Microbiota-driven antitumour immunity mediated by dendritic cell migration
Bibliographic record
Abstract
Gut microbiota influence the antitumour efficacy of immune checkpoint blockade1–6, but the mechanisms of action have not been fully elucidated. Here, we show that a new strain of the bacterial genus Hominenteromicrobium (designated YB328) isolated from the faeces of patients who responded to programmed cell death 1 (PD-1) blockade augmented antitumour responses in mice. YB328 activated tumour-specific CD8+ T cells through the stimulation of CD103+CD11b− conventional dendritic cells (cDCs), which, following exposure in the gut, migrated to the tumour microenvironment. Mice showed improved antitumour efficacy of PD-1 blockade when treated with faecal transplants from non-responder patients supplemented with YB238. This result suggests that YB328 could function in a dominant manner. YB328-activated CD103+CD11b− cDCs showed prolonged engagement with tumour-specific CD8+ T cells and promoted PD-1 expression in these cells. Moreover, YB238-augmented antitumour efficacy of PD-1 blockade treatment was observed in multiple mouse models of cancer. Patients with elevated YB328 abundance had increased infiltration of CD103+CD11b− cDCs in tumours and had a favourable response to PD-1 blockade therapy in various cancer types. We propose that gut microbiota enhance antitumour immunity by accelerating the maturation and migration of CD103+CD11b− cDCs to increase the number of CD8+ T cells that respond to diverse tumour antigens. A newly identified bacterial strain (YB328) isolated from the faeces of patients who responded to immune checkpoint blockade therapies can promote antitumour immunity through the activation of tumour-specific CD8+ T cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".