Immunotherapy efficacy in colorectal cancer is dependent on activation of a microbial-metabolite-immune circuit
Bibliographic record
Abstract
Summary Cancer is a leading cause of death globally. Checkpoint blockade therapies offer a promising treatment for many cancers, but have been ineffective for colorectal cancers. Previous studies have shown a dependency of immunotherapies on the microbiota. Consequently, we hypothesized that specific gut bacteria promote immunotherapy for colorectal cancer. We identify three commensal bacteria and a microbial metabolite, inosine, that enhance the efficacy of immune checkpoint blockade therapy in colorectal cancer. We show that inosine interacts with the adenosine A 2A receptor on T cells resulting in intestinal Th1 cell differentiation. Decreased gut barrier function induced by immunotherapy increased the translocation of bacterial metabolites and promoted cancer protective Th1 cell activation. This microbial-metabolite-immune circuit provides a mechanism for a new class of bacteria-enhanced checkpoint blockade therapies. The efficacy of this mechanism differs among colorectal cancer subtypes and highlights the strengths as well as potential limitations of this novel bacterial co-therapy for 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) | 0.000 | 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".