Switch ofHLA-G alternative splicing in a melanoma cell line causes loss of HLA-G1 expression and sensitivity to NK lysis
Bibliographic record
Abstract
Considerable information has been accumulated on HLA-G expression in tumor lesions in which HLA-G is viewed as a way to turn off anti-tumoral immunity. Nevertheless, there is little data concerning the mechanisms by which expression and function of HLA-G are regulated in malignant cells. Here, we have addressed these points by studying a melanoma cell line derived from a surgically-removed HLA-G-positive melanoma lesion. We show that HLA-G expression in melanoma cells can be regulated at the mRNA splicing level. Indeed, melanoma cells rapidly switched from cell-surface HLA-G1 to intra-cellular HLA-G2 expression. This mechanism restored tumor sensitivity to NK lysis. Moreover, switch from HLA-G1 to HLA-G2 was strong enough to prevent re-expression of immunoprotective HLA-G1 even following treatments with cytokines and DNA demethylating agent. Modulating HLA-G at the mRNA splicing level would be an efficient way of lifting in vivo HLA-G-mediated tumor immune escape.
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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 | 0.000 |
| 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".