Associations of Prostate Tumor Immune Landscape with Vigorous Physical Activity and Prostate Cancer Progression
Bibliographic record
Abstract
BACKGROUND: Vigorous physical activity has been associated with lower risk of fatal prostate cancer. However, mechanisms contributing to this relationship are not understood. METHODS: We studied 117 men with prostate cancer in the University of North Carolina Cancer Survivorship Cohort (UNC CSC) who underwent radical prostatectomy and 101 radiation-treated patients with prostate cancer in FASTMAN. Structured questionnaires administered in UNC CSC assessed physical activity. In both studies, digital image analysis of hematoxylin and eosin-stained tissues was applied to quantify tumor-infiltrating lymphocytes in segmented regions. NanoString gene expression profiling in UNC CSC and microarray in FASTMAN were performed on tumor tissue, and a 50-gene signature utilized to predict immune cell types. RESULTS: Vigorous recreational activity, reported by 34 (29.1%) UNC CSC men, was inversely associated with tumor-infiltrating lymphocyte abundance. Tumors of men reporting any vigorous activity versus none showed lower gene expression-predicted abundance of Th, exhausted CD4 T cells, and macrophages. T-cell subsets, including regulatory T cells, Th, Tfh, exhausted CD4 T cells, and macrophages, were associated with an increased risk of biochemical recurrence, only among men with ERG-positive tumors. CONCLUSIONS: Vigorous activity was associated with lower prostate tumor inflammation and immune microenvironment differences. Macrophages and T-cell subsets, including those with immunosuppressive roles and those with lower abundance in men reporting vigorous exercise, were associated with worse outcomes in ERG-positive prostate cancer. IMPACT: Our novel findings contribute to our understanding of the role of the tumor immune microenvironment in prostate cancer progression and may provide insights into how vigorous exercise could affect prostate tumor biology.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".