Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer
Bibliographic record
Abstract
Background: The composition of gut microbiota affects antitumor immune responses, preclinical and clinical outcome following immune checkpoint inhibitors (ICI) in cancer. Antibiotics (ATB) alter gut microbiota diversity and composition leading to dysbiosis, which may affect effectiveness of ICI. Patients and methods: We examined patients with advanced renal cell carcinoma (RCC) and non-small-cell lung cancer (NSCLC) treated with anti-programmed cell death ligand-1 mAb monotherapy or combination at two academic institutions. Those receiving ATB within 30 days of beginning ICI were compared with those who did not. Objective response, progression-free survival (PFS) determined by RECIST1.1 and overall survival (OS) were assessed. Results: Sixteen of 121 (13%) RCC patients and 48 of 239 (20%) NSCLC patients received ATB. The most common ATB were β-lactam or quinolones for pneumonia or urinary tract infections. In RCC patients, ATB compared with no ATB was associated with increased risk of primary progressive disease (PD) (75% versus 22%, P < 0.01), shorter PFS [median 1.9 versus 7.4 months, hazard ratio (HR) 3.1, 95% confidence interval (CI) 1.4-6.9, P < 0.01], and shorter OS (median 17.3 versus 30.6 months, HR 3.5, 95% CI 1.1-10.8, P = 0.03). In NSCLC patients, ATB was associated with similar rates of primary PD (52% versus 43%, P = 0.26) but decreased PFS (median 1.9 versus 3.8 months, HR 1.5, 95% CI 1.0-2.2, P = 0.03) and OS (median 7.9 versus 24.6 months, HR 4.4, 95% CI 2.6-7.7, P < 0.01). In multivariate analyses, the impact of ATB remained significant for PFS in RCC and for OS in NSCLC. Conclusion: ATB were associated with reduced clinical benefit from ICI in RCC and NSCLC. Modulatation of ATB-related dysbiosis and gut microbiota composition may be a strategy to improve clinical outcomes with ICI.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".