Ultrasensitive Detection and Monitoring of Circulating Tumor DNA Using Structural Variants in Early-Stage Breast Cancer
Bibliographic record
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".