Peripheral PD-1+CD56+ T-cell frequencies correlate with outcome in stage IV melanoma under PD-1 blockade
Bibliographic record
Abstract
Immune checkpoint blockade with anti-PD-1 antibodies is showing great promise for patients with metastatic melanoma and other malignancies, but despite good responses by some patients who achieve partial or complete regression, many others still do not respond. Here, we sought peripheral blood T-cell biomarker candidates predicting treatment outcome in 75 stage IV melanoma patients treated with anti-PD-1 antibodies. We investigated associations with clinical response, progression-free survival (PFS) and overall survival (OS). Univariate analysis of potential biological confounders and known biomarkers, and a multivariate model, was used to determine statistical independence of associations between candidate biomarkers and clinical outcomes. We found that a lower than median frequency of peripheral PD-1+CD56+ T-cells was associated with longer OS (p = 0.004), PFS (p = 0.041) and superior clinical benefit (p = 0.009). However, neither frequencies of CD56-CD4+ nor CD56-CD8+ T-cells, nor of the PD-1+ fraction within the CD4 or CD8 subsets was associated with clinical outcome. In a multivariate model with known confounders and biomarkers only the M-category (HR, 3.11; p = 0.007) and the frequency of PD-1+CD56+ T-cells (HR, 2.39; p = 0.028) were identified as independent predictive factors for clinical outcome under PD-1 blockade. Thus, a lower than median frequency of peripheral blood PD-1+CD56+ T-cells prior to starting anti-PD-1 checkpoint blockade is associated with superior clinical response, longer PFS and OS of stage IV melanoma patients.
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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 | 0.000 | 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".