Mechanisms Of Hypoxia-Induced Immune Escape In Cancer And Their Regulation By Nitric Oxide
Bibliographic record
Abstract
The acquired ability of tumour cells to avoid destruction by immune effector mechanisms (immune escape) is important for malignant progression. Also associated with malignant progression is tumour hypoxia , which induces aggressive phenotypes such as invasion, metastasis and drug resistance in cancer cells. Our studies revealed that hypoxia contributes to escape from innate immunity by increasing tumour cell expression of the metalloproteinase ADAM10 in a manner dependent on accumulation of the alpha subunit of the transcription factor hypoxia-inducible factor-1 (HIF-1α). Increased ADAM10 expression leads to shedding of the NK cell-activating ligand, MICA, from the surface of tumour cells, thereby resulting in resistance to NK cell-mediated lysis. Our more recent studies demonstrated that hypoxia, also via HIF-1α accumulation, increases the expression of the inhibitory co-stimulatory ligand PD-L1 on tumour cells. Elevated PD-L1 expression leads to escape from adaptive immunity via increased apoptosis of CD8 + cytotoxic T lymphocytes . Accumulating evidence indicates that hypoxia-induced acquisition of malignant phenotypes, including immune escape, is in part due to impaired nitric oxide (NO)-mediated activation of cGMP signalling and that restoration of cGMP signalling prevents such hypoxic responses . We have shown that NO/cGMP signalling inhibits hypoxia-induced malignant phenotypes likely in part by interfering with HIF-1α accumulation via a mechanism involving calpain . These findings indicate that activation of NO/cGMP signalling may have useful applications in cancer therapy.
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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) | 0.000 | 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".