TERT promoter mutations in atypical melanocytic lesions: A series of seven cases with adverse melanoma-specific outcome
Bibliographic record
Abstract
The majority of melanocytic proliferations can be readily categorized as benign or malignant based on histologic assessment under the microscope by a trained dermatopathologist. However, a subset of lesions, termed Atypical Melanocytic Proliferations (AMPs), are histologically ambiguous, leading to possible diagnostic error and suboptimal treatment. Mutations in the promoter region of the catalytic subunit of telomerase, telomerase reverse transcriptase (TERT), are commonly found in melanomas but are rare in melanocytic nevi. In this study, we aimed to determine the prevalence of hot spot TERT promoter (TERT-p) mutations in AMPs with adverse melanoma-specific outcome. Studies were approved by respective institutional review boards. Using a multi-center database, we identified seven cases of melanocytic proliferations with a clinical follow-up period of at least 4 years, which were initially diagnosed as AMPs, and which recurred either as melanoma at site of prior biopsy or as metastatic melanoma. Sequencing of the TERT-p region showed hotspot mutations in three cases (43 %), suggesting that TERT-p mutations are enriched and could aid in the identification of AMPs with adverse outcome. In comparison with existing ancillary techniques for prognostication of AMPs, TERT-p mutation analysis may have advantages in terms of cost effectiveness and turnaround time, and is a promising diagnostic parameter with potential widespread utility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.002 | 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".