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. 2025 Apr 14;31(8):1520-1532.
doi: 10.1158/1078-0432.CCR-24-3472.

Ultrasensitive Detection and Monitoring of Circulating Tumor DNA Using Structural Variants in Early-Stage Breast Cancer

Mitchell J Elliott  1 , Karen Howarth  2 , Sasha Main  3 , Jesús Fuentes Antrás  4 , Philippe Echelard  5 , Aaron Dou  1 , Eitan Amir  1 , Michelle B Nadler  1 , Elizabeth Shah  6 , Celeste Yu  6 , Scott Bratman  7 , Taylor Bird  1 , June Roh  1 , Elza C de Bruin  8 , Christopher Rushton  2 , Yilun Chen  2 , Sergii Gladchuk  2 , Anthony M George  2 , Sofia Birkeälv  2 , Miguel Alcaide  2 , Lucia Oton  2 , Girish Putcha  2 , Samuel Woodhouse  2 , Philippe L Bedard  1 , Lillian L Siu  1 , Hal K Berman  9 , David W Cescon  1
Affiliations

Ultrasensitive Detection and Monitoring of Circulating Tumor DNA Using Structural Variants in Early-Stage Breast Cancer

Mitchell J Elliott et al. Clin Cancer Res. .

Abstract

Purpose: The detection of circulating tumor DNA (ctDNA) after curative-intent therapy in early-stage breast cancer is highly prognostic of disease recurrence. Current ctDNA assays, mainly targeting single-nucleotide variants, vary in sensitivity and specificity. Although increasing the number of single-nucleotide variants in tumor-informed assays improves sensitivity, structural variants (SV) may achieve similar or better sensitivity without compromising specificity. SVs occur across all cancers, linked to genomic instability and tumorigenesis, with unique tumor- and patient-specific breakpoints occurring throughout the genome. SVs in breast cancer are underexplored, and their potential for ctDNA detection and monitoring has not been fully evaluated.

Experimental design: We retrospectively analyzed a tumor-informed SV-based ctDNA assay in a cohort of patients with early-stage breast cancer (n = 100, 568 timepoints) receiving neoadjuvant systemic therapy, evaluating ctDNA dynamics and lead times to clinical recurrence in the postoperative period.

Results: ctDNA was detected in 96% (91/95) of participants at baseline with a median variant allele frequency of 0.15% (range: 0.0011%-38.7%); of these, 10% (9/91) had a variant allele frequency <0.01%. ctDNA detection at cycle 2 (C2) of neoadjuvant therapy was associated with a higher likelihood of distant recurrence (log-rank P = 0.047) and enhanced residual cancer burden prognostication (log-rank P = 0.041). ctDNA was detected prior to distant recurrence in all cases (100% sensitivity) with a median lead time of 417 days (range: 4-1,931 days).

Conclusions: These results demonstrate the clinical validity of ultrasensitive ctDNA detection and monitoring using SVs. Prospective trials are required to evaluate ctDNA-guided treatment strategies.

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Conflict of interest statement

K. Howarth reports other support from SAGA Dx during the conduct of the study as well as other support from NeoGenomics outside the submitted work. E. Amir reports personal fees from Pfizer and Novartis, as well as other support from Novartis outside the submitted work. M.B. Nadler reports personal fees from Novartis and Exact Sciences outside the submitted work. S. Bratman reports personal fees from Adela and EMD Serono and grants from AstraZeneca outside the submitted work as well as a patent for ctDNA mutation analysis issued, licensed, and with royalties paid from Roche and a patent for ctDNA methylation analysis issued, licensed, and with royalties paid from Adela. E.C. de Bruin reports being an AstraZeneca employee and holding AstraZeneca shares. C. Rushton reports personal fees from SAGA Dx during the conduct of the study. Y. Chen reports other support from SAGA Dx during the conduct of the study; in addition, Y. Chen has a patent for 63/650,048 pending to SAGA Dx, a patent for 63/650,061 pending to SAGA Dx, a patent for 63/497,872 pending to SAGA Dx, a patent for 63/402,511 pending to SAGA Dx, and a patent for 63/402,512 pending to SAGA Dx. S. Gladchuk reports other support from SAGA Dx during the conduct of the study as well as other support from SAGA Dx outside the submitted work; in addition, S. Gladchuk reports a patent for 63/402,512 pending to SAGA Dx. A.M. George reports other support from SAGA Dx during the conduct of the study; in addition, A.M. George has a patent for 63/497,872 pending, a patent for 18/240,416 pending, a patent for 63/650,048 pending, a patent for 63/650,052 pending, a patent for 63/650,061 pending, a patent for 63/402,511 pending, a patent for 63/402,512 pending, a patent for 63/348,855 pending, and a patent for 63/348,857 pending. S. Birkeälv reports other support from SAGA Dx during the conduct of the study as well as a patent for 18/240435 pending to SAGA Dx. M. Alcaide reports other support from SAGA Dx during the conduct of the study, as well as a patent for 63/497872 pending to SAGA Dx, 18/240416 pending to SAGA Dx, 63/650048 pending to SAGA Dx, 63/650052 pending to SAGA Dx, and 63/650061 pending to SAGA Dx. L. Oton reports other support from SAGA Dx during the conduct of the study and outside the submitted work, as well as a patent for 63/497872 pending to SAGA Dx, 63/650048 pending to SAGA Dx, and 63/650061 pending to SAGA Dx. G. Putcha reports personal fees from SAGA Dx during the conduct of the study and from Natera and Optum Genomics outside the submitted work. S. Woodhouse reports other support from Saga Dx during the conduct of the study, as well as patents 63/650048, 63/650052, and 63/650061 pending to SAGA Dx. P.L. Bedard reports grants from AstraZeneca, Bicara Therapeutics, Bayer, Boehringer Ingelheim, Merck, Novartis, Roche Genentech, LegoChem Biosciences, Medicenna, Zymeworks, Eli Lilly, Gilead, Takeda, GlaxoSmithKline, Bristol Myers Squibb, Amgen, and Pfizer outside the submitted work, as well as being Uncompensated Advisory for Janssen, Zymeworks, Repare Therapeutics, Lilly, Seagen, and Roche Genentech. L.L. Siu reports personal fees from Merck, Pfizer, AstraZeneca, Roche, GlaxoSmithKline, Voronoi, Arvinas, Navire, Relay, Daiichi Sankyo, Amgen, Marengo, Medicenna, Tubulis, LTZ Therapeutics, Pangea, and Break Through Cancer; grants from Novartis, Bristol Myers Squibb, Pfizer, Boehringer Ingelheim, GlaxoSmithKline, Roche Genentech, AstraZeneca, Merck, Celgene, Astellas, Bayer, AbbVie, Amgen, Symphogen, Mirati, BioNTech, 23andMe, and EMD Serono; and personal fees and other support from Agios Pharmaceuticals and Treadwell Therapeutics outside the submitted work. D.W. Cescon reports financial support from AstraZeneca and other support from SAGA Dx during the conduct of the study as well as research support from Grail, Guardant Health, Inivata/NeoGenomics, Knight, and ProteinQure; personal fees and research support from AstraZeneca, GenomeRx, Gilead, GlaxoSmithKline, Merck, Pfizer, and Roche; and personal fees from Daiichi Sankyo, Lilly, Novartis, and SAGA Dx outside the submitted work. No disclosures were reported by the other authors.

Figures

Figure 1.
Figure 1.
SV burden and associated copy number state across solid tumors and within breast cancer subtypes. A, Somatic SV burden across adult solid tumor types, as obtained from the 100,000 Genomes Project. The median SV burden for each cancer type is annotated on the corresponding boxplot. Note that cancer types with <100 samples are excluded from visualization. B, Somatic SV burden within breast cancer clinical receptor subtypes, as derived from the 100,000 Genomes Project.
Figure 2.
Figure 2.
Contrived cfDNA samples prepared for assessment of assay analytical validity. A, dPCR output of the LoD95 study at a dilution of 0.0005% (5 ppm) in BT474 demonstrating 100% ctDNA detection. Illustration of replicate number (1–32) vs. SV number (1–16). Dark green–filled cells indicate positive SV results. B, Example dPCR data (1D plots) for one positive SV result, including positive and negative controls, are shown. A representative threshold value is illustrated.
Figure 3.
Figure 3.
Tumor-specific SV-based assay analysis. A, Histogram of the genomic coordinates of validated SV breakpoints genome wide in the EBC cohort (n = 100) using 100,000 bp bins. B and C, Example Circos plots showing the breakpoint coordinates of all validated SV junctions in which one of the breakpoints falls on chromosome 8 or chromosome 17. D, Representation of VAF at baseline (n = 95) and in all samples (n = 568) tested in the EBC cohort. Comparison of baseline VAF and routine clinical variables: (E) receptor subtype, (F) clinical nodal status, (G) tumor size, (H) clinical stage, and (I) Nottingham grade as assessed on the diagnostic biopsy and (J) in participants with and without recurrence (including local recurrence). K, Fingerprint copy number in those with and without recurrence. Chr, chromosome; CN, copy number; N, nodal status; ND, not detected; T, tumor size.
Figure 4.
Figure 4.
Swimmer plots and clinical events. Participants’ clinical timeline and timeline of plasma collection for ctDNA analysis for (A) ER+, (B) TNBC, and (C) HER2+ EBC. Plots are broken down by participants with and without clinical recurrence and individual stage (I, II, and III) as well as RCB is defined. FUP, follow-up.
Figure 5.
Figure 5.
On-treatment ctDNA clearance, and postoperative ctDNA detection and association with clinical outcomes. A and B, Representative longitudinal plots of ctDNA detection with reference to clinical timelines. C, Association between pre-C2 ctDNA clearance and association with DRFI. D, Association between pre-C2 ctDNA clearance and association with DRFI, stratified by RCB. E, Association between postoperative or follow-up ctDNA detection and association with DRFI. F, Association between postoperative or follow-up ctDNA detection and association with iDFI.
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References

    1. Siravegna G, Marsoni S, Siena S, Bardelli A. Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol 2017;14:531–48. - PubMed
    1. Donaldson J, Park BH. Circulating tumor DNA: measurement and clinical utility. Annu Rev Med 2018;69:223–34. - PubMed
    1. Cescon DW, Bratman SV, Chan SM, Siu LL. Circulating tumor DNA and liquid biopsy in oncology. Nat Cancer 2020;1:276–90. - PubMed
    1. McDonald BR, Contente-Cuomo T, Sammut SJ, Odenheimer-Bergman A, Ernst B, Perdigones N, et al. . Personalized circulating tumor DNA analysis to detect residual disease after neoadjuvant therapy in breast cancer. Sci Transl Med 2019;11:eaax7392. - PMC - PubMed
    1. Coombes RC, Page K, Salari R, Hastings RK, Armstrong A, Ahmed S, et al. . Personalized detection of circulating tumor DNA antedates breast cancer metastatic recurrence. Clin Cancer Res 2019;25:4255–63. - PubMed

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