Uveal melanoma: Real-world analysis of immune checkpoint inhibitors efficacy and lymphocyte-to-monocyte ratio as a biomarker of response.
Bibliographic record
Abstract
e21590 Background: Uveal melanoma (UM) is a rare subtype of melanoma. Real-world data is needed to guide management since patients with advanced UM were excluded from immune checkpoint inhibition (ICI) phase III clinical trials. Lymphopenia and elevated monocyte count, which represents a surrogate marker of systemic inflammation and a source of pro-angiogenic tumor-associated macrophages (TAM), have been associated with poor treatment outcomes in various solid tumors. We sought to characterize ICI efficacy in patients with UM vs cutaneous melanoma (CM) and to investigate pre-treatment lymphocyte-to-monocyte ratio (LMR) as a biomarker of response. Methods: We conducted a multicentric cohort study across 3 academic centers in Canada. Best overall responses as per the investigators were recorded. Overall survival (OS) and progression-free survival (PFS) were compared using the log rank (Mantel-Cox) test, with univariate analyses performed using Cox proportional hazard regression model. Results: A total of 122 patients with metastatic melanoma were included, either UM (n = 60) or CM (n = 62). UM patients were treated either with anti-PD-1 monotherapy (69%) or combination with anti-CTLA-4 (31%). Median OS was 35.4 months for CM versus 8.0 months for UM (HR 0.38, 95%CI 0.24-0.60, p = 0.0001). Median PFS was 10.7 months for CM versus 3.5 months for UM (HR 0.38, 95%CI 0.22-0.65, p = 0.0001). Best overall response in the UM group was progressive disease (74%), stable disease (10%) or partial response (16%). In UM patients, higher baseline LMR (HR 0.48, 95%CI 0.24-0.97, p = 0.04) and presence of immune-related adverse events (HR 0.42, 95%CI 0.20-0.89, p = 0.02) were associated with improved OS in univariate analysis. In CM, such association with high LMR was not significant (HR 0.58, 95% 0.28-1.21, p = 0.10) unless excluding patients with corticosteroid-induced lymphopenia due to recently treated brain metastases (HR 0.42, 95%CI 0.19-0.92, p = 0.03). Conclusions: Advanced UM treated with ICI have inferior survival outcomes compared with cutaneous primaries. Higher pre-treatment LMR is associated with improved OS to ICI in UM patients and could represent a surrogate marker of immune activation. Our findings reinforce the need for new treatment strategies in UM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".