Small molecule drug inhibition of PD-1 transcription is as effective as anti-PD-1 biologic blockade in cancer therapy
Bibliographic record
Abstract
Abstract Despite the importance of the co-receptor PD-1 in T cell immunity, the upstream signaling pathway(s) that regulate PD-1 expression has not been defined. Glycogen synthase kinase 3 (GSK-3, isoform α and β) is a serine-threonine kinase implicated in cellular processes. We have shown that GSK-3 is a key upstream kinase that regulates PD-1 expression in CD8+ T cells. GSK-3 inactivation increased Tbx21 transcription for enhanced Tbet expression, and its suppression of Pdcd1 transcription in CD8+ cytolytic T-cells (CTLs) (Taylor et al 2016 Immunity 44, 274–86). Here, we show that GSK-3 inhibitor blockade of pcdc1 (PD-1) transcription with a small molecule inhibitor (i.e. SB415286) is as effective as anti-PD-1 and PDL-1 blocking antibodies in the control of pulmonary metastasis of B16 melanoma, intra-dermally injected B16 and EL4 lymphoma solid tumors, and in the ex vivo pre-treatment of T-cells before adoptive transfer into mice carrying tumors. In an analysis of knock out mice, GSK-3α/β−/− mice, which had greatly reduced PD-1 expression on T-cells, showed the same reduction in B16 pulmonary metastasis as Pdcd1−/− mice. Further, the ex vivo treatment of T-cells with SB415286, anti-PD-1, or in combination followed by adoptive transfer, had identical effects in inhibiting EL4 lymphoma growth. In all tumor models, GSK-3 inactivation markedly inhibited Pdcd1 transcription and PD-1 expression on tumor infiltrating T-cells (TILs), while increasing Tbx21 (Tbet) transcription and the presence of CD8+ TILs expressing CD107a+ (LAMP1) and granzyme B (GZMB). Our findings define for the first time that a next generation approach using small molecule inhibition of PD-1 expression is as effective as anti-PD-1/PL1 biologics in cancer therapy.
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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".