Glycogen synthase kinase-3 (GSK-3) inactivation compensates for the lack of CD28 in the priming of CD8+ cytotoxic T-cells: Implications for anti-PD-1 immunotherapy
Bibliographic record
Abstract
Abstract The rescue of exhausted CD8+ cytolytic T-cells (CTLs) by anti-programmed cell death-1 (anti-PD-1) immune checkpoint blockade has been found to require CD28 expression. At the same time, we have shown that the inactivation of the serine/threonine kinase glycogen synthase kinase (GSK)-3α/β with small-interfering RNAs (siRNAs) and small molecule inhibitors (SMIs) specifically down-regulates PD-1 expression for enhanced CD8+ CTL function and clearance of tumors and viral infections (Taylor et al., 2016 Immunity, 44(2):274-86, Taylor et al., 2017 Cancer Research, canres.0491.2017). It has been unclear whether the GSK-3α/β pathway accounts for CD28 costimulation of CD8+ CTL function. In this presentation, we show for the first time that the inactivation of GSK-3α/β through siRNA or by SMIs during priming can substitute CD28 co-stimulation in the potentiation of cytotoxic CD8+ CTL function against the EL-4 lymphoma cells expressing OVA peptide. The effect was seen using several structurally distinct GSK-3 SMIs and was accompanied by an increase in Lamp-1 and GZMB expression. Conversely, CD28 crosslinking obviated the need for GSK-3α/β inhibition in its enhancement of CTL function. Our findings support a model where GSK-3 is the central co-signal for CD28 priming of CD8+ CTLs in anti-PD-1 immunotherapy. Anti-PD-1 and GSK-3 SMIs cooperate synergistically to limit the growth of solid melanoma tumors in mice. We will present data on the altered nature of tumor-infiltrating lymphocytes (TILs) in response to anti-PD-1 versus GSK-3 blockade and combination therapy. Our findings outline a next-generation approach using small molecule inhibitors in combination with check-point blockade therapy for the elimination of tumors.
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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".