Targeting Glycosylated PD-1 Induces Potent Antitumor Immunity
Bibliographic record
Abstract
Immunotherapies targeting programmed cell death protein 1 (PD-1) and programmed cell death 1 ligand 1 (PD-L1) immune checkpoints represent a major breakthrough in cancer treatment. PD-1 is an inhibitory receptor expressed on the surface of activated T cells that dampens T-cell receptor (TCR)/CD28 signaling by engaging with its ligand PD-L1 expressed on cancer cells. Despite the clinical success of PD-1 blockade using mAbs, most patients do not respond to the treatment, and the underlying regulatory mechanisms of PD-1 remain incompletely defined. Here we show that PD-1 is extensively N-glycosylated in T cells and the intensities of its specific glycoforms are altered upon TCR activation. Glycosylation was critical for maintaining PD-1 protein stability and cell surface localization. Glycosylation of PD-1, especially at the N58 site, was essential for mediating its interaction with PD-L1. The mAb STM418 specifically targeted glycosylated PD-1, exhibiting higher binding affinity to PD-1 than FDA-approved PD-1 antibodies, potently inhibiting PD-L1/PD-1 binding, and enhancing antitumor immunity. Together, these findings provide novel insights into the functional significance of PD-1 glycosylation and offer a rationale for targeting glycosylated PD-1 as a potential strategy for immunotherapy. SIGNIFICANCE: These findings demonstrate that glycosylation of PD-1 is functionally significant and targeting glycosylated PD-1 may serve as a means to improve immunotherapy response.
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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) | 0.001 | 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".