The Gut Microbiome Associates with Immune Checkpoint Inhibition Outcomes in Patients with Advanced Non–Small Cell Lung Cancer
Bibliographic record
Abstract
Abstract The gut microbiome (GM) plays an important role in shaping systemic immune responses and influences immune checkpoint inhibitor (ICI) efficacy. Antibiotics worsen clinical outcomes in patients receiving ICI. However, whether GM profiling and baseline antibiotic can be a biomarker of ICI efficacy in advanced non–small cell lung cancer (NSCLC) remains unknown. We prospectively collected baseline (pre-ICI) fecal samples and clinical data of 70 Japanese patients suffering from advanced NSCLC and treated them with anti–PD-1/PD-L1 antibodies as a first-line or treatment-refractory therapy. We performed 16S rRNA V3–V4 sequencing of gene amplicons of fecal samples, and bacteria diversity and differential abundance analysis was performed. The clinical endpoints were objective response rate (ORR), progression-free survival (PFS), overall survival (OS), and immune-related adverse events (irAE). ORR was 34%, and median PFS and OS were 5.2 and 16.2 months, respectively. Patients who received pre-ICI antibiotic had lower alpha diversity at baseline and underrepresentation of Ruminococcaceae UCG 13 and Agathobacter. When analyzing antibiotic-free patients, alpha diversity correlated with OS. In addition, Ruminococcaceae UCG 13 and Agathobacter were enriched in patients with favorable ORR and PFS >6 months. Ruminococcaceae UCG 13 was enriched in patients with OS >12 months. GM differences were observed between patients who experienced low- versus high-grade irAE. We demonstrated the negative influence of antibiotic on the GM composition and identified the bacteria repertoire in patients experiencing favorable responses to ICI. See articles by Tomita et al., p. 1236, and Peng et al., p. 1251
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".