Genome-Wide Methylation Patterns in Primary Uveal Melanoma: Development of MethylSig-UM, an Epigenomic Prognostic Signature to Improve Patient Stratification
Bibliographic record
Abstract
Despite studies highlighting the prognostic utility of DNA methylation in primary uveal melanoma (pUM), it has not been translated into a clinically useful tool. We sought to define a methylation signature to identify newly diagnosed individuals at high risk for developing metastasis. Methylation profiling was performed on 41 patients with pUM with stage T2-T4 and at least three years of follow-up using the Illumina Infinium HumanMethylation450K BeadChip (N = 24) and the EPIC BeadChip (N = 17). Findings were validated in the TCGA cohort with known metastatic outcome (N = 69). Differentially methylated probes were identified in patients who developed metastasis. Unsupervised consensus clustering revealed three epigenomic subtypes associated with metastasis. To identify a prognostic signature, recursive feature elimination and random forest models were utilized within repeated cross-validation iterations. The 250 most commonly selected probes comprised the final signature, named MethylSig-UM. MethylSig-UM could distinguish individuals with pUM at diagnosis who develop future metastasis with an area under the curve of ~81% in the independent validation cohort, and remained significant in Cox proportional hazard models when combined with clinical features and established genomic biomarkers. Altered expression of immune-modulating genes were detected in MethylSig-UM positive tumors, providing clues for pUM resistance to immunotherapy. The MethylSig-UM model is available to enable additional validation in larger cohort sizes including T1 tumors.
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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 | 0.001 |
| 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) | 0.000 | 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".