Glycogen synthase kinase 3 (GSK-3) synergizes with immune-checkpoint blockade (ICB) to overcome tumor resistance due to unique tumor infiltrating T-cell subsets as revealed by mass cytometry (CyTOF)
Bibliographic record
Abstract
Abstract We have shown that the serine/threonine kinase glycogen synthase kinase-3 (GSK-3) is a major regulator of programmed cell death-1 (PD-1) transcription and expression in T-cells (Taylor et al., 2016, Immunity). Further, small molecule inhibitors (SMIs) increased Tbx21 transcription for enhanced Tbet expression which suppressed Pdcd1 transcription in CD8+cytolytic T-cells. SMIs against GSK-3 are as effective as anti-PD-1 blocking antibody in the control of spontaneous pulmonary metastasis of B16 melanoma and EL-4 lymphoma cells (Taylor et al., 2018, Cancer Research). This introduced a new chemical approach to down-regulate PD-1 for the treatment of cancer. In addition, GSK-3 effects in Tbet expression can induce the expression of interferon-γ1 (IFNγ1) and granzyme B (GZMB) for enhanced cytolytic T-cell (CTL) killing of tumors suggesting that GSK-3 SMIs might synergize with check-point blockade. We now show that GSK-3α/β inhibition can cooperate with anti-PD-1 blockade to overcome tumor resistance. Using high dimensional profiling of tumor infiltrated lymphocytes (TILs) with mass cytometry (CyTOF), we show that the synergy of GSK-3 SMI and anti-PD-1 is accompanied by 2 specific findings: (i) an increase in the presence of a unique subset of CD8+ TIL effector-memory T-cells beyond that observed with anti-PD-1 alone, and (ii) the significant decrease of a subset of CD4+ suppressor regulatory T-cells (Tregs) not observed with anti-PD-1 alone. Our findings offer a small molecule approach for synergy with anti-PD-1 that leads to an increase in CD8+ T-cells and the marked decrease of Tregs in 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) | 0.002 | 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".