A human prostatic bacterial isolate alters the prostatic microenvironment and accelerates prostate cancer progression
Bibliographic record
Abstract
Inflammation is associated with several diseases of the prostate including benign enlargement and cancer, but a causal relationship has not been established. Our objective was to characterize the prostate inflammatory microenvironment after infection with a human prostate-derived bacterial strain and to determine the effect of inflammation on prostate cancer progression. To this end, we mimicked typical human prostate infection with retrograde urethral instillation of CP1, a human prostatic isolate of Escherichia coli. CP1 bacteria were tropic for the accessory sex glands and induced acute inflammation in the prostate and seminal vesicles, with chronic inflammation lasting at least 1 year. Compared to controls, infection induced both acute and chronic inflammation with epithelial hyperplasia, stromal hyperplasia, and inflammatory cell infiltrates. In areas of inflammation, epithelial proliferation and hyperplasia often persist, despite decreased expression of androgen receptor (AR). Inflammatory cells in the prostates of CP1-infected mice were characterized at 8 weeks post-infection by flow cytometry, which showed an increase in macrophages and lymphocytes, particularly Th17 cells. Inflammation was additionally assessed in the context of carcinogenesis. Multiplex cytokine profiles of inflamed prostates showed that distinct inflammatory cytokines were expressed during prostate inflammation and cancer, with a subset of cytokines synergistically increased during concurrent inflammation and cancer. Furthermore, CP1 infection in the Hi-Myc mouse model of prostate cancer accelerated the development of invasive prostate adenocarcinoma, with 70% more mice developing cancer by 4.5 months of age. This study provides direct evidence that prostate inflammation accelerates prostate cancer progression and gives insight into the microenvironment changes induced by inflammation that may accelerate tumour initiation or progression.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".