Automated Quantitative Analysis of HDM2 Expression in Malignant Melanoma Shows Association with Early-Stage Disease and Improved Outcome
Bibliographic record
Abstract
The incidence of cutaneous malignant melanoma continues to increase every year, and this disease remains the leading cause of skin cancer death in industrialized countries. Despite the aggressive nature of advanced melanoma, there are no standard biological assays in clinical usage that can predict metastasis. This may be due, in part, to the inadequacy of reproducible assessment of protein expression using traditional immunohistochemistry. We have previously described a novel method of quantitative assessment of protein expression (AQUA) with the continuity and accuracy of an ELISA assay but with maintenance of critical spatial information. Here, we modify this technology for the evaluation of protein expression in melanoma. Using a tissue microarray cohort of 405 melanoma lesions and 17 normal skin samples, we analyzed expression of HDM2, the human homologue of murine double minute 2 with automated quantitative analysis. We show that expression levels in the nucleus are significantly higher in primary melanomas than in metastatic lesions. Furthermore, high levels of expression are predictive of better outcome. This study demonstrates that quantitative assessment of protein expression is useful in melanoma to validate potential tissue biomarkers and suggests that human homologue of murine double minute 2 may be a valuable prognostic tool for management of malignant melanoma.
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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".