Functional role of the cancer microbiome in the solid tumour niche
Bibliographic record
Abstract
The importance of gut microbiome to cancer therapy and response cannot be overstated, however the contribution of the bacterial population to the local solid tumour ecosystem is often overlooked. Seminal studies of tumour-resident microbiomes have shown that relative abundances of specific bacteria in the tumour correlate with survival metrics, implicating the microbiome in patient outcome. Similarly, patterns of microbiome community shifts between tumour-bearing and unaffected organs suggests a role for the tumour microbiome niche in contributing to tumour biology and behaviour. Recent reports of the detection of bacteria in solid tumours of diverse human organs have provided a strong rationale for deciphering the role of the solid-tumour microbiome across all human-host anatomic and physiologic niches, as the microbiome is ubiquitously present throughout the human body. Here, we review the role of the human microbiome in mediating response to therapies, as well as the differences between tumour and non-malignant-resident communities. We discuss the ability of the tumour microbiome to interact with the host, thereby influencing host cell behaviour and cancer-associated processes. Further, we evaluate recent technological advances that allow us to actively quantify these populations and the relationships between cell types. Finally, we suggest how these dynamic interactions can be harnessed for therapeutic benefit in the treatment of cancer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".