Low-Dose Paclitaxel Prior to Intratumoral Dendritic Cell Vaccine Modulates Intratumoral Cytokine Network and Lung Cancer Growth
Bibliographic record
Abstract
PURPOSE: The main goal of this study was to provide the "proof-of-principle" that low-dose paclitaxel is able to change the tumor microenvironment and improve the outcome of intratumoral dendritic cell vaccine in a murine lung cancer model. EXPERIMENTAL DESIGN: We evaluated the antitumor potential and changes in the intratumoral milieu of a combination of low-dose chemotherapy and dendritic cell vaccine in the Lewis lung carcinoma model in vivo. RESULTS: The low-dose paclitaxel, which induced apoptosis in approximately 10% of tumor cells, was not toxic to bone marrow cells and dendritic cells and stimulated dendritic cell maturation and function in vitro. Although tumor cells inhibited dendritic cell differentiation in vitro, this immunosuppressive effect was abrogated by the pretreatment of tumor cells with low-dose paclitaxel. Based on these data, we next tested whether pretreatment of tumor-bearing mice with low-dose paclitaxel in vivo would improve the antitumor potential of dendritic cell vaccine administered intratumorally. Significant inhibition of tumor growth in mice treated with low-dose paclitaxel plus intratumoral dendritic cell vaccine, associated with increased tumor infiltration by CD4(+) and CD8(+) T cells and elevated tumor-specific IFN-gamma production by draining lymph node cells, was revealed. Using a novel intratumoral microdialysis technique and Luminex technology for collecting and characterizing soluble factors released within the tumor bed for several days in live freely moving animals, we showed that low-dose paclitaxel altered the cytokine network at the tumor site. CONCLUSIONS: Our data indicate that low-dose chemotherapy before intratumoral delivery of dendritic cells might be associated with beneficial alterations of the intratumoral microenvironment and thus support antitumor immunity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".