Downregulation of PTP1B and TC-PTP phosphatases potentiate dendritic cell-based immunotherapy through IL-12/IFNγ signaling
Bibliographic record
Abstract
PTP1B and TC-PTP are highly related protein-tyrosine phosphatases (PTPs) that regulate the JAK/STAT signaling cascade essential for cytokine-receptor activation in immune cells. Here, we describe a novel immunotherapy approach whereby monocyte-derived dendritic cell (moDC) function is enhanced by modulating the enzymatic activities of PTP1B and TC-PTP. To downregulate or delete the activity/expression of these PTPs, we generated mice with PTP-specific deletions in the dendritic cell compartment or used PTP1B and TC-PTP specific inhibitor. While total ablation of PTP1B or TC-PTP expression leads to tolerogenic DCs via STAT3 hyperactivation, downregulation of either phosphatase remarkably shifts the balance toward an immunogenic DC phenotype due to hyperactivation of STAT4, STAT1 and Src kinase. The resulting increase in IL-12 and IFNγ production subsequently amplifies the IL-12/STAT4/IFNγ/STAT1/IL-12 positive autocrine loop and enhances the therapeutic potential of mature moDCs in tumor-bearing mice. Furthermore, pharmacological inhibition of both PTPs improves the maturation of defective moDCs derived from pancreatic cancer (PaC) patients. Our study provides a new advance in the use of DC-based cancer immunotherapy that is complementary to current cancer therapeutics.
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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".