Telomerase reverse transcriptase immunohistochemical expression is sensitive but not specific for <i>TERT</i> gene amplification in acral melanoma
Bibliographic record
Abstract
BACKGROUND: TERT gene amplification (TGA) is a mechanism of telomerase reverse transcriptase (TERT) upregulation frequently utilized by acral melanomas (AMs). Currently, the utility of TERT immunohistochemistry (IHC) to predict TGA status in AMs is poorly documented. METHODS: AMs (26 primary and 3 metastatic) and non-acral cutaneous melanomas (6 primary) were subjected to immunohistochemical analysis using anti-TERT antibody to demonstrate protein expression and fluorescence in situ hybridization (FISH) to assess genomic copy number alteration. The relationship between TERT immunoreactivity and TGA confirmed by FISH was assessed using logistic regression. RESULTS: TERT expression was seen in 50% (13/26) of primary and 100% (3/3) of metastatic AMs and 50% (3/6) of primary non-acral cutaneous melanomas. TGA was found in 15% (4/26) and 67% (2/3) of primary and metastatic AMs and 17% (1/6) of non-acral cutaneous melanomas. The intensity of TERT immunoreactivity correlated with TGA (p = 0.04) and a higher TERT copy number-to-control ratio in AMs, with a correlation coefficient of 0.41 (p = 0.03). The sensitivity and specificity of TERT immunoreactivity for predicting TGA in AMs were 100% and 57%, with corresponding positive and negative predictive values of 38% and 100%, respectively. CONCLUSIONS: The clinical utility of TERT IHC to predict TGA status in AMs appears to be limited given its low specificity and positive predictive value.
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 | 0.001 | 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) | 0.002 | 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".