Contribution of NK cells to immunotherapy mediated by PD-1/PD-L1 blockade
Bibliographic record
Abstract
Checkpoint blockade immunotherapy targeting the PD-1/PD-L1 inhibitory axis has produced remarkable results in the treatment of several types of cancer. Whereas cytotoxic T cells are known to provide important antitumor effects during checkpoint blockade, certain cancers with low MHC expression are responsive to therapy, suggesting that other immune cell types may also play a role. Here, we employed several mouse models of cancer to investigate the effect of PD-1/PD-L1 blockade on NK cells, a population of cytotoxic innate lymphocytes that also mediate antitumor immunity. We discovered that PD-1 and PD-L1 blockade elicited a strong NK cell response that was indispensable for the full therapeutic effect of immunotherapy. PD-1 was expressed on NK cells within transplantable, spontaneous, and genetically induced mouse tumor models, and PD-L1 expression in cancer cells resulted in reduced NK cell responses and generation of more aggressive tumors in vivo. PD-1 expression was more abundant on NK cells with an activated and more responsive phenotype and did not mark NK cells with an exhausted phenotype. These results demonstrate the importance of the PD-1/PD-L1 axis in inhibiting NK cell responses in vivo and reveal that NK cells, in addition to T cells, mediate the effect of PD-1/PD-L1 blockade immunotherapy.
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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) | 0.001 | 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) | 0.001 | 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".