Gut microbiome to predict efficacy and immune-related toxicities in patients with advanced non-small cell lung cancer treated with anti-PD-1/PD-L1 antibody-based immunotherapy.
Bibliographic record
Abstract
3095 Background: The gut microbiome (GM) plays an important role in shaping systemic immune responses. Preclinical and clinical data suggest that GM influences anti-PD-1/PD-L1 or -CTLA-4 Antibody (Ab)-mediated anti-cancer responses. Furthermore, there is strong evidence that antibiotics (ATB) worsen clinical outcomes based on multiple retrospective and one prospective studies using immune checkpoint inhibitor (ICI). However, whether GM profiling, at baseline or post-ATB, could represent a biomarker of response in advanced non-small cell lung cancer (NSCLC) during ICI therapy remains unknown. Methods: We prospectively collected baseline (pre-ICI) fecal samples and clinical data Japanese patients (pts) with NSCLC treated with anti-PD-1/PD-L1 Abs in first or second-line therapy. We performed a 16S rRNA V3-V4 sequencing of gene amplicons of fecal microbes. Amplicon sequence variants were generated with dada2 R package. Diversity analysis was performed with phyloseq R. Differential abundance analysis was performed with both LEfSe and DESeq2 methods. Clinical endpoints were progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and immune-related adverse events (irAE). Results: 70 fecal samples were analyzed. Median OS and PFS in all patients were 16.1 and 5.2 months, respectively. 16 pts (23%) were exposed to ATB 1 month prior to ICI initiation. Pts on ATB had lower α-diversity at baseline and underrepresentation of Clostridiales and Ruminococcaceae UCG 13. When analyzing ATB-free pts, lower α-diversity was observed in non-responders. In addition, Ruminococcaceae UCG 13 was enriched in patients with OS > 12 months, favorable ORR, and PFS > 6 months. Clostridiales order was also enriched in patients with OS > 12 months. Compositional GM differences were also observed between the patients who experienced clinically significant (≥grade 2) irAE; Lactobacillaceae and Raoultella were enriched in pts who had no significant irAE. Conclusions: We demonstrated the negative influence of ATB on GM composition and identified differential bacteria repertoire in pts experiencing favorable clinical outcomes or low grade irAE. Our data pave the way to the development of diagnosis tools aimed at identifying gut dysbiosis to predict resistance or irAE during ICI for NSCLC.
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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 | 0.000 |
| 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".