Mechanisms of PD-L1 Regulation in Malignant and Virus-Infected Cells
Bibliographic record
Abstract
Programmed cell death protein 1 (PD-1), a receptor on T cells, and its ligand, PD-L1, have been a topic of much interest in cancer research. Both tumour and virus-infected cells can upregulate PD-L1 to suppress cytotoxic T-cell killing. Research on the PD-1/PD-L1 axis has led to the development of anti-PD-1/PD-L1 immune checkpoint blockades (ICBs) as promising cancer therapies. Although effective in some cancer patients, for many, this form of treatment is ineffective due to a lack of immunogenicity in the tumour microenvironment (TME). Despite the development of therapies targeting the PD-1/PD-L1 axis, the mechanisms and pathways through which these proteins are regulated are not completely understood. In this review, we discuss the latest research on molecules of inflammation and innate immunity that regulate PD-L1 expression, how its expression is regulated during viral infection, and how it is modulated by different cancer therapies. We also highlight existing research on the development of different combination therapies with anti-PD-1/PD-L1 antibodies. This information can be used to develop better cancer immunotherapies that take into consideration the pathways involved in the PD-1/PD-L1 axis, so these molecules do not reduce their efficacy, which is currently seen with some cancer therapies. This review will also assist in understanding how the TME changes during treatment, which will provide further rationale for combination therapies.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".