Abstract 6522: Sex differences in gut microbial composition during pelvic radiation therapy in experimental models of muscle-invasive bladder cancer
Bibliographic record
Abstract
Background: Radical cystectomy is the standard of care for muscle-invasive bladder cancer (MIBC) but significantly impacts patients’ quality of life. As not all patients are surgery-eligible, radiation therapy (RT) has emerged as a viable bladder-sparing approach. Yet, 25-30% of patients do not respond and will require salvage cystectomy. To add, while females are less at risk for bladder cancer, they tend to develop more aggressive disease phenotypes, and poorer response to surgery or RT. Determinants of radiation efficacy may reside outside the stole study of the tumor micro-environment (TME), as the gut microbiome, which is implicated in radiation-induced toxicity, may be influential in polarizing tumor responses to RT. Although RT effects on the microbiome have been previously studied, relationship between TME, local immune responses and gut microbes have yet to be described in MIBC. Methods: C57Bl/6 male (n=20) and female (n=20) mice were treated with 6x6Gy of RT to the bladder or left untreated (n=10 per group) then monitored for 4 weeks without tumor induction. We then generated a N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN) bladder tumor model where 3x8Gy radiation was administered to the bladder tumor at week 16 of BBN exposure. After RT was administered, stool samples were collected from all mice for 4 weeks for 16S rRNA-sequencing. Sequence data was converted to metabolic pathways using PiCRUST2. Formalin-fixed tumor tissues were stained by immunohistochemistry for B cell, T cell, macrophages and neutrophil markers. Results: RT-treated males associated with high abundance of Akkermansia, Bifidobacterium and Faecalibaculum, all promoters gut health and response to anti-cancer therapies in humans. In contrast, RT-treated females selectively clustered according to Roseburia, Romboutsia and Bifidobacterium abundance. Females appeared more resistant to RT-induced dysbiosis at baseline, a protection that is abrogated upon tumor induction. RT treatment following tumor induction led to increased Clostridium abundance in females but decreased abundance in males. To add, KEGG pathways analysis predicted increased proteasome and flavonoid synthesis activity in females receiving RT, suggesting higher rates of metabolic conversion in females compared to males. Lastly, tumor immune infiltrates varied in composition and quantity according to both sex and treatment, underlining the sex-specific impacts of RT on both the microbiome and TME. Conclusion: Our results indicate pelvic RT does affect microbiome composition with significant dysbiosis in males compared to females. Differential composition in both sexes upon tumor induction suggest RT does not affect males and female microbiomes similarly, which may condition distinct tumor immune responses. This is the first study to link sex, microbiome and RT in experimental models of MIBC to our knowledge. Citation Format: Éva Michaud, Matthew Stendel, José Joao Mansure, Fabio Cury, Wassim Kassouf. Sex differences in gut microbial composition during pelvic radiation therapy in experimental models of muscle-invasive bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6522.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".