Circulating tumor DNA quantity is related to tumor volume and both predict survival in metastatic pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Circulating tumor DNA (ctDNA) is assumed to reflect tumor burden and has been suggested as a tool for prognostication and follow-up in patients with metastatic pancreatic ductal adenocarcinoma (mPDAC). However, the prognostic value of ctDNA and its relation with tumor burden has yet to be substantiated, especially in mPDAC. In this retrospective analysis of prospectively collected samples, cell-free DNA from plasma samples of 58 treatment-naive mPDAC patients was isolated and sequenced using a custom-made pancreatobiliary NGS panel. Pathogenic mutations were detected in 26/58 (44.8%) samples. Cross-check with droplet digital PCR showed good agreement in Bland-Altman analysis (p = 0.217, nonsignificance indicating good agreement). In patients with liver metastases, ctDNA was more frequently detected (24/37, p < 0.001). Tumor volume (3D reconstructions from imaging) and ctDNA variant allele frequency (VAF) were correlated (Spearman's ρ = 0.544, p < 0.001). Median overall survival (OS) was 3.2 (95% confidence interval [CI] 1.6-4.9) versus 8.4 (95% CI 1.6-15.1) months in patients with detectable versus undetectable ctDNA (p = 0.005). Both ctDNA VAF and tumor volume independently predicted OS after adjustment for carbohydrate antigen 19.9 and treatment regimen (hazard ratio [HR] 1.05, 95% CI 1.01-1.09, p = 0.005; HR 1.00, 95% CI 1.01-1.05, p = 0.003). In conclusion, our study showed that ctDNA detection rates are higher in patients with larger tumor volume and liver metastases. Nevertheless, measurements may diverge and, thus, can provide complementary information. Both ctDNA VAF and tumor volume were strong predictors of OS.
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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 | 0.001 | 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) | 0.001 | 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".