Treatment combining RU486 and Ad5IL-12 vector attenuates the growth of experimentally formed prostate tumors and induces changes in the sentinel lymph nodes of mice
Bibliographic record
Abstract
BACKGROUND: Tumor immune responses are first generated and metastases often begin in tumor sentinel lymph nodes (TSLN). Therefore, it is important to promote tumor immunity within this microenvironment. Mifepristone (RU486) treatment can interfere with cortisol signaling that can lead to suppression of tumor immunity. Here, we assessed whether treatment with RU486 in conjunction with an intratumor injection of Ad5IL-12 vector (a recombinant adenovirus expressing IL-12) could impact the TSLN microenvironment and prostate cancer progression. METHODS: The human PC3, LNCaP or murine TRAMP-C1 prostate cancer cell lines were used to generate subcutaneous tumors in NOD.scid and C57BL/6 mice, respectively. Adjuvant effects of RU486 were looked for in combination therapy with intratumor injections (IT) of Ad5IL-12 vector in comparison to PBS, DL70-3 vector, DL70-3 + RU486, RU486 and Ad5IL-12 vector treatment controls. Changes in tumor growth, cell cytotoxic activity and populations of CD4+/FoxP3+ T regulatory cells (Treg) in the TSLN were evaluated. RESULTS: Treatment of human PC3 prostate xenograft or TRAMP-C1 tumors with combination Ad5IL-12 vector and RU486 produced significantly better therapeutic efficacy in comparison to controls. In addition, we found that combination therapy increased the capacity of TSLN lymphocytes to produce Granzyme B in response to tumor cell targets. Finally, combination therapy tended towards decreases of CD4+/FoxP3+ T regulatory cell populations to be found in the TSLN. CONCLUSION: Inclusion of RU486 may serve as a useful adjuvant when combined with proinflammatory tumor killing agents by enhancement of the immune response and alteration of the TSLN microenvironment.
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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) | 0.001 | 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) | 0.002 | 0.001 |
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".