Clinical utility of circulating tumor DNA (ctDNA) in resectable pancreatic ductal adenocarcinoma (PDAC).
Bibliographic record
Abstract
247 Background: ctDNA is emerging as a promising biomarker, with potential utility in screening, detecting minimal residual disease after curative resection and monitoring treatment response or resistance in advanced disease. Most PDAC studies to date have focused on identifying mutant KRAS ctDNA in metastatic disease. Here we perform sequential ctDNA quantification in patients (pts) with resectable PDAC using a novel and highly sensitive multiplex technology to explore the clinical utility of ctDNA as a diagnostic and prognostic biomarker. Methods: Banked plasma and tumor samples from 18 pts with resected PDAC were retrieved. Plasma samples were collected 0-28 days before, and 28-70 days after surgery. DNA was extracted using standard protocols and analyzed using the OnTarget system, which enriches for DNA molecules containing hot spot mutations prior to sequencing. A 96-plex panel that includes the most prevalent mutations in KRAS, PIK3CA and TP53 was used. Results: 16 pts (89%) had at least 1 mutation detected by OnTarget in the tumor sample, most frequently in KRAS codon 12 (n = 14). ctDNA was detected in the pre-operative blood sample in 7/16 pts with tumor mutations (sensitivity 44%) and 0/2 pts without detectable tumor mutations (specificity 100%). Of the 10 pts with available post-operative blood samples, 1 did not have a tumor mutation. 4 pts had detectable ctDNA, 3 of whom have recurred. In contrast, 0 of the 5 pts without detectable post-operative ctDNA have recurred. At median follow-up of 37 weeks, recurrence-free survival (RFS) was significantly longer in pts without detectable ctDNA after surgery (median not reached vs 9 weeks, p = 0.022). Of 11 plasma samples with detectable ctDNA, 3 harbored mutations that were not detected in the primary tumor, including 2 non-KRAS mutations (GNAS R201H and PIK3CA E542K). Conclusions: Pre-operative ctDNA has low sensitivity, suggesting limited utility in PDAC screening. RFS was significantly longer in pts without detectable post-operative ctDNA; however this analysis is limited by small numbers and short follow-up. Discordance in hot spot mutations detected in tumor and matched plasma was observed in 27% of samples, possibly related to intratumoral heterogeneity.
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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 | 0.001 |
| 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 | 0.001 | 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".