Increased stromal densities of B cells, CD103+ cells, and CD163+ M2-like macrophages associate with poor clinical outcomes in BCG treated non-muscle invasive bladder cancer
Bibliographic record
Abstract
Abstract Non-muscle invasive bladder cancer (NMIBC) constitutes a significant clinical challenge, with over 50% of patients experiencing poor clinical outcomes in the form of early recurrence or progression following treatment with Bacillus Calmette-Guerin (BCG) immunotherapy. The pre-treatment tumor immune microenvironment (TIME) is an established determinant of response to BCG. This study explores the spatial profiles of CD79a+ B cells, CD163+ M2-like macrophages, proliferating and tissue-resident phenotypes of T cells, along with PD-1/PD-L1 checkpoint expression in pre-BCG treatment tumors of 173 patients (139 males, 34 females). Multiplex immunofluorescence staining of a tumor tissue microarray, revealed elevated infiltration of CD79a+ B cells, CD163+ M2-like macrophages, CD103+ cells, and CD8+ T cells at the tumor invasive margins. Increased epithelial PD-L1 immune-checkpoint expression in tumors was observed in female and male patients who exhibited significantly shorter recurrence-free survival (RFS). Importantly, high CD79a+ B cell density in BCG-treated females in both stromal and epithelial compartments exhibited significantly shorter RFS and progression-free survival compared to males. Stromal CD79a+ B cell density was positively correlated with M2-like macrophages, CD8+ T cells, CD103+ cells and PD-1 expressing cells. CD79a+ B cells, CD103+ cells, and M2-like macrophage density were associated with higher grade and enriched in basal subtype tumor. This study highlights the significance of an understudied role of B cells and their cellular neighborhoods in the pre-treatment TIME and BCG-therapy response. Overall, findings from this study underscore the importance of considering sex-related immunobiological differences in the stromal compartments of bladder tumors towards the development of optimal therapeutic targeting strategies.
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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".