Small-Molecule Inhibition of PD-1 Transcription Is an Effective Alternative to Antibody Blockade in Cancer Therapy
Bibliographic record
Abstract
Abstract The impact of PD-1 immune checkpoint therapy prompts exploration of other strategies to downregulate PD-1 for cancer therapy. We previously showed that the serine/threonine kinase, glycogen synthase kinase, GSK-3α/β, is a central regulator of PD-1 transcription in CD8+ T cells. Here, we show that the use of small-molecule inhibitors of GSK-3α/β (GSK-3i) to reduce pcdc1 (PD-1) transcription and expression was as effective as anti–PD-1 and PD-L1–blocking antibodies in the control of B16 melanoma, or EL4 lymphoma, in primary tumor and metastatic settings. Furthermore, the conditional genetic deletion of GSK-3α/β reduced PD-1 expression on CD8+ T cells and limited B16 pulmonary metastasis to the same degree as PD-1 gene deficiency. In each model, GSK-3i inhibited PD-1 expression on tumor-infiltrating lymphocytes, while increasing Tbx21 (T-bet) transcription, and the expression of CD107a+ (LAMP1) and granzyme B (GZMB) on CD8+ T cells. Finally, the adoptive transfer of T cells treated ex vivo with a GSK-3 inhibitor delayed the onset of EL4 lymphoma growth to a similar extent as anti–PD-1 pretreatment. Overall, our findings show how GSK-3 inhibitors that downregulate PD-1 expression can enhance CD8+ T-cell function in cancer therapy to a similar degree as PD-1–blocking antibodies. Significance: These findings show how GSK-3 inhibitors that downregulate PD-1 expression can enhance CD8+ T-cell function in cancer therapy to a similar degree as PD-1 blocking antibodies, offering a next-generation approach in the design of immunotherapeutic approaches for cancer management. Cancer Res; 78(3); 706–17. ©2017 AACR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".