Prognostic and predictive value of circulating tumor DNA during neoadjuvant chemotherapy for triple negative breast cancer
Bibliographic record
Abstract
Response to neoadjuvant chemotherapy (NAC) in triple negative breast cancer (TNBC) is highly prognostic and determines whether adjuvant chemotherapy is needed if residual tumor is found at surgery. To evaluate the predictive and prognostic values of circulating tumor DNA (ctDNA) in this setting, we analyzed tumor and serial bloods from 26 TNBC patients collected prior, during, and after NAC. Individual digital droplet PCR assays were developed for 121 variants (average 5/patient) identified from tumor sequencing, enabling ctDNA detection in 96% of patients at baseline. Mutant allele frequency at baseline was associated with clinical characteristics. Levels drastically fell after one cycle of NAC, especially in patients whose tumors would go on to have a pathological complete response (pCR), but then rose significantly before surgery in patients with significant residual tumor at surgery (p = 0.0001). The detection of ctDNA early during treatment and also late at the end of NAC before surgery was strongly predictive of residual tumor at surgery, but its absence was less predictive of pCR, especially when only TP53 variants are considered. ctDNA detection at the end of neoadjuvant chemotherapy indicated significantly worse relapse-free survival (HR = 0.29 (95% CI 0.08-0.98), p = 0.046), and overall survival (HR = 0.27 95% CI 0.075-0.96), p = 0.043). Hence, individualized multi-variant ctDNA testing during and after NAC prior to surgery has prognostic and predictive value in early TNBC patients.
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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 | 0.000 | 0.003 |
| 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".