M2 macrophages, B cells, and PD-L1 immune checkpoint protein exhibit sexual dimorphism in the outcomes of non-muscle invasive bladder cancer
Bibliographic record
Abstract
Abstract Non-muscle-invasive bladder cancer (NMIBC) is more than three times as common in men as it is in women. However, female patients with NMIBC do not respond as well to immunotherapeutic treatments and experience worse clinical outcomes than their male counterparts. The underlying causes of these discrepancies have yet to be fully elucidated. We hypothesized that sexual dimorphism in the tumor immune microenvironment (TIME) may contribute to the inferior clinical outcomes observed in female patients with NMIBC. To test this hypothesis, we investigated immune-associated gene expression in tumors from male (n=357) and female (n=103) patients. High-grade tumors from female patients exhibited significantly increased expression of B cell-associated genes CD40 and CXCL13, and the immune checkpoint genes CTLA4, PDCD1, LAG3, and ICOS. Based on these differences, we utilized multiplexed immunofluorescence to evaluate the density and spatial distribution of 12 immune cell markers (CD79a, CD3, CD8, FoxP3, Ki67, CD103, CD163, GATA3, CK5, IDO1, PD-1, and PD-L1) in tumors from an independent cohort of 332 patients with NMIBC (n=259 males and n=73 females). Tumors from female patients showed significantly higher infiltration of PD-L1+ cells and CD163+ M2-like macrophages compared to tumors from male patients. Notably, increased abundance of CD163+ macrophages and CD79a+ B cells were independently associated with decreased recurrence-free survival. This study has the potential to inform the rational utilization of immunomodulatory treatments for NMIBC based on the TIME of both male and female patients. Furthermore, 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) | 0.001 | 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".