Abstract 5915: Exploring the impact of microbiome in the response of combined radiation with immune checkpoint blockade in muscle invasive bladder cancer (MIBC)
Bibliographic record
Abstract
Abstract BACKGROUND: Radiation therapy is a promising bladder-sparing option for MIBC treatment. Yet, 30% of patients do not respond and of those, half will later die of metastasis. Combining radiation therapy with immunotherapy (CT) thus presents a worthy candidate to fulfill this unmet need. In preclinical models, improved antitumor responses when RT is combined with PD-1/PD-L1 blockade have been described. Yet, there remains a flagrant lack of knowledge on the determinants of combination therapy success. In this context, the composition of the gut microbiome is critical in conditioning local and peripheral immunity. Recent metagenomic studies have shown the gut microbiome influences the efficacy of PD-1-based IT in epithelial tumors, melanoma, and lung cancer, to name a few. In addition, modification of the gut microbiome with oral gavage of immunogenic bacteria potentiates the activity of combined RT and anti-PD-L1 therapy. Furthermore, immune profiling shows enhanced systemic and antitumor immunity in responding patients with a favorable gut microbiome (such as enrichment in Akkermansia muciniphila, Bifidobacterium and Faecalibacterium). Therefore, we aim to document the role of patients’ microbiome in polarizing anti-tumor immune responses to CT in order to eventually use its composition as a predicting factor of CT success. METHODS: Fecal material from responder (R, n=3) and non-responder (NR, n=3) MIBC patients was gavaged into 20 germ-free (GF) mice in two administrations one week apart. 3 weeks after the last gavage, tumor cells (MB49) were delivered subcutaneaously. Once tumors reached 0.1-0.15cm3, mice were randomized into 4 groups: control; anti-PD-L1 alone; RT alone; RT + anti-PD-L1. 7 days after the start of treatment, tumors were harvested, dissociated and frozen as cell suspensions for single cell immune profiling and TCR sequencing (10X Genomics). Stool were collected weekly for 16S sequencing. Correlation networks from sequencing data were built (TransNet and Microbiome R packages) and visualized using Cytoscape to show interactions between the tumor immune microenvironment and the gut microbiome in R and NR-receiving mice. RESULTS: We show feasibility of the experimental design as well as robust engraftment of human FMT to germ-free mice in a bladder cancer tumor model. FMT from NR lessened the known beneficial effects of RT in the MB49 model compared to FMT from R. Transkingdom analysis of sc-RNA-seq and 16S show robust statistical interactions between immunosuppression and enrichment in microbes associated to poor outcome in immunotherapy. 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. Findings from this study have a strong predictive value that could be used to select patients who will benefit most from a personalized therapeutic approach. Citation Format: Eva Michaud, Cynthia Faubert, Jose Joao Mansure, Irah King, Wassim Kassouf. Exploring the impact of microbiome in the response of combined radiation with immune checkpoint blockade in muscle invasive bladder cancer (MIBC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5915.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".