Developing natural killer cells producing hypoxia-inducible cytokines for effective immunotherapy against solid tumors
Bibliographic record
Abstract
Abstract Natural Killer (NK) cells are a promising means for adoptive immunotherapy for cancers. NK cell therapies against hematological cancers such as acute myeloid leukemia have demonstrated good efficacy. However, the poor effector function of tumor-infiltrating NK cells limits the potential of NK cells against solid tumors. Accumulating data suggests that hypoxia in the tumor microenvironment (TME) suppresses the effector function of NK cells. One of the immunosuppressive features of hypoxia is the generation of adenosine, a metabolite that highly suppresses NK cell cytotoxicity and proliferation. Although engineering NK cells to overexpress stimulatory cytokines before the adoptive immunotherapy has demonstrated promising potential for cancer therapy, it raises a concern that high systemic levels of cytokines may lead to cytokine-mediated pathology. Therefore, we hypothesize that driving overexpression of the potent stimulatory cytokines exclusively in the TME would circumvent systemic toxicity. An attractive strategy to achieve this goal is to take advantage of the hypoxic condition, a key characteristic of the TME. Hypoxia-inducible factor 1 alpha (HIF1-α) plays a pivotal role in expressing hypoxia-responsive genes by associating with promoters containing hypoxia response element (HRE). By mimicking the biological system, HRE-induced cytokine expression vectors were generated and tested in NK cells. The expression was inducible under hypoxic conditions and was reversible upon re-oxygenated conditions. Our results suggest that the hypoxia-inducible vector system can be beneficial to trigger TME specific cytokine expressions in NK cells and provide insight into efficient immunotherapy against solid tumors.
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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 | 0.001 |
| 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".