A Functional Role of HLA-G Expression in Human Gliomas: An Alternative Strategy of Immune Escape
Bibliographic record
Abstract
HLA-G is a nonclassical MHC molecule with highly limited tissue distribution that has been attributed chiefly immune regulatory functions. Glioblastoma is paradigmatic for the capability of human cancers to paralyze the immune system. To delineate the potential role of HLA-G in glioblastoma immunobiology, expression patterns and functional relevance of this MHC class Ib molecule were investigated in glioma cells and brain tissues. HLA-G mRNA expression was detected in six of 12 glioma cell lines in the absence of IFN-gamma and in 10 of 12 cell lines in the presence of IFN-gamma. HLA-G protein was detected in four of 12 cell lines in the absence of IFN-gamma and in eight of 12 cell lines in the presence of IFN-gamma. Immunohistochemical analysis of human brain tumors revealed expression of HLA-G in four of five tissue samples. Functional studies on the role of HLA-G in glioma cells were conducted with alloreactive PBMCs, NK cells, and T cell subpopulations. Expression of membrane-bound HLA-G1 and soluble HLA-G5 inhibited alloreactive and Ag-specific immune responses. Gene transfer of HLA-G1 or HLA-G5 into HLA-G-negative glioma cells (U87MG) rendered cells highly resistant to direct alloreactive lysis, inhibited the alloproliferative response, and prevented efficient priming of cytotoxic T cells. The inhibitory effects of HLA-G were directed against CD8 and CD4 T cells, but appeared to be NK cell independent. Interestingly, few HLA-G-positive cells within a population of HLA-G-negative tumor cells exerted significant immune inhibitory effects. We conclude that the aberrant expression of HLA-G may contribute to immune escape in human glioblastoma.
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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".