Glycogen synthase kinase (GSK-3) synergizes with PD-1/PDL1 blockade to generate super-armed CD8 killers against tumors
Bibliographic record
Abstract
Abstract Immune checkpoint blockade (ICB) of negative co-receptors on T-cells such as programmed cell death-1 (PD-1) is promising for the treatment of cancer. Despite success, the poor prognosis for most patients highlights the need for novel clinical interventions. We have shown that the kinase, glycogen synthase kinase-3 (GSK-3) negatively regulates T-cell activation due to altered PD-1 and LAG-3 expression (Taylor et al., 2016 Immunity; Rudd et al., 2019 Cell Reports). GSK-3 inhibition (GSK-3i) is as effective as anti-PD-1 in controlling the growth of melanoma (Taylor et al., 2017 Can Res; Krueger and Rudd, Immunity 2017; Stelle et al., 2021 iScience). GSK-3i increases Tbet (Tbx21) transcription, which inhibits PD-1/LAG-3 transcription, while increasing granzyme B (GZMB) and interferon gamma (IFNγ). Here, we show that Gsk3−/− mice and small molecule inhibitors (SMIs) synergize with anti-PD-1 to eliminate melanomas that are resistant to anti-PD-1 monotherapy. Transcriptomic profiling showed that GSK-3 × PD-1 cooperativity was characterized by a specific increase in a family of different granzymes (7/12 GZM genes out of a data base of 20,500 potential genes). Some GZMs have been characterized and others not, but as a family, this increased armory of GZMs in CD8+ T-cells is expected to greatly enhance tumor killing. Overall, our data shows the PD-1 × GSK-3 synergy in limiting tumor growth is due to a specific set of cytolysis mediators needed for tumor killing.
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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".