Molecular KRAS ctDNA Predicts Metastases and Survival in Pancreatic Cancer: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Patients with pancreatic ductal adenocarcinoma (PDAC) commonly have occult metastatic dissemination and current standard staging methods have significant limitations in identifying these patients. A clinically available assay allows for the identification of mutant KRAS (mKRAS) circulating tumor DNA (ctDNA) from patient plasma and peritoneal fluid that may identify these patients and impact treatment decision making. We investigated the patterns of diagnostic and prognostic capabilities of mKRAS ctDNA in patients with localized PDAC. METHODS: Patients with non-metastatic PDAC were identified and underwent a full staging work-up during their first visit at our institution. Development of metastatic disease and long-term survival outcomes were assessed to compare between the mKRAS testing groups. RESULTS: Between 2018 and 2022, 785 patients were evaluated. Among the 785 patients who underwent plasma mKRAS testing, 104 were mKRAS positive. Plasma mKRAS-positive patients were more likely to develop metastatic disease and had worse overall survival. In the 419 patients who underwent peritoneal mKRAS, 123 were mKRAS-positive and were more likely to harbor occult metastases or develop peritoneal rather than hematogenous metastases. For patients who underwent both baseline plasma and peritoneal mKRAS testing, any positive mKRAS test regardless of compartment was associated with worse outcomes. CONCLUSIONS: Detection of mKRAS ctDNA in plasma and peritoneal fluid of patients with localized PDAC is not only feasible but also identifies those at high risk of metastatic progression and worse survival outcomes. It allows for better prognostication and can significantly impact subsequent treatment decisions, particularly in patients where an aggressive surgical approach is being considered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".