Understanding the relationship between breast cancer, immune checkpoint inhibitors, and gut microbiota: a narrative review
Bibliographic record
Abstract
Background and Objective: The composition of gut microbiota plays an important role in predicting and influencing outcomes of cancer treated with immunotherapy. Our objective is to summarize the role of gut microbiota and immunotherapy in breast cancer. Methods: A systematic search from inception until July 2024 of key search terms including immunity, breast neoplasm, gastrointestinal microbiome/microbiota, fecal microbiota transplantation, pro- and prebiotics, antibiotics and immunotherapy using EMBASE, MEDLINE and CENTRAL was conducted. The results were screened by two reviewers independently and synthesized and presented descriptively. Key Content and Findings: Thirteen studies (5 clinical, 8 pre-clinical) met the eligibility criteria and were published from 2020-2024. Clinical studies showed that the composition and diversity of gut microbiota was associated with patient response to immunotherapy. In pre-clinical studies, dysbiotic states induced by obesity, antibiotics, and diet were associated with immunosuppression and influenced response to programmed cell death-ligand 1 (PD-L1) inhibitors. Microbiota-modulating treatments such as probiotics showed the ability to enhance response to immunotherapy, indicating their potential use as adjunct therapies in breast cancer treatment. Conclusions: The composition of gut microbiota could help predict the chance of response to immunotherapy, and modulating gut microbiota has the potential to enhance the efficacy of chemo-immunotherapy in breast cancer. However, the available data relating to breast cancer are limited. Larger prospective studies are required to further elucidate their role as a biomarker and treatment.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".