Association of Recurrence with a Tumor-informed Personalized ctDNA Detection Approach in Resectable Colorectal Cancer
Bibliographic record
Abstract
OBJECTIVE: Primary objective was to evaluate the association between post-surgical MRD detected by a tumor-informed personalized panel (brPROPHET) and CRC recurrence, Secondary objectives were to determine the optimal timepoint for MRD assessment, and compare the performance of different MRD detection methods, including brPROPHET, a tumor-informed fixed panel (TIFP) and a tumor-naïve fixed panel (TNFP). SUMMARY OF BACKGROUND DATA: Circulating tumor DNA (ctDNA)-based molecular residual disease (MRD) has emerged as a pivotal marker in colorectal cancer (CRC), but optimal detection timing and methods remain unclear. METHODS: This study included patients with resectable stage I-IV CRC. Tumor tissues were obtained at surgery, and blood samples were collected preoperatively, on post-surgical days 7 and 30 (D7/D30), and every 3-6 months. MRD was assessed using the above three methods. RESULTS: A total of 214 patients were included in the analysis, with imaging follow-up available for 196 patients (median follow-up: 18.2 months), among whom 24 (12.2%) experienced recurrence. MRD positivity at D7/D30 associated with significantly reduced disease-free survival (DFS). Longitudinal ctDNA-MRD positivity and MTM levels >0.01/mL were also associated with recurrence. Adjuvant chemotherapy was associated with better DFS in patients with positive MRD at D7 (HR=0.26, 95% CI 0.07-0.98, P=0.03) instead of those with negative MRD at D7. Among the 168 patients assessed with all three methods, the brPROPHET assay demonstrated better association of DFS at D7. CONCLUSIONS: ctDNA-based MRD detected by brPROPHET associates with recurrence in CRC. Day 7 is an effective alternative landmark to Day 30 for MRD assessment and brPROPHET outperforms TIFP and TNFP in the association of DFS. ClinicalTrials.gov number: NCT06143644.
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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 | 0.003 |
| 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".