Sexual dimorphism in outcomes of non-muscle invasive bladder cancer: a role of CD163+ M2 macrophages, B cells and PD-L1 immune checkpoint
Bibliographic record
Abstract
Abstract Non-muscle invasive bladder cancer (NMIBC) is significantly more common in men than women. However, female patients with NMIBC often present with more aggressive disease and do not respond as well to immunotherapy treatments. We hypothesized that sexual dimorphism in the tumor immune microenvironment (TIME) may contribute to the inferior clinical outcomes observed in female patients. To test this hypothesis, we interrogated the expression patterns of genes associated with specific immune cell types and immune regulatory pathways using tumor whole transcriptome profiles from male (n=357) and female (n=103) patients with NMIBC. High-grade tumors from female patients exhibited significantly increased expression of CD40, CTLA4, PDCD1, LAG3 and ICOS immune checkpoint genes. Based on the significant differences in expression profiles of these genes and the cell types that most commonly express these in the TIME, we evaluated the density and spatial distribution of CD8+Ki67+ (activated cytotoxic T cells), FoxP3+ (regulatory T cells), CD103+ (tissue resident T cells), CD163+ (M2-like tumor associated macrophages), CD79a+ (B cells), PD-L1+ (Programmed-Death Ligand-1) and PD-1+ cells using multiplexed immunofluorescence in an independent cohort of 332 patient tumors on a tissue microarray (n=259 males and n=73 females). Tumors from female patients showed significantly higher infiltration of CD163+ macrophages and PD-L1+ cells compared to tumors from male patients. Notably, increased infiltration of CD163+ macrophages and CD79a+ B cells independently associated with decreased recurrence free survival. Not only do these results have the potential to inform the rational utilization of immunomodulatory therapies based on the TIME of both male and female patients with NMIBC, these novel findings highlight the necessity of considering sexual dimorphism in the design of future immunotherapy trials.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".