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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".