Data from Validation of Melanoma Immune Profile (MIP), a Prognostic Immune Gene Prediction Score for Stage II–III Melanoma
Bibliographic record
Abstract
AbstractPurpose: Biomarkers are needed to stratify patients with stage II–III melanoma for clinical trials of adjuvant therapy because, while immunotherapy is protective, it also confers the risk of severe toxicity. We previously defined and validated a 53-immune gene melanoma immune profile (MIP) predictive both of distant metastatic recurrence and of disease-specific survival (DSS). Here, we test MIP on a third independent population. Experimental Design: A retrospective cohort of 78 patients with stage II–III primary melanoma was analyzed using the NanoString assay to measure expression of 53 target genes, and MIP score was calculated. Statistical analysis correlating MIP with DSS, overall survival, distant metastatic recurrence, and distant metastasis-free interval was performed using ROC curves, Kaplan–Meier curves, and standard univariable and multivariable Cox proportional hazards models. Results: MIP significantly distinguished patients with distant metastatic recurrence from those without distant metastatic recurrence using ROC curve analysis (AUC = 0.695; P = 0.008). We defined high- and low-risk groups based on the cutoff defined by this ROC curve and find that MIP correlates with both DSS and overall survival by ROC curve analysis (AUC = 0.719; P = 0.004 and AUC = 0.698; P = 0.004, respectively). Univariable Cox regression reveals that a high-risk MIP score correlates with DSS (P = 0.015; HR = 3.2). Conclusions: MIP identifies patients with low risk of death from melanoma and may constitute a clinical tool to stratify patients with stage II–III melanoma for enrollment in clinical trials.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
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".