Using ex vivo culture to assess dynamic phenotype changes in human prostate macrophages following exposure to therapeutic drugs
Bibliographic record
Abstract
Within the prostate tumor microenvironment (TME) there are complex multi-faceted and dynamic communication occurring between cancer cells and immune cells. Macrophages are key cells which infiltrate and surround tumor cells and are recognized to significantly contribute to tumor resistance and metastases. Our understanding of their function in the TME is commonly based on in vitro and in vivo models, with limited research to confirm these model observations in human prostates. Macrophage infiltration was evaluated within the TME of human prostates after 72 h culture of fresh biopsies samples in the presence of control or enzalutamide. In addition to immunohistochemistry, an optimized protocol for multi-parametric evaluation of cellular surface markers was developed using flow cytometry. Flow cytometry parameters were compared to clinicopathological features. Immunohistochemistry staining for 19 patients with paired samples suggested enzalutamide increased the expression of CD163 relative to CD68 staining. Techniques to validate these results using flow cytometry of dissociated biopsies after 72 h of culture are described. In a second cohort of patients with Gleason grade group ≥ 3 prostate cancer, global macrophage expression of CD163 was unchanged with enzalutamide treatment. However, exploratory analyses of our results using multi-parametric flow cytometry for multiple immunosuppressive macrophage markers suggest subgroup changes as well as novel associations between circulating biomarkers like the neutrophil to lymphocyte ratio (NLR) and immune cell phenotype composition in the prostate TME. Further, we observed an association between B7-H3 expressing tumor-associated macrophages and the presence of intraductal carcinoma. The use of flow cytometry to evaluate ex vivo cultured prostate biopsies fills an important gap in our ability to understand the immune cell composition of the prostate TME. Our results highlight novel associations for further investigation.
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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".