Microbial Ecosystem Therapeutics 4 (MET4) elicits treatment-specific IgG responses associated with changes in gut microbiota in immune checkpoint inhibitor recipients with advanced solid tumors
Bibliographic record
Abstract
Background Gut microbiome modulation has shown promise in its potential to treat cancer in combination with immunotherapy. Mechanistically, the pathways and routes by which gut microbiota may influence systemic and antitumor immunity remain uncertain. Here, we used blood and stool samples from Microbial Ecosystem Therapeutic 4 (MET4)-IO, an early-phase trial testing the safety and engraftment of the MET4 bacterial consortium in immune checkpoint inhibitor recipients, to assess how MET4 may affect systemic immunity. Methods Circulating antibody responses induced by MET4 were assessed using an antimicrobial antibody flow cytometry assay on pretreatment and post-treatment plasma. Antibody responses were associated with taxonomic changes in stool identified by metagenomic sequencing. Mass cytometry was performed on peripheral blood mononuclear cells to identify shifts in circulating immune subsets associated with antibody responses. Results Increases in circulating anti-MET4 immunoglobulin G (IgG) responses were measured by flow cytometry post-consortium treatment in MET4 recipients, but not untreated control participants, with five individuals displaying notably higher antibody responses. Stronger IgG responses were associated with greater increases in multiple taxa, including MET4 microbe Collinsella aerofaciens , which was previously linked with immune checkpoint response. However, these taxa were not enriched in the IgG-bound fraction post-MET4 treatment. Greater increases in circulating B cells and FoxP3 + CD4 + T cells post-MET4 treatment were observed in the blood of high IgG responders, while CD14 + and CD16 + monocyte populations were decreased in these individuals. Conclusion These results demonstrate the induction of treatment-specific circulating humoral immunity by a bacterial consortium and suggest potential mechanisms by which gut microbes may contribute to antitumor immunity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".