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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".