GENETIC CHARACTERIZATION OF A FAMILIAL PATHOGENIC VARIANT OF MAX GENE ASSOCIATED WITH PHEOCHROMOCYTOMA, PARAGANGLIOMA AND DUODENAL/ PANCREATIC NEUROENDOCRINE TUMOURS
Bibliographic record
Abstract
OBJECTIVES A 30-year-old male was diagnosed with pheochromocytoma based on hypertension, imaging and biochemistry. He underwent a 14-susceptibility gene panel for PPGLs (Invitae, USA) that revealed a germline pathogenic nonsense heterozygous MYC- associated factor X (MAX) variant (c.223c>t p.arg75*). The patient’s father (61 years old) was found to carry the same germline mutation which led to the diagnosis of a functional abdominal paraganglioma and two neuroendocrine tumors (NETs) in his pancreas and his duodenum that were operated on. Our objective was to determine the causal role of the familial germline MAX pathogenic variant in the development of PPGLs and NETs. METHODOLOGY Leucocyte DNA was extracted from blood cells and tumoral DNA was extracted from FFPE tissues. All 5 exons of the MAX gene (nm_002382.4) were studied by Sanger sequencing. The amplicons were directly sequenced using the applied biosystems 3730xl DNA analyzer (Mcgill University, Genome Quebec, Qc, Canada). RESULTS The germline MAX mutation (c.223c>t p.arg75*) was confirmed in both leucocyte DNA and all tumoral DNA (son: PHEO, father: PGL and NETs). The wild-type max allele was lost in the tumoral DNA of the father’s PGL. A second somatic pathogenic variant of MAX c.263t>c (p.leu88pro) was identified in the duodenal NET and was predicted to be likely deleterious (CADD score 31). CONCLUSION We report the rare association of a familial germline MAX mutation presenting with PPGLs and NETs. A second hit in the MAX gene in the duodenal NET supports the hypothesis that MAX mutations might be associated with multiple endocrine tumors.
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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".