EVALUATING HUMAN MICROBIOME SIGNATURES ASSOCIATED WITH DEVELOPMENT, OUTCOME, AND TREATMENT RESPONSE IN GLIOMA AND BRAIN METASTASIS: A SCOPING REVIEW
Bibliographic record
Abstract
Abstract The human microbiome is made up of over 500 species of bacteria in the body. Microbiome dysbiosis has been implicated in cancer development and treatment response, including in both primary brain tumors and brain metastases, by acting through the gut-brain axis. We conducted a scoping review of the current evidence surrounding the human microbiome and brain tumors. METHODS: A systematic search of relevant studies and abstracts from 5 electronic databases from 1946-2023 was conducted based on a search strategy developed by an information specialist. Eligible studies included human, in vivo, or in vitro studies that focused on the relationship between microbiome and glioma and brain metastasis development, response to therapies, and outcomes. RESULTS: Of 383 citations, 31 studies met inclusion criteria, including 15 articles and 16 conference abstracts. There were 19 human studies and 18 mouse studies, of which 24 studies focused on glioma and 7 studies focused on brain metastases. They either characterized the microbiome in patients with brain tumors (n=19) or its correlation to systemic therapies for glioma (n=12). Changes in the Firmicutes/Bacteroidetes ratio, which is a marker of dysbiosis in patients with brain tumors was observed in 7 studies; however, the direction of change varied. Microbiome dysbiosis seemed to occur in response to both immunotherapy (5 studies) and temozolomide (3 studies), with variable impacts on treatment response. CONCLUSION: Evidence surrounding the impact of the gut-brain axis on brain tumors remains in its infancy with a large amount of heterogeneity.
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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".