First-in-class Microbial Ecosystem Therapeutic 4 (MET4) in combination with immune checkpoint inhibitors in patients with advanced solid tumors (MET4-IO trial)
Bibliographic record
Abstract
BACKGROUND: The intestinal microbiome has been associated with response to immune checkpoint inhibitors (ICIs) in humans and causally implicated in ICI responsiveness in animal models. Two recent human trials demonstrated that fecal microbiota transplant (FMT) from ICI responders can rescue ICI responses in refractory melanoma, but FMT has specific limitations to scaled use. PATIENTS AND METHODS: We conducted an early-phase clinical trial of a cultivated, orally delivered 30-species microbial consortium (Microbial Ecosystem Therapeutic 4, MET4) designed for co-administration with ICIs as an alternative to FMT and assessed safety, tolerability and ecological responses in patients with advanced solid tumors. RESULTS: The trial achieved its primary safety and tolerability outcomes. There were no statistically significant differences in the primary ecological outcomes; however, differences in MET4 species relative abundance were evident after randomization that varied by patient and species. Increases in the relative abundance of several MET4 taxa, including Enterococcus and Bifidobacterium, taxa previously associated with ICI responsiveness, were observed and MET4 engraftment was associated with decreases in plasma and stool primary bile acids. CONCLUSIONS: This trial is the first report of the use of a microbial consortium as an alternative to FMT in advanced cancer patients receiving ICI and the results justify the further development of microbial consortia as a therapeutic co-intervention for ICI treatment in cancer.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".