Hypoxia Induces Escape from Innate Immunity in Cancer Cells via Increased Expression of ADAM10: Role of Nitric Oxide
Bibliographic record
Abstract
One key to malignant progression is the acquired ability of tumor cells to escape immune-mediated lysis. Whereas tumor hypoxia is known to play a causal role in cancer metastasis and resistance to therapy, the link between hypoxia and immune escape in cancer remains poorly understood. Here, we show that hypoxia induces tumor cell resistance to lysis mediated by immune effectors and that this resistance to lysis occurs via a hypoxia-inducible factor-1 (HIF-1)-dependent pathway linked to increased expression of the metalloproteinase ADAM10. This enzyme is required for the hypoxia-induced shedding of MHC class I chain-related molecule A (MICA), a ligand that triggers the cytolytic action of immune effectors, from the surface of tumor cells. Indeed, our findings show a mechanistic link between hypoxia-induced accumulation of the α-subunit of HIF-1 (HIF-1α), increased expression of ADAM10, and decreased surface MICA levels leading to tumor cell resistance to lysis mediated by innate immune effectors. Nitric oxide mimetic agents interfered with the hypoxia-induced accumulation of HIF-1α and with the hypoxia-induced upregulation of ADAM10 expression required for decreased surface MICA expression and resistance to lysis. Furthermore, treatment of tumor-bearing mice with nitroglycerin, a nitric oxide mimetic, attenuated tumor growth by a mechanism that relied upon innate immune effector cells. Together, these findings reveal a novel mechanism by which the hypoxic tumor microenvironment contributes to immune escape in cancer, lending support to potential immunotherapeutic strategies involving the use of nitric oxide 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".