Abstract
Differentiating sequencing errors from true variants is a central genomics challenge, calling for error suppression strategies that balance costs and sensitivity. For example, circulating cell-free DNA (ccfDNA) sequencing for cancer monitoring is limited by sparsity of circulating tumor DNA, abundance of genomic material in samples and preanalytical error rates. Whole-genome sequencing (WGS) can overcome the low abundance of ccfDNA by integrating signals across the mutation landscape, but higher costs limit its wide adoption. Here, we applied deep (~120×) lower-cost WGS (Ultima Genomics) for tumor-informed circulating tumor DNA detection within the part-per-million range. We further leveraged lower-cost sequencing by developing duplex error-corrected WGS of ccfDNA, achieving 7.7 × 10−7 error rates, allowing us to assess disease burden in individuals with melanoma and urothelial cancer without matched tumor sequencing. This error-corrected WGS approach will have broad applicability across genomics, allowing for accurate calling of low-abundance variants at efficient cost and enabling deeper mapping of somatic mosaicism as an emerging central aspect of aging and disease.
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Data availability
The raw genomic sequencing data generated are available from the European Genome–Phenome Archive under dataset accession code EGAD50000001234. Datasets obtained from the PCAWGC (Supplementary Table 11) are available at https://www.icgc-argo.org/. Urothelial cancer tumor/normal alignment files were obtained from Nguyen et al.51 and were deposited to dbGap under accession number phs001087.v4.p1.
Code availability
Code and custom scripts are available at https://github.com/alexpcheng/WGSDuplex.
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Acknowledgements
We thank the participants and their families for contributing plasma and tissue for this study. We also thank H. R. He at Weill Cornell, J. Park and all members of the laboratory of D.A.L., the New York Genome Center computational biology team, especially M. Shah, and the New York Genome Center research sequencing laboratory for thoughtful discussions throughout this work. This work was supported by the Mark Foundation Emerging Leader Award, the Vallee Scholar Award, the Burroughs Wellcome Fund Career Award for Medical Scientists, a National Cancer Institute R01 grant (R01-CA266619-01) and the Melanoma Research Alliance Established Investigator Award (D.A.L.). A.P.C. received support from the American Cancer Society Postdoctoral Fellowship program. Memorial Sloan Kettering Cancer Center investigators are supported by Cancer Center Support Grant P30 CA08748 from the National Institutes of Health/National Cancer Institute. A.J.W. received support from the Conquer Cancer Foundation Young Investigator Award, the Melanoma Research Alliance Young Investigator Award and the NCI K08 Mentored Career Scientist Award (K08 CA263301-03). D.A.L. is a Scholar of the Leukemia and Lymphoma Society. This work was made possible by the MacMillan Family Foundation and the MacMillan Center for the Study of the Non-Coding Cancer Genome at the New York Genome Center. The opinions, results and conclusions reported in this paper are those of the authors and are independent from these funding sources.
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D.A.L., A.P.C., A.J.W., G.B. and B.M.F. conceived and designed the project. D.A.L., G.B. and B.M.F. served as lead principal investigators. A.J.W., A.I.A., M.S.M., A. Saxena, M.K.C., D.T.F., L.S., M.S., J.M., A.K., S.T., D.W., J.O., O.E., J.M.M., N.K.A., J.D.W., M.A.P. G.B. and B.M.F. performed participant selection, curated participant data and prepared samples for sequencing. G.I. provided mouse PDX samples. M.M., D.M., A.H., R.F., J.M., Z.S. and L.W. performed library preparation and sequencing. A.P.C., A.A., I.R., A. Sossin, S.R., N.M., W.F.H., R.M.M., D.H., T.L., H.C., S.G., M.C.Z., N.R., Y.W., A.J. and D.L. performed data analysis. A.P.C., C.P. and D.A.L. wrote the manuscript with comments and contributions from all authors.
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A.P.C. and D.A.L. have filed a provisional patent regarding certain aspects of this manuscript. D.A.L. and A.J.W. have also filed two additional patent applications regarding work presented in this manuscript. A.P.C. is listed as an inventor on submitted patents pertaining to cell-free DNA (US patent applications 63/237,367, 63/056,249, 63/015,095 and 16/500,929) and receives consulting fees from Eurofins Viracor and has received conference travel support from Ultima Genomics. I.R. and A.J. are employees and shareholders of Ultima Genomics. D.L. is a shareholder of Ultima Genomics. G.I. has received consulting fees from Daiichi Sankyo. J.D.W. is a consultant for Apricity, Ascentage Pharma, Bicara Therapeutics, Bristol Myers Squibb, Daiichi Sankyo, Dragonfly, Imvaq, Larkspur, Psioxus, Takeda, Tizona, Trishula Therapeutics, Immunocore – Data Safety board and Scancell; reports grant and research support from Bristol Myers Squibb and Enterome; has equity in Apricity, Arsenal IO/Cell Carta, Ascentage, Imvaq, Linneaus, Georgiamune, Takeda, Tizona Pharmaceuticals and Xenimmune; and is an inventor on the following patents: Xenogeneic DNA Vaccines; Newcastle Disease viruses for Cancer Therapy; Myeloid-derived suppressor cell (MDSC) assay; Prediction of Responsiveness to Treatment with Immunomodulatory Therapeutics and Method of Monitoring Abscopal Effects during such Treatment; Anti-PD1 Antibody; Anti-CTLA4 antibodies; Anti-GITR antibodies and methods of use thereof; CD40 binding molecules and uses thereof. A. Saxena receives research funding from AstraZeneca, has served on Advisory Boards for G1 Therapeutics, Boehringer Ingelheim, Novocure, InxMed, Bristol Myers Squibb and Galvanize Therapeutics, and as a consultant for Galvanize Therapeutics. M.A.P. has received consulting fees from Bristol Myers Squibb, Merck, Novartis, Eisai, Pfizer, Lyvgen and Chugai and has received institutional support from RGenix, Merck Infinity, Bristol Myers Squibb, Merck and Novartis. M.K.C. has received consulting fees from Bristol Myers Squibb, Merck, InCyte, Moderna, ImmunoCore and AstraZeneca and receives institutional support from Bristol Myers Squibb. S.T. is funded by Cancer Research UK (grant reference number A29911); the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC10988), the UK Medical Research Council (FC10988) and the Wellcome Trust (FC10988); the National Institute for Health Research Biomedical Research Centre at the Royal Marsden Hospital and Institute of Cancer Research (grant reference number A109), the Royal Marsden Cancer Charity, The Rosetrees Trust (grant reference number A2204), Ventana Medical Systems (grant reference numbers 10467 and 10530), the National Institute of Health (U01 CA247439) and Melanoma Research Alliance (686061). S.T. has received speaking fees from Roche, AstraZeneca, Novartis and Ipsen. S.T. has the following patents filed: Indel mutations as a therapeutic target and predictive biomarker PCTGB2018/051892 and PCTGB2018/051893. G.B. has sponsored research agreements through her institution with Olink Proteomics, Teiko Bio, InterVenn Biosciences and Palleon Pharmaceuticals; served on advisory boards for Iovance, Merck, Nektar Therapeutics, Novartis and Ankyra Therapeutics; consulted for Merck, InterVenn Biosciences and Ankyra Therapeutics and holds equity in Ankyra Therapeutics. B.M.F. is on the advisory boards for Astrin Bioscience, Natera, Guardant, Janssen, Gilead, Merck, Immunomedics and QED Therapeutics, is a consultant for QED Therapeutics, Astra Biosciences and BostonGene and obtains patent royalties from Immunomedics and Gilead, honoraria from Urotoday and Axiom Healthcare Strategies and research support from Eli Lilly. B.M.F. reports support from the NIH, DoD-CDMRP, Starr Cancer Consortium and the P-1000 Consortium. D.A.L. is on the Scientific Advisory Board of Mission Bio, Pangea, Alethiomics and Veracyte, and has received prior research funding support from Illumina, Ultima Genomics, Celgene, 10x Genomics and Oxford Nanopore Technologies. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Ultima and Illumina sequencing datasets of human-mapped reads in mouse PDX datasets (n = 3).
A Homopolymer size estimation of bases between two PCR duplicates (all samples combined) in Ultima datasets. B Homopolymer size estimation of bases between a read and the aligned reference (all samples combined) in Ultima datasets. C Homopolymer size estimation of bases between two PCR duplicates (all samples combined) in Illumina datasets. D Homopolymer size estimation of bases between a read and the aligned reference (all samples combined) in Illumina datasets. E Indel calling accuracy by PCR duplicate family sizes in Ultima datasets (n = 3 in each boxplot). F Indel calling accuracy of Illumina sequencing reads (for single family reads, n = 3 in each boxplot). G Frequency of homopolymer sizes across the human genome. For boxplots in (E) and (F), the lower and upper ends of boxes represent the 25th and 75th percentiles of the data, respectively, and the horizontal lines represent the median. The whiskers represent at most 1.5 times the IQR. Accuracy in (E) and (F) is defined as the number of correct homopolymer assignments in individual sequencing reads divided by the occurrences of that homopolymer size in the human genome in all sequenced reads.
Extended Data Fig. 2 Flow-based sequencing provides predictable error-robust motifs.
A Single-nucleotide variant analysis of matched Ultima and Illumina sequencing datasets across 96 trinucleotide contexts. Cycle shift motifs (described in B) are indicated by plus signs. B Left: Example sequencing of a TGC trinucleotide in flowspace. Given a flow order of T > G > C > A, one full flow cycle of each nucleotide should provide a 1 > 1 > 1 > 0 signal. Top, right: Example of how a T[G > A]C alt disrupts the cycles in flow space basecalling. Two sequencing cycles are required to fully resolve a TAC sequencing motif. We refer to these types of motifs as cycle shift motifs. Bottom, right: Example of how a T[G > C]C variant does not affect the cycles of flow space basecalling. C Error rates in Ultima and Illumina sequencing datasets for trinucleotide variants that alter the flowspace sequencing cycle (n = 120 in the cycle shift motif boxplots (blue), corresponding to the 40 trinucleotide variants that are classified as cycle shift motifs across 3 mouse PDX plasma samples. n = 168 in the non cycle shift motif boxplots (red), corresponding to the 52 trinucleotide variants that are not classified as cycle shift motifs across 3 mouse PDX plasma samples). P-values were measured using a two-sided Wilcoxon test. Error bars in (A) represent the standard error of the mean. For boxplots in (C), the lower and upper ends of boxes represent the 25th and 75th percentiles of the data, respectively, and the horizontal lines represent the median. The whiskers represent at most 1.5 times the IQR.
Extended Data Fig. 3 Tradeoffs between deep-targeted sequencing and modest whole-genome sequencing for ctDNA detection.
A Mutational burden (number of SNVs) of 22 cancer types retrieved from the Pan Cancer Analysis of Whole Genomes consortium. The numbers along the x-axis represent the number of tumors analyzed per cancer type. B Median ctDNA detection opportunities using a whole-genome approach with 10x sequencing coverage, a 10-target panel at 10,000x coverage and a 1-target panel at 10,000x coverage. The pink shaded area represents tumor types for which targeting only a few sites may offer benefit over whole-genome sequencing. The blue shaded area represents tumor types for which a whole-genome approach will offer more opportunities to detect ctDNA over targeted panels. The lower and upper ends of the boxplots in (A) represent the 25th and 75th percentiles of the data, respectively, and the horizontal lines represent the median. The whiskers represent at most 1.5 times the IQR.
Extended Data Fig. 4 Circulating tumor DNA cost and coverage analysis between Illumina and Ultima sequencing in a matched sample.
Areas under the curve (AUCs) are measured by calculating the area under a receiver operating characteristic curve comparing a given group (for example, Illumina 20x at 10−6 expected tumor fraction) to its platform and coverage-matched cancer-free control (for example, Illumina 20x, expected tumor fraction of 0). All AUCs at expected tumor fractions of 10−4 and greater were 1.00. Z-scores of a given sample are calculated against their coverage and platform matched cancer-free control (expected tumor fraction of 0).
Extended Data Fig. 5 Variant allele frequencies for variants across denoising approaches.
Variant allele frequencies (calculated using unfiltered sequencing reads) in positions where a variant was found using UMI-agnostic denoised reads, Single strand corrected reads and in duplex corrected reads. Allele frequencies of 0.2 and below are colored in red.
Extended Data Fig. 6 Comparison of detected UV-derived mutations using duplex, single-strand and UMI-agnostic denoising methods.
A Cosine similarities by cancer stage at baseline timepoints (pre-treatment or pre-surgery) for UV and CH-associated signatures. B Comparison of duplex, single-strand and UMI-agnostic denoising methods to detect melanoma-associated variants using a single-read variant calling pipeline for pre-treatment plasma samples from melanoma patients (top) and cancer-free controls (bottom). P-values were measured using a two-sided Wilcoxon test. For all boxplots, the lower and upper ends of boxes represent the 25th and 75th percentiles of the data, respectively, and the horizontal lines represent the median. The whiskers represent at most 1.5 times the IQR.
Extended Data Fig. 7 Tumor-agnostic copy-number based tumor fraction estimation in stage III and IV melanoma and cancer-free control samples.
Samples include cancer-free controls (n = 10); stage III melanoma (pre-surgery; n = 10) and stage IV melanoma (pre-treatment; n = 4). Dotted line at 0.03 represents the limit of detection of ichorCNA. For boxplots, the lower and upper ends of boxes represent the 25th and 75th percentiles of the data, respectively, and the horizontal lines represent the median.
Extended Data Fig. 8 ctDNA dynamics throughout treatment in melanoma patients.
A Changes in circulating tumor DNA (increase or decrease) relative to the earliest sampled timepoint. Solid lines represent patients with recurrence or progressive disease, and dashed lines represent patients with either partial response or who are recurrence-free following treatment. Closed and open circles represent samples with and without detected ctDNA, respectively. B Difference in ctDNA relative to the pre-treatment timepoint stratified by clinical outcome. One sample did not have a pre-treatment timepoint available (MEL-15; progressive disease) and so a day 9 post-treatment time point was used as baseline. For boxplots in (B), the lower and upper ends of boxes represent the 25th and 75th percentiles of the data, respectively, and the horizontal lines represent the median. The whiskers represent at most 1.5 times the IQR. P-values were calculated using a two-sided Wilcoxon test.
Extended Data Fig. 9 Major signature contributions from urothelial cancer patients’ tumors measured through whole-genome sequencing.
Top: total mutation counts per sequenced tumor. Bottom: signature contributions. Trinucleotide frequencies were fit to the entire COSMIC database (version v.3.3). When a patient had two or more tumors (B01, B04, B15, B16, B17, B18, B19), we measured signature contributions of mutations that were present in two or more tumors and thereby likely reflect mutations that arise earlier in tumor evolution.
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Cheng, A.P., Widman, A.J., Arora, A. et al. Error-corrected flow-based sequencing at whole-genome scale and its application to circulating cell-free DNA profiling. Nat Methods 22, 973–981 (2025). https://doi.org/10.1038/s41592-025-02648-9
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DOI: https://doi.org/10.1038/s41592-025-02648-9
