A probiotic bacterium modulates antitumor γδ T-cell responses in lung cancer
Bibliographic record
Abstract
ABSTRACT The link between the intestinal microflora and cancer outcomes has been recognized for over a decade. Several recent studies have demonstrated that the gut microbiome is associated with the efficiency of T-cell checkpoint blockade therapy for cancer raising interest in strategies to harness this effect via consumption of live microorganisms (probiotics). The probiotic Clostridium butyricum strain MIYAIRI 588 (CBM588) enhances both response rates and overall survival in patients receiving immune checkpoint blockade therapy for non-small cell lung cancer and metastatic renal cell carcinoma but the mechanism underpinning this benefit remains unclear. Here, we show that CBM588 spores induce a population of Vγ9Vδ2 T-cells from the peripheral blood of healthy donors and lung cancer patients. A subset of these T-cells responded to, and directly lysed, cancer cell lines via a butyrophilin 3A-dependant mechanism. In patients taking CBM588 alongside checkpoint blockade, peripheral Vδ2⁺ T-cells expressed the activation marker CD69 more frequently than those receiving checkpoint blockade alone and the frequency of Vδ2⁺CD69⁺ cells increased following initiation of CBM588 treatment (p = 0.0041). Our findings provide a potential mechanism by which the manipulation of the intestinal microflora might directly contribute to cancer prognosis by activating immune effector cells with intrinsic anticancer properties.
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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".