PD-1 endocytosis unleashes the cytolytic potential of check-point blockade in tumor immunity
Bibliographic record
Abstract
Abstract PD-1 immune checkpoint blockade (ICB) has revolutionized cancer treatment. Present models indicate that anti-PD-1 operates by preventing PD-1 from binding to its ligand PD-L1. Surprisingly, ICB-mediated internalization of PD-1 and its importance have not been fully explored. Here, we demonstrate that PD-1 internalization is associated with increased cytolytic activity during cancer immunotherapy. Anti-hPD-1 and -mPD-1 antibodies induced internalization of PD-1 generating a PD-1int subset, resistant to further internalization. Moreover, anti-PD-1 downregulation was more effective in CD8+ T cells than CD4+ T cells. ICB also induced PD-1 degradation in a proteosome-dependent manner. Interestingly, Nivolumab outperformed Pembrolizumab with both antibodies exhibiting distinct effects on CD8+ effector and effector memory T-cells. Importantly, PD-1 downregulation was dependent on receptor crosslinking by bivalent but not monovalent antibodies. Consistent with this, B16-PD-L1+ tumor-bearing mice treated with bivalent antibodies showed better anti-tumor response relative to monovalent antibodies. While mono and bivalent blockade increased granzyme B expression, only bivalent antibody induced perforin in CD8+ T-cells. Our findings unveil a mechanism beyond steric blockade and underscore the significance of PD-1 endocytosis in optimizing checkpoint blockade. Targeting PD-1 internalization holds promise for enhancing anti-tumor immunity and improving the efficacy of PD-1 ICB.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".