Role of circulating tumor DNA in early-stage triple-negative breast cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Triple-negative breast cancer (TNBC) accounts for 15% of all breast cancers and carries a worse prognosis relative to other breast cancer subtypes. This systematic review and meta-analysis evaluated the prognostic value of circulating tumor DNA (ctDNA) in early-stage TNBC. METHODS: A literature search was conducted using Ovid Medline, Elsevier EMBASE, Cochrane Central Register of Controlled Trials, and Web of Science Databases for publications up to 11/16/2023. Results were uploaded to Covidence and assessed by two independent reviewers. Studies assessing the use of ctDNA to predict recurrence free survival and related outcomes as well as overall survival were included. All recurrence outcomes were combined during analysis. Statistical analysis was performed using Revman Web. Log-hazard ratios (HR) were pooled for studies reporting recurrence and death as a time-to-event outcomes. Odds ratios (OR) were calculated and pooled for studies reporting patient-level data on recurrence, death, and pathological complete response (pCR). Prospero ID: CRD42023492529. RESULTS: A total of 3,526 publications were identified through our literature search, and 20 publications (n = 1202 patients) were included in the meta-analysis. In studies that reported recurrence as a time-to-event outcome, post-neoadjuvant (before or after surgery) ctDNA + status was associated with a higher likelihood of disease recurrence (HR 4.12, 95% confidence interval [CI] 2.81-6.04). For studies that reported patient-level data, post-neoadjuvant ctDNA + status was associated with higher odds of disease recurrence (OR 6.72, 95% CI 3.61-12.54). Pooled log-HR also revealed that ctDNA + status in the post-neoadjuvant setting (before or after surgery) was associated with worse overall survival (HR 3.26, 95% CI 1.88-5.63). CONCLUSIONS: Our findings suggest that ctDNA could be used as a prognostic biomarker to anticipate the risk of relapse. However, it remains unclear if therapeutic intervention for patients who are ctDNA + can improve outcomes. While more studies are needed before incorporating ctDNA into clinical practice, the findings of this meta-analysis are reassuring and show the promise of ctDNA as a biomarker.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".