Global pannexin 1 deletion increases tumor‐infiltrating lymphocytes in the <scp>BRAF</scp> /Pten mouse melanoma model
Bibliographic record
Abstract
Immunotherapies for malignant melanoma seek to boost the anti‐tumoral response of CD8 + T cells, but have a limited patient response rate, in part due to limited tumoral immune cell infiltration. Genetic or pharmacological inhibition of the pannexin 1 (PANX1) channel‐forming protein is known to decrease melanoma cell tumorigenic properties in vitro and ex vivo . Here, we crossed Panx1 knockout ( Panx1 −/− ) mice with the inducible melanoma model Braf CA , Pten loxP , Tyr::CreER T2 (BPC). We found that deleting the Panx1 gene in mice does not reduce BRAF(V600E)/Pten‐driven primary tumor formation or improve survival. However, tumors in BPC‐ Panx1 −/− mice exhibited a significant increase in the infiltration of CD8 + T lymphocytes, with no changes in the expression of early T‐cell activation marker CD69, lymphocyte activation gene 3 protein (LAG‐3) checkpoint receptor, or programmed cell death ligand‐1 (PD‐L1) in tumors when compared to the BPC‐ Panx1 +/+ genotype. Our results suggest that, although Panx1 deletion does not overturn the aggressive BRAF / Pten ‐driven melanoma progression in vivo , it does increase the infiltration of effector immune T‐cell populations in the tumor microenvironment. We propose that PANX1‐targeted therapy could be explored as a strategy to increase tumor‐infiltrating lymphocytes to boost anti‐tumor immunity.
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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".