Case Report: Novel likely pathogenic MEN1 mosaic mutation in the family with MEN-1 syndrome
Bibliographic record
Abstract
Multiple endocrine neoplasia type 1 (MEN-1; OMIM 131100) is a rare, autosomal dominant syndrome caused by heterozygous inactivating mutations in the MEN1 tumor suppressor gene (11q13; OMIM 613733). MEN-1 is characterized by polyglandular pathology, which typically involves the parathyroid glands (90%), pancreas (30-80%) and anterior pituitary (15-50%). To date, over 1,600 pathogenic MEN1 variants have been documented, including nonsense, frameshift, and splice-site mutations, as well as rare large deletions. While germline mutation detection rates reach 70-90% in clinically diagnosed probands, approximately 10-30% of phenotypically confirmed MEN-1 families test negative by conventional sequencing, suggesting possible regulatory region defects, deep intronic mutations, or mosaic variants. In cases where MEN1 germline testing is negative despite a clinical MEN-1 phenotype, somatic mosaicism should be considered. We investigated a familial cohort presenting with primary hyperparathyroidism, multifocal pancreatic and pituitary neuroendocrine neoplasms – a triad strongly suggestive of MEN-1. Using a multi-tissue sequencing approach, we analyzed DNA extracted from peripheral blood leukocytes and parathyroid adenomas tissue via both Sanger sequencing and next-generation sequencing (NGS) with high coverage. While conventional Sanger analysis failed to detect a mutation, targeted NGS revealed a novel, likely pathogenic MEN1 variant present at low allele frequency (5-15%), consistent with postzygotic mosaicism. The variant was classified as pathogenic per ACMG/AMP guidelines and correlated with disease manifestations in affected tissues. These findings demonstrate that high-coverage NGS of multiple tissues is critical for identifying low-level mosaic MEN1 mutations missed by standard testing. Alternative screening methods are required for patients with strong clinical indications of MEN-1 and/or a family history, but negative germline test results, one such method is NGS with high coverage.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".