Microbiota-Centered Interventions: The Next Breakthrough in Immuno-Oncology?
Bibliographic record
Abstract
The cancer-immune dialogue subject to immuno-oncological intervention is profoundly influenced by microenvironmental factors. Indeed, the mucosal microbiota-and more specifically, the intestinal ecosystem-influences the tone of anticancer immune responses and the clinical benefit of immunotherapy. Antibiotics blunt the efficacy of immune checkpoint inhibitors (ICI), and fecal microbial transplantation may restore responsiveness of ICI-resistant melanoma. Here, we review the yin and yang of intestinal bacteria at the crossroads between the intestinal barrier, metabolism, and local or systemic immune responses during anticancer therapies. We discuss diagnostic tools to identify gut dysbiosis and the future prospects of microbiota-based therapeutic interventions. SIGNIFICANCE: Given the recent proof of concept of the potential efficacy of fecal microbial transplantation in patients with melanoma primarily resistant to PD-1 blockade, it is timely to discuss how and why antibiotics compromise the efficacy of cancer immunotherapy, describe the balance between beneficial and harmful microbial species in play during therapies, and introduce the potential for microbiota-centered interventions for the future of immuno-oncology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".