Expression of Tumor Necrosis Factor–Related Apoptosis-Inducing Ligand Receptors 1 and 2 in Melanoma
Bibliographic record
Abstract
PURPOSE: The proapoptotic receptors tumor necrosis factor--related apoptosis-inducing ligand receptor 1 (TRAIL-R1) and TRAIL-R2 are targets of drugs in clinical development, and receptor expression levels may be important determinants of sensitivity to receptor agonists. We assessed TRAIL-R1 and TRAIL-R2 expression patterns in a large cohort of melanomas and benign nevi. EXPERIMENTAL DESIGN: We analyzed tissue microarrays containing 546 melanomas and 540 nevi using our automated quantitative method to measure protein levels in situ (AQUA). The system uses S100 to define pixels as melanoma (tumor mask) within the array spot and measures intensity of TRAIL-receptor expression using Cy5-conjugated antibodies within the mask. AQUA scores were correlated with clinical and pathologic variables. RESULTS: TRAIL-R1 and TRAIL-R2 expression was higher in melanomas than in nevi (P < 0.0001), and higher in primary than in metastatic specimens (P = 0.0031 and P < 0.0001, respectively). TRAIL-R1 and TRAIL-R2 expression exceeding the 95th percentile for nevi was found in 19% and 74% of melanoma specimens, respectively. Although on univariate analysis, high TRAIL-R2 expression correlated with increased survival (P = 0.0439), it was not associated with survival within the primary or metastatic subcohorts. TRAIL-R1 expression was not associated with survival. CONCLUSIONS: TRAIL-R1 and TRAIL-R2 expression is higher in malignant melanocytes than in their benign counterparts, suggesting that these receptors might be effective therapeutic targets in melanoma. Expression is higher in early-stage disease than in metastatic specimens, and expression exceeding that found in nevi is found in a substantially larger fraction of melanomas for TRAIL-R2 compared with TRAIL-R1. Assessment of baseline tumor TRAIL receptor expression may be important in analysis of clinical trials involving TRAIL receptor agonists.
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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) | 0.000 | 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) | 0.001 | 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".