CD28 and glycogen synthase kinase-3 (GSK-3) inhibitor combination therapy generates tumor infiltrating T-cells (TILs) with a reversal of markers for T-cell exhaustion
Bibliographic record
Abstract
Abstract Immune checkpoint blockade (ICB) of inhibitory receptors such as PD-1 has revolutionized the treatment of cancer. ICB is dependent on CD28 co-receptor expression introducing the question of whether the positive co-receptor could be targeted for the reversal of T-cell exhaustion in immunotherapy. We previously showed that the serine/threonine kinase glycogen synthase kinase-3 (GSK-3) is a central regulator of programmed cell death-1 (PD-1) transcription (Taylor et al., 2016, Immunity) and that small molecule inhibitors of GSK-3 (GSK-3 SMI) are as effective as anti-PD-1 blocking antibody in the control of tumor growth (Taylor et al., 2018, Cancer Research). In this study, we now show that anti-CD28 can synergise with GSK-3α/β inhibitors to regress tumors that are resistant to anti-PD-1 immunotherapy. Combination therapy showed responses in >55% mice with an overall reduction in tumor volume of 70% in responders compared to control mice (182 mm3 vs. 611 mm3). Flow cytometry analysis of tumor infiltrating lymphocytes (TILs) shows that, while non-treated mice were enriched with TOX+TIM3+PD1+CD8+, combination therapy increased tumor infiltration of TOX-negative CD8 T cells with TIM3-PD1int CD8 phenotype. Remarkably, this reversal involved only a single injection of anti-CD28 (PV-1) and GSK-3 SMI (SB415286). These results showed that CD28/GSK-3 combination therapy greatly reduced TOX, TIM-3 and PD1markers indicative of exhaustion. Our findings offer a small molecule approach for synergy with anti-CD28 that leads to an increase tumor infiltration with TOX-negative CD8 T cells that have decrease expression of inhibitory receptors.
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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) | 0.001 | 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".