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Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning

An Author Correction to this article was published on 30 September 2021

This article has been updated

Abstract

The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of tumour-derived signals at dilution factors as low as 1 in 10,000. For a total of 308 patients with surgery-resectable lung cancer and 261 age- and sex-matched non-cancer control individuals recruited from two hospitals, the assay detected 52–81% of the patients at disease stages IA to III with a specificity of 96% (95% confidence interval (CI) 93–98%). In a subgroup of 115 individuals, the assay identified, at 100% specificity (95% CI 91–100%), nearly twice as many patients with cancer as those identified by ultradeep mutation sequencing analysis. The low amounts of ctDNA permitted by machine-learning-aided deep methylation sequencing could provide advantages in cancer screening and the assessment of treatment efficacy.

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Fig. 1: Overview of ELSA-seq for deep methylation sequencing of low-ctDNA samples.
Fig. 2: WGBS library construction by ELSA-seq.
Fig. 3: Methylation block definition and pattern recognition.
Fig. 4: Analytical validation of ELSA-seq.
Fig. 5: Classification of plasma samples in two independent cohorts.
Fig. 6: Proof-of-concept study and marker selection for LC.
Fig. 7: A parallel comparison of ELSA-seq, HS-UMI and ddPCR.

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The microarray data used for identification of differentially methylated sites can be downloaded from the TCGA database at https://gdac.broadinstitute.org/runs/analyses__2016_01_28/data and from the GEO database under the accession code GSE40279. Illumina EPIC TruSeq Methyl data are available at https://basespace.illumina.com/projects/31997005. The raw sequencing data (.fastq files) generated are available from the NCBI Sequence Read Archive (SRA) repository, under the accession code PRJNA534206. The analysed datasets generated during the study are too large to be publicly shared, but they are available for research purposes from the corresponding authors on reasonable request. Any data and materials that can be shared will be released subject to a data-transfer agreement.

Code availability

Codes and scripts developed for this study are available at http://github.com/bnr-ed/mworkflow.

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Acknowledgements

We thank K. Kemphues (Cornell University) for critical review of the manuscript. We thank Z. Liang, H. Wu, Z. Jin, F. Tan, S. Chuai, W. Deng, X. Mao, Y. Ma, L. Yang, J. Ye and F. Duan for their assistance with this study. This work was supported, in part, by Beijing Natural Science Foundation (grant number 7182132), the Major Projects of the Beijing Municipal Science and Technology Commission (grant number Z171100002017013), the Capital Special Project for Featured Clinical Application (grant number Z151100004015157), the Peking Union Medical College Hospital Youth Fund (grant numbers PUMCH-2016-2.25, HI626500), and the Peking Union Medical College Special Youth Teacher Project (grant numbers 2014zlgc0717; 2014zlgc0135). We acknowledge research funding, not attached to this study or to any research project or collaboration, from Burning Rock Biotech to the University of California at Berkeley.

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Authors

Contributions

N.L. designed, supervised the clinical study, and provided funding. B.L. designed, supervised the technical study, and wrote the paper. Z.J., P.W., Y. Wang, Y. Wu, Z.C., L.C., Z.B., Hongsheng Liu, L.L., C.H., Y.Q. and Y.C. conducted the clinical study including participant recruitment, sample preparation, clinical information collection and interpretation. C. Wang, T.Z., F.Q., J.S., J. Xu, F.X., H.C., S.F., X.Y, H.H.-Z., J. Xiang and Hao Liu performed the technical development including experiment conduction, computational framework construction and data analysis. C. Wu and X.G. optimized the machine-learning algorithm. H.Z. and S.L designed the clinical study and provided funding. Z.Z. conceived the idea and oversaw the overall direction. All authors discussed the results and contributed to the final manuscript.

Corresponding authors

Correspondence to Shanqing Li, Heng Zhao or Zhihong Zhang.

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Competing interests

T.Z., B.L. and Z.Z. are inventors on a pending patent application held by Burning Rock Biotech related to target deep methylation sequencing (WO2019192489A1, filed in the United States, Canada, Europe, Japan, Sigapore, Australia and Brazil). C. Wang., B.L. and Z.Z. are on a patent application to be submitted by Burning Rock Biotech that covers other aspects of ELSA-seq described in this article.

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Supplementary information

Supplementary Information

Supplementary Methods and Figs. 1–10.

Reporting Summary

Supplementary Tables 1–10

Oligonucleotide sequences used in ELSA-seq, methylation-pattern counts, hotspot mutations, clinical metadata and other datasets.

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Liang, N., Li, B., Jia, Z. et al. Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning. Nat Biomed Eng 5, 586–599 (2021). https://doi.org/10.1038/s41551-021-00746-5

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