Commentary: Small Molecule Inhibition of PD-1 Transcription is an Effective Alternative to Antibody Blockade in Cancer Therapy
Bibliographic record
Abstract
The past few years has witnessed exciting progress in the application of "immune check-point inhibitors" (ICI) in the treatment of various human cancers 1-3 .This involves the use of antibody blockade with monoclonal antibodies (mAbs) that block receptor binding to their natural ligands.Programmed cell death-1 (PD-1) recognises PD ligand (PDL)-1 and PDL-2 on presenting cells and this sends signals that inhibit T-cell activation and effector cytotoxic responses.Through these mechanisms, PD-1 inhibits the immune system and can prevent autoimmune diseases 4 .Tumor cells expressing PDL-1/PD-L2 can use this mechanism to evade immune surveillance, allowing disease progression.A therapeutic approach involves administration of mAbs that block the engagement of checkpoint molecules with their ligand.In the case of anti-PD-1, these mAbs block the binding of PD-1 on the T-cell with PDL-1/ PDL-2 on the tumor cell, preventing recognition and allowing activation of the T-cell to provide an immune response against the tumor cell.Blockade also reverses T-cell exhaustion and restores T-cell functionality 5, 6 .Furthermore, PD-1 expression on tumorinfiltrating CD8 + T-cells correlates with impaired function, while PD-L1 expression on tumors facilitates escape 4 .
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.027 | 0.036 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".