Small Molecule Inhibition of GSK-3 Specifically Inhibits the Transcription of Inhibitory Co-receptor LAG-3 for Enhanced Anti-tumor Immunity
Bibliographic record
Abstract
Immune checkpoint blockade using antibodies against negative co-receptors such as cytolytic T cell antigen-4 (CTLA-4) and programmed cell death-1 (PD-1) has seen much success treating cancer. However, most patients are still not cured, underscoring the need for improved treatments and the possible development of small molecule inhibitors (SMIs) for improved immunotherapy. We previously showed that glycogen synthase kinase (GSK)-3α/β is a central regulator of PD-1 expression, where GSK-3 inhibition down-regulates PD-1 and enhances CD8 + cytolytic T cell (CTL) function, reducing viral infections and tumor growth. Here, we demonstrate that GSK-3 also negatively regulates Lymphocyte Activation Gene-3 (LAG-3) expression on CD4 + and CD8 + T cells. GSK-3 SMIs are more effective than LAG-3 blockade alone in suppressing B16 melanoma growth, while their combination resulted in enhanced tumor clearance. This was linked to increased expression of the transcription factor, Tbet, which bound the LAG-3 promoter, inhibiting its transcription, and to increased granzyme B and interferon-γ1 expression. Overall, we describe a small molecule approach to inhibit LAG-3, resulting in enhanced anti-tumor immunity.
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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) | 0.002 | 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".