MHC Class II Engagement by Its Ligand LAG-3 (CD223) Contributes to Melanoma Resistance to Apoptosis
Bibliographic record
Abstract
Melanoma is the most aggressive skin cancer in humans that often expresses MHC class II (MHC II) molecules, which could make these tumors eliminable by the immune system. However, this MHC II expression has been associated with poor prognosis, and there is a lack of immune-mediated eradication. The lymphocyte activation gene-3 (LAG-3) is a natural ligand for MHC II that is substantially expressed on melanoma-infiltrating T cells including those endowed with potent immune-suppressive activity. Based on our previous data showing the signaling capacity of MHC II in melanoma cells, we hypothesized that LAG-3 could contribute to melanoma survival through its MHC II signaling capacity in melanoma cells. In this study, we demonstrate that both soluble LAG-3 and LAG-3-transfected cells can protect MHC II-positive melanoma cells, but not MHC II-negative cells, from FAS-mediated and drug-induced apoptosis. Interaction of LAG-3 with MHC II expressed on melanoma cells upregulates both MAPK/Erk and PI3K/Akt pathways, albeit with different kinetics. Inhibition studies using specific inhibitors of both pathways provided evidence of their involvement in the LAG-3-induced protection from apoptosis. Altogether, our data suggest that the LAG-3-MHC II interaction could be viewed as a bidirectional immune escape pathway in melanoma, with direct consequences shared by both melanoma and immune cells. In the future, compounds that efficiently hinder LAG-3-MHC II interaction might be used as an adjuvant to current therapy for MHC II-positive melanoma.
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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".