Clinical Validation of Digital PCR-based ctDNA detection for risk stratification in residual triple negative breast cancer: TRICIA trial results
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) patients who have residual tumor at surgery (non-pathological complete response or non-pCR) after neoadjuvant chemotherapy (NAC) have a poor prognosis. In these cases, adjuvant chemotherapy with capecitabine improves disease-free survival in ∼15% of patients. Identifying those who would benefit or not from such additional therapy remains a critical need. Circulating tumor DNA (ctDNA), a plasma-based biomarker, provides real-time insights into disease and treatment progression. We previously demonstrated that ctDNA detection after NAC and before surgery signals poor prognosis. In the TRICIA trial, 92 patients with non-pCR provided plasma before surgery and after NAC (T1), after surgery (T2), during adjuvant capecitabine therapy (T3) and late after surgery following completion of adjuvant treatment (T4). The sensitivity, specificity, and predictive values of a tumor-informed digital-droplet-based ctDNA detection assay were measured with a median follow-up of 38 months. ctDNA was detected in 97% of patients before clinical relapse. We confirmed that the lack of detection of ctDNA at the post-NAC pre-operative (T1) time point is highly prognostic, with 95% distant-disease relapse free survival. The other time points were not as strongly prognostic. The detection of ctDNA in patients with significant residual tumor (Residual Cancer Burden 2 or 3) was also highly prognostic and our test performed with 100% sensitivity and 100% specificity in RCB 3 patients. Although the extent of residual disease was correlated with the Fractional Abundance of individual variants, the effect of surgery on ctDNA detectability was not significant except for RCB 3 cases, in which large amounts of residual disease were removed. We measured 3 time points before, during and after capecitabine treatment and found that capecitabine treatment was associated with clearance of ctDNA in 41% of cases, and clearance (from detection to non-detection) was associated with good prognosis. These findings suggest that ctDNA testing using ddPCR assays in an academic hospital-based context can reliably identify a very low-risk group of non-pCR TNBC patients, and this personalized approach is ready for prospective testing for clinical utility in TNBC patients who have undergone NAC and require additional chemotherapy.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".