GSK-3 regulates CD4-CD8 cooperation needed to generate super-armed CD8+ cytolytic T cells against tumors
Bibliographic record
Abstract
While immune checkpoint blockade (ICB) has revolutionized cancer treatment, the key T-cell signaling pathways responsible for its potency remain unclear. GSK-3 is an inhibitory kinase that is most active in resting T-cells. In this study, we demonstrate that GSK-3 facilitates PD-1 blockade, an effect seen by modulating CD4 T-cell help for CD8+ CTL responses against ICB resistant tumors. We show that GSK-3 controls metabolic reprogramming towards glycolysis and synergizes with PD-1 to induce a transcriptional program that reduces suppressive CD4+ Treg numbers while generating super-armed effector-memory CD8+ CTLs that express an unprecedented 7/9 granzymes from the genome. Crucially, we found that GSK-3 cooperates with PD-1 blockade to determine the dependency of CD8+ CTLs on help from CD4+ T-cells. Our study unravels a novel cooperative PD-1 blockade-dependent signaling pathway that potentiates CTL responses against tumors, offering a new strategy to overcome immunotherapy resistance by modulating CD4+ helper and CD8+ cytotoxic functions. Significance: This study demonstrates for the first time that GSK-3 controls the crosstalk between CD4+ and CD8+ T cells, synergizing with anti-PD-1 therapy to overcome resistance to checkpoint blockade and to generate super-armed CD8+ effector cells in cancer immunotherapy. This newly uncovered GSK-3-dependent CD4-CD8 T-cell crosstalk mechanism presents a new approach to enhance anti-PD-1 immunotherapy.
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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".