A narrative review from gut to lungs: non-small cell lung cancer and the gastrointestinal microbiome
Bibliographic record
Abstract
Background and Objective: The gut microbiome has emerged as an important gateway to improving therapeutic outcomes in lung cancer, especially for immunotherapy. Our objective is to review the impact of the bidirectional relationship between the gut microbiome, lung cancer, and the immune system, and to identify areas of future research. Methods: We conducted a search on PubMed, EMBASE, and ClinicalTrials.gov using the search terms non-small cell lung cancer (NSCLC), gut microbiome, and microbiota until July 11, 2022. The authors screened resulting studies independently. Results were synthesized and presented descriptively. Key Content and Findings: Sixty original published studies were identified from PubMed (n=24) and EMBASE (n=36), respectively. Twenty-five ongoing clinical studies were identified on ClinicalTrials.gov. Gut microbiota has been shown to influence tumorigenesis and modulate tumor immunity via local and neurohormonal mechanisms depending on the microbiome ecosystem that populates the gastrointestinal tract. Probiotics, antibiotics, and proton pump inhibitors (PPIs), amongst other medications, can impact gut microbiome health, leading either to improved or worsened therapeutic outcomes with immunotherapy. Most clinical studies assess the impact of the gut microbiome, but emerging data suggest microbiome composition in other host sites may be important. Conclusions: A strong relationship exists between gut microbiome, oncogenesis, and anticancer immunity. Although the underlying mechanisms are poorly understood, immunotherapy outcomes seem to depend on host-related factors such as gut microbiome alpha diversity, relative abundance of microbial genera/taxa, and extrinsic factors such as prior or concurrent exposure to probiotics, antibiotics, and other microbiome-modifying drugs.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 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) | 0.010 | 0.001 |
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".