Germline Mutations in DNA Repair Genes in Patients with Pancreatic Neuroendocrine Neoplasms: Diagnostic and Therapeutic Implications
Bibliographic record
Abstract
Pancreatic neuroendocrine neoplasms (pNENs) are the second most common type of pancreatic cancer after pancreatic ductal adenocarcinoma. Germline mutations in DNA repair genes drive several hereditary and sporadic cancers; however, their role in pNENs remains poorly defined. This pilot study aimed to assess the frequency and clinical relevance of germline DNA repair gene mutations in patients with pNENs, both with and without a family history of cancer. Germline DNA from 57 Polish patients with pNENs was analyzed using targeted next-generation sequencing to identify variants in a panel of DNA repair genes. Variant classification followed the American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines. Germline mutations were identified in 14 patients (24.6%), both with and without a family history of malignancy. Two patients carried pathogenic variants in BRCA2 and CHEK2, while seven carried variants of uncertain significance (VUS). The identified variants have been implicated in various cancer types, including breast, ovarian, prostate, gastric, colorectal, and pancreatic cancers. These findings indicate that germline mutations in DNA repair genes may contribute to the pathogenesis of pNENs, even in patients without a family history. Broader germline testing and population-specific studies are needed to clarify the genetic landscape and clinical implications of these alterations.
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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 | 0.000 | 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".