Tumor miRNA Signatures Associate with Outcomes of Patients with Stage II/III Melanoma
Bibliographic record
Abstract
PURPOSE: Patients with stage II and resected stage III melanomas have variable clinical outcomes, providing evidence of underlying biological differences in tumors and/or the patients themselves, beyond stage. The approval of adjuvant immunotherapy for stage IIB/C and resected stage III/IV disease (and adjuvant targeted therapy for resected stage III disease) has created a pressing need to develop biomarkers to accurately distinguish patients at low risk versus high risk for recurrence and death from melanoma. miRNAs are promising biomarkers because of their stability in tissues and fluids and their demonstrated functional and prognostic roles in melanoma. We hypothesized that miRNA expression could be integrated into prognostic models that would classify 5-year survival outcomes more accurately than clinical factors alone. EXPERIMENTAL DESIGN: Using a NanoString miRNA Expression Assay, we analyzed 715 primary melanomas from patients with stage II or stage III disease within the InterMEL consortium and examined associations between miRNA expression and melanoma-specific death. RESULTS: When integrated into clinical prognostic models for 5-year melanoma-specific survival, miRNA signatures improved the area under the receiver operating characteristic curve for patients in stage II from 0.71 for a "clinical factors-only" model to 0.81 for a "clinical plus miRNA" model in an independent test set, an improvement of 0.10 with a 95% confidence interval (0.03-0.19). The improvement was more modest for patients in stage III who were included in the analysis. CONCLUSIONS: Incorporating miRNA expression in primary melanomas may enhance the accuracy of clinical prognostic models and potentially aid in the selection of patients with melanoma for adjuvant treatment and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".