A Mechanism of Hypoxia-Mediated Escape from Adaptive Immunity in Cancer Cells
Bibliographic record
Abstract
Immune escape is a fundamental trait of cancer in which mechanistic knowledge is incomplete. Here, we describe a novel mechanism by which hypoxia contributes to tumoral immune escape from cytotoxic T lymphocytes (CTL). Exposure of human or murine cancer cells to hypoxia for 24 hours led to upregulation of the immune inhibitory molecule programmed cell death ligand-1 (PD-L1; also known as B7-H1), in a manner dependent on the transcription factor hypoxia-inducible factor-1α (HIF-1α). In vivo studies also demonstrated cellular colocalization of HIF-1α and PD-L1 in tumors. Hypoxia-induced expression of PD-L1 in cancer cells increased their resistance to CTL-mediated lysis. Using glyceryl trinitrate (GTN), an agonist of nitric oxide (NO) signaling known to block HIF-1α accumulation in hypoxic cells, we prevented hypoxia-induced PD-L1 expression and diminished resistance to CTL-mediated lysis. Moreover, transdermal administration of GTN attenuated tumor growth in mice. We found that higher expression of PD-L1 induced in tumor cells by exposure to hypoxia led to increased apoptosis of cocultured CTLs and Jurkat leukemia T cells. This increase in apoptosis was prevented by blocking the interaction of PD-L1 with PD-1, the PD-L1 receptor on T cells, or by addition of GTN. Our findings point to a role for hypoxia/HIF-1 in driving immune escape from CTL, and they suggest a novel cancer immunotherapy to block PD-L1 expression in hypoxic-tumor cells by administering NO mimetics.
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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".