Longitudinal evaluation of circulating tumor DNA in patients undergoing neoadjuvant therapy for early breast cancer using a tumor-informed assay
Bibliographic record
Abstract
Circulating tumor DNA (ctDNA) is an emerging biomarker for the treatment of early breast cancer (EBC). We sought to evaluate a highly sensitive tumor-informed ctDNA assay in a real-world cohort of patients receiving neoadjuvant therapy (NAT) to assess clinical validity and explore prognostic outcomes. ctDNA is detected in 77.2% (88/114) of participants at baseline, with 18/88 (20.5%) having a baseline estimated variant allele frequency (eVAF) of <0.01%. Persistent detection of ctDNA, measured midway through NAT (mid-NAT), is associated with disease recurrence in all participants, reaching statistical significance in those with HER2-negative disease. Stratified analyses demonstrate that ctDNA detected mid-NAT enhances the prognostic accuracy of the residual cancer burden (RCB) score for disease recurrence. Postoperative or follow-up detection of ctDNA demonstrates a 100% positive predictive value for disease recurrence, with a median lead time of 374 days (range: 13-1010 days). These data suggest that assays with high analytical sensitivity may improve baseline ctDNA detection in patients with EBC. The ability to replicate the prognostic association of ctDNA dynamics in a real-world cohort supports further investigation. Prospective trials incorporating ctDNA testing are warranted to assess and develop the clinical utility of 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 | 0.004 |
| 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 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".