Clinical, Morphologic, and Molecular Features of Benign and Intermediate-grade Melanocytic Tumors With Activating Mutations in MAP2K1
Bibliographic record
Abstract
Activating mutations in MAP2K1 can be seen in benign and intermediate-grade melanocytic neoplasms with spitzoid morphology. We analyzed the clinical, histopathologic, and genetic features for 16 cases of benign and intermediate-grade melanocytic tumors harboring activating MAP2K1 mutations. We compared them to Spitz neoplasms with characteristic Spitz fusions or HRAS mutation. We also compared the mutational pattern of benign and intermediate-grade MAP2K1 -mutated neoplasms and melanomas with activating MAP2K1 mutations. Among the 16 cases, the favored morphologic diagnosis was Spitz nevus (8/16), atypical Spitz tumors (6/16), and deep penetrating nevus (2/16). The 2 most common architectural patterns seen included a plaque-like silhouette with fibroplasia around the rete reminiscent of a dysplastic nevus (n=7) or a wedge-shaped or nodular pattern with the plexiform arrangement of the nests aggregating around the adnexa or neurovascular bundle (n=8). The cases with dysplastic architecture and spitzoid cytology resembled dysplastic Spitz nevi. Compared with true Spitz neoplasms, MAP2K1 -mutated neoplasms occurred in older age groups and had more frequent pagetosis and a lower average mitotic count. The most common type of mutation in the benign and intermediate-grade cases in the literature involves an in-frame deletion, while, in melanomas, missense mutations are predominant. Benign and intermediate-grade melanocytic neoplasms with activating mutations in MAP2K1 can have morphologic overlap with Spitz neoplasms. A significant proportion of melanomas also have activating MAP2K1 mutations. In-frame deletions are predominantly seen in the benign and intermediate-grade cases, and missense mutations are predominantly seen in melanomas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".