Single-Cell Immune Mapping of Melanoma Sentinel Lymph Nodes Reveals an Actionable Immunotolerant Microenvironment
Bibliographic record
Abstract
PURPOSE: Improving our understanding of the immunologic response to cancer cells within the sentinel lymph nodes (SLN) of primary tumors is expected to identify new approaches to stimulate clinically meaningful cancer immunity. EXPERIMENTAL DESIGN: We used mass cytometry by time-of-flight (CyTOF), flow cytometry, and T-cell receptor immunosequencing to conduct simultaneous single-cell analyses of immune cells in the SLNs of patients with melanoma. RESULTS: We found increased effector-memory αβ T cells, TCR clonality, and γδ T cells selectively in the melanoma-bearing SLNs relative to non-melanoma-bearing SLNs, consistent with possible activation of an antitumor immune response. However, we also observed a markedly immunotolerant environment in the melanoma-bearing SLNs indicated by reduced and impaired NK cells and increased levels of CD8+CD57+PD-1+ cells, which are known to display low melanoma killing capabilities. Other changes observed in melanoma-bearing SLNs when compared with non-melanoma-bearing SLNs include (i) reduced CD8+CD69+ T cell/T regulatory cell ratio, (ii) high PD-1 expression on CD4+ and CD8+ T cells, and (iii) high CTLA-4 expression on γδ T cells. CONCLUSIONS: Our data suggest that these immunologic changes compromise antimelanoma immunity and contribute to a high relapse rate. We propose the development of clinical trials to test the neo-adjuvant administration of anti-PD-1 antibodies prior to SLN resection in patients with stage III melanoma. See related commentary by Lund, p. 1996.
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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 | 0.000 |
| 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".