A narrative review from gut to lungs: non-small cell lung cancer and the gastrointestinal microbiome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it