Invasive Melanoma Arising in a <scp>BAP1</scp>‐Inactivated Melanocytic Tumor With <i>NRAS</i> Mutation: A Report of Exceptional Case With Emphasis on Its Genomic Features and Review of the Literature
Bibliographic record
Abstract
BAP1-inactivated melanocytic tumor is a distinct entity with loss of BAP1 protein and epithelioid morphology. It shares histopathologic features with Spitz nevus and nevoid melanoma, and it can occur sporadically or with germline BAP1 predisposition syndrome. These lesions typically have tumor-infiltrating lymphocytes and infrequent mitoses. They are generally indolent, though melanoma can arise in both germline and sporadic cases. Most show BRAF V600E and BAP1 mutations. We describe four tumors in one patient diagnosed with BAP1-tumor predisposition syndrome (BAP1-TPDS): two invasive melanomas arising in BIMT and two BIMTs with uncertain malignant potential. Molecular analysis and fluorescence in situ hybridization (FISH) revealed BAP1 and NRAS mutations in melanoma and BAP1-inactivated melanocytic tumor components, with a gain of 6p25 (RREB1) in the melanoma component only. The patient completed pembrolizumab adjuvant therapy with no evidence of metastasis. This is a rare presentation of BIMT with BAP1 and NRAS mutations, absence of BRAF V600 mutation, and loss of BAP1 immunoreactivity in all lesional cells. Our case adds to the understanding of the histomorphologic and mutational spectrum in BAP1-inactivated melanocytic 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".