Expanding the clinical phenotype of hereditary <i>BAP1</i> cancer predisposition syndrome, reporting three new cases
Bibliographic record
Abstract
The clinical phenotype of BAP1 hereditary cancer predisposition syndrome (MIM 614327) includes uveal melanoma (UM), cutaneous melanoma (CM), renal cell carcinoma (RCC), and mesothelioma. However, the frequency of the syndrome in patients with UM and the association with other cancers are still not clear. In this study, we screened 46 previously untested, unrelated UM patients with high risk for hereditary cancer for germline mutation in BAP1. We also studied four additional patients with a personal or family history suggestive of BAP1 hereditary cancer syndrome. We identified three patients with germline pathogenic mutations (c.2050 C>T, pGln684*; c.1182C>G, p.Tyr394*, and c.1882_1885delTCAC, p. Ser628Profs*8) in BAP1. Two of these three patients presented with UM and the third with a metastatic adenocarcinoma likely from a hepatic cholangiocarcinoma. Reported family histories included UM, mesothelioma, RCC, CM, and several other internal malignancies. The results of this study confirm the association between germline BAP1 mutation and predisposition to UM, mesothelioma, CM and RCC. However, other cancers, such as cholangiocarcinoma and breast carcinoma may be part of the phenotype of this hereditary cancer predisposition syndrome. In addition, the results support the existence of other candidate genes in addition to BAP1 contributing to hereditary predisposition to UM.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".