Attenuation of the Glucocorticoid Response during Ad5IL-12 Adenovirus Vector Treatment Enhances Natural Killer Cell–Mediated Killing of MHC Class I–Negative LNCaP Prostate Tumors
Bibliographic record
Abstract
Tumor cells can evolve to evade immune responses by down-modulating surface MHC class I expression and become refractory to T cell-directed immunotherapy. We employed a strategy to bypass this escape mechanism using a recombinant adenovirus vector expressing interleukin-12 (Ad5IL-12) to target natural killer (NK) cell-mediated killing of human prostate tumors in NOD.scid mice. Fluorescence-activated cell sorting analysis revealed that LNCaP tumor cells bear negligible levels of MHC class I molecules; yet, they express MICA/B molecules, ligands for the NKG2D receptors found on NK cells. Transduction of LNCaP cells with the Ad5IL-12 vector prevented tumor formation in NOD.scid mice, indicating that NK cells alone can conduct tumor immunosurveillance and mediate protection. Intratumor injection of the Ad5IL-12 vector to established LNCaP tumors in NOD.scid mice resulted in a significant delay of tumor growth mediated by NK cell killing activity. The dependency of NK cells in this protective response was shown by the complete loss of Ad5IL-12 therapeutic efficacy on LNCaP tumors established in NOD.Cg-Rag1(tm1Mom)Prf1(tm1Sdz) congenic mice, which are devoid of NK cell activity. More pronounced attenuation of tumor growth and enhanced NK killing activity was observed when pharmacologic adrenalectomy with mitotane was done in combination with Ad5IL-12 vector treatment. The Ad5IL-12 vector treatment also induced killing of MICA/B-negative MHC class I-positive PC3 tumors formed in NOD.scid mice. Together, these results indicate that a targeted NK cell response could provide a generic approach for cancer immunotherapy, and that enhancing the NK cell response via control of cortisol levels may provide an additional therapeutic avenue in cancer.
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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".