When killers become thieves: trogocytosed PD-1 inhibits NK cells in cancer
Bibliographic record
Abstract
Abstract Leucocytes often perform trogocytosis, the process by which cells acquire parts of the plasma membrane from interacting cells. Accumulating evidence indicates that trogocytosis modulates immune responses, but the underlying molecular mechanisms are unclear. Here, using two mouse models of leukemia, we found that cytotoxic lymphocytes perform trogocytosis at high rates with tumor cells. While performing trogocytosis, both Natural Killer and CD8 + T cells acquire the checkpoint receptor PD-1 from leukemia cells. In vitro and in vivo investigation revealed that PD-1 protein found on the surface of Natural Killer cells, rather than being endogenously expressed, was derived entirely from leukemia cells. Mechanistically, SLAM receptors were essential for PD-1 trogocytosis. PD-1 acquired via trogocytosis actively suppressed anti-tumor immunity, as revealed by the positive outcome of PD-1 blockade in PD-1-deficient mice. PD-1 trogocytosis was corroborated in patients with clonal plasma cell disorders, where Natural Killer cells that stained for PD-1 also stained for tumor cell markers. Our results, in addition to shedding light on a previously unappreciated mechanism underlying the presence of PD-1 on Natural Killer and cytotoxic T cells, reveal the immune-regulatory effect of membrane transfer occurring when immune cells contact tumor cells. Once sentence summary Natural Killer cells are inhibited by PD-1 acquired from the surface of tumor cells via trogocytosis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".