, Corvus, Ipsen, UpToDate, NCCN, Analysis Group, Michael J. Hennessy (MJH) Associates (a healthcare communications company with several brands such as OnClive, PeerView and PER), Research to Practice, L-path, Kidney Cancer Journal, Clinical Care Options, Platform Q, Navinata Healthcare, Harborside Press, American Society of Medical Oncology, New England Journal of Medicine, Lancet Oncology, Heron Therapeutics and Lilly; consulting or advisory role: AstraZeneca, Alexion, Sanofi/Aventis, Bayer, Bristol Myers-Squibb/ER Squibb and Sons, Cerulean, Eisai, Foundation Medicine, Exelixis, Genentech, Heron Therapeutics, Lilly, Roche, GlaxoSmithKline, Merck, Novartis, Peloton, Pfizer, EMD Serono, Prometheus Labs, Corvus, Ipsen, UpToDate, NCCN, Analysis Group, Pionyr and Tempest; stock ownership: Pionyr and Tempest; no leadership or employment in for-profit companies; other present or past leadership roles: Director of GU Oncology Division at Dana-Farber and past President of Medical Staff at Dana-Farber), member of NCCN Kidney Cancer Panel and the GU Steering Committee, past chairman of the Kidney Cancer Association Medical and Scientific Steering Committee; patents, royalties or other intellectual properties: patents filed related to biomarkers of response to immune checkpoint inhibitors; travel, accommodation, expenses, in relation to consulting, advisory roles, or honoraria medical writing and editorial assistance support may have been funded by communications companies funded by pharmaceutical companies (ClinicalThinking, Envision Pharma Group, Fishawack Group of Companies, Health Interactions, Parexel, Oxford PharmaGenesis and others). The institution (Dana-Farber Cancer Institute) may have received additional independent funding from drug companies and/or royalties potentially involved in research concerning the subject matter; a CV will be provided upon request for scope of clinical practice and research; mentored several non-US citizens on research projects with potential funding (in part) from non-US sources and foreign components.

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Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Flow chart of study samples.

Flow chart outlining the number of cfDNA samples collected, excluded per previously established pre-defined criteria, and included in the analytical cohorts.

Extended Data Fig. 2 Differentially methylated regions in cfDNA between RCC and control samples.

Representative sites of plasma cfDNA methylation gain (left) and loss (right) in RCC relative to controls. Each panel is a 300 base pair window. Each row represents one sample. Row height represents amount of cfDNA methylation at that site.

Extended Data Fig. 3 Correlation of differential methylation in plasma cfDNA and tumor DNA.

Density plot showing the overlap of plasma cfMeDIP-seq DMRs between 69 RCC and 13 control samples with differentially methylated CpGs in tissue from 450 K methylation arrays between 93 peripheral blood mononuclear cell and 324 RCC tumor samples. Each observation (n = 102,852) represents a 450 K methylation array probe (x-axis) that overlaps a region covered by the cfMeDIP-seq assay (y-axis) where the color of each hex bin corresponds to the number of observations in that bin. A simple linear regression line is shown in red. The spearman rank correlation was computed and a 2-sided hypothesis of whether this correlation is equal to zero was carried out.

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Nuzzo, P.V., Berchuck, J.E., Korthauer, K. et al. Detection of renal cell carcinoma using plasma and urine cell-free DNA methylomes. Nat Med 26, 1041–1043 (2020).

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