Predicting Early Disease Recurrence of Pancreatic Cancer following Surgery: Determining the Role of NUDT15 as a Prognostic Biomarker
Bibliographic record
Abstract
Surgical resection remains the only curative treatment strategy for Pancreatic Ductal Adenocarcinoma (PDAC). A proportion of patients succumb to early disease recurrence post-operatively despite receiving adjuvant chemotherapy. The ability to identify these high-risk individuals at their initial diagnosis, prior to surgery, could potentially alter their treatment algorithm. This unique patient cohort may benefit from neo-adjuvant chemotherapy, even in the context of resectable disease, as this may secure systemic control over their disease burden. It may also improve patient selection for surgery. Using the Cancer Genome Atlas dataset, we first confirmed the poor overall survival associated with early disease recurrence (p < 0.0001). The transcriptomic profiles of these tumours were analysed, and we identified key aberrant signalling pathways involved in early disease relapse; downregulation across several immune signalling pathways was noted. Differentially expressed genes that could serve as biomarkers were identified (BPI, C6orf58, CD177, MCM7 and NUDT15). Receiver operating characteristic curves were constructed in order to identify biomarkers with a high diagnostic ability to identify patients who developed early disease recurrence. NUDT15 expression had the highest discriminatory capability as a biomarker (AUC 80.8%). Its expression was confirmed and validated in an independent cohort of patients with resected PDAC (n = 13). Patients who developed an early recurrence had a statistically higher tumour expression of NUDT15 when compared to patients who did not recur early (p < 0.01). Our results suggest that NUDT15 can be used as a prognostic biomarker that can stratify patients according to their risk of developing early disease recurrence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".