Abstract 6694: Gut microbial drivers of response to bladder-sparing therapy in human muscle-invasive bladder cancer enhance immunotherapy efficacy and limit immunosuppression following fecal microbiome transplantation <i>in vivo</i>
Bibliographic record
Abstract
Abstract BACKGROUND: Radiation therapy (RT) is a bladder-sparing option for Muscle-Invasive Bladder Cancer (MIBC), yet encounters a 30% non-response rate, with half of the patients succumbing to metastasis. Preclinical models demonstrate enhanced antitumor responses with RT combined with PD-1/PD-L1 blockade but give limited insight into the determinants of success for such combination therapies (CT). The gut microbiome, pivotal in conditioning local and peripheral immunity, influences PD-1-based immunotherapy efficacy in various cancers. Immune profiling indicates heightened systemic and antitumor immunity in responders with a favorable gut microbiome, enriched in Akkermansia muciniphila, Bifidobacterium, and Faecalibacterium. Oral gavage of immunogenic bacteria further enhances combined RT and anti-PD-L1 therapy. We thus aim to 1) elucidate the role of patients’ microbiome in shaping anti-tumor immune responses to CT, and 2) use its composition as a predictive factor for CT success. METHODS: We performed shotgun metagenomics on microbial DNA from fecal materials of MIBC RT responders (R, n=26) and non-responders (NR, n=11). Fecal homogenates from donors were gavaged into germ-free (GF) mice. Three weeks post-gavage, MB49 tumor cells were subcutaneously delivered to mice, randomized into four groups: control, anti-PD-L1, RT, and RT + anti-PD-L1. Tumors were harvested seven days post-treatment for single-cell RNA-seq and TCR sequencing (10X Genomics), while stool samples were collected weekly for 16S sequencing. RESULTS: Significant gut microbial disparities were observed between R and NR MIBC patients, with particular enrichment in known promoters of anti-PD-L1 response, such as Phocaeicola dorei in R. Transplantation of R microbiome into GF mice resulted in significant increases in predicted survival, tumor growth delay, and counts of effector CD4+ T cell infiltration in the RT+PD-L1 arm. Conversely, NR microbiome transplantation correlated with increased neutrophil and Treg infiltration, abrogating the survival tumor growth delay benefits of the combination arm. Spatial tissue analyses revealed higher neutrophil densities around CD8+ T cells in all NR arms. Single-cell RNA-seq of tumor-infiltrating immune cells unveiled skewed CD4 and CD8 polarization between R and NR in vivo batches. SIGNIFICANCE: To our knowledge, this is the first study to use FMT as a modulator of response in the context of radiation therapy combinations in MIBC. Our results highlight that the gut microbiome alone may condition a favorable immune terrain for combinatorial therapies involving radiation. This offers strong predictive value for personalized therapeutic approaches in selecting patients who will benefit the most from bladder-sparing therapies. Citation Format: Eva Michaud, Sabina Fehric, Cynthia Faubert, Bertrand Routy, Irah King, José Joao Mansure, Wassim Kassouf. Gut microbial drivers of response to bladder-sparing therapy in human muscle-invasive bladder cancer enhance immunotherapy efficacy and limit immunosuppression following fecal microbiome transplantation in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6694.
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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".