Antibiotics are associated with worse outcomes in lung cancer patients treated with chemotherapy and immunotherapy
Bibliographic record
Abstract
Anti-PD(L)-1 inhibition combined with platinum doublet chemotherapy (Chemo-IO) has become the most frequently used standard of care regimen in patients with non-small cell lung cancer (NSCLC). The negative impact of antibiotics on clinical outcomes prior to anti-PD(L)-1 inhibition monotherapy (IO) has been demonstrated in multiple studies, but the impact of antibiotic exposure prior to initiation of Chemo-IO is controversial. We assessed antibiotic exposures at two time windows: within 60 days prior to therapy (-60 d window) and within 60 days prior to therapy and 42 days after therapy (-60 + 42d window) in 2028 patients with advanced NSCLC treated with Chemo-IO and IO monotherapy focusing on objective response rate (ORR: rate of partial response and complete response), progression-free survival (PFS), and overall survival (OS). We also assessed impact of antibiotic exposure in an independent cohort of 53 patients. Univariable and multivariable analyses were conducted along with a meta-analysis from similar studies. For the -60 d window, in the Chemo-IO group (N = 769), 183 (24%) patients received antibiotics. Antibiotic exposure was associated with worse ORR (27% vs 40%, p = 0.001), shorter PFS (3.9 months vs. 5.9 months, hazard ratio [HR] 1.35, 95%CI 1.1,1.6, p = 0.0012), as well as shorter OS (10 months vs. 15 months, HR 1.50, 95%CI 1.2,1.8, p = 0.00014). After adjusting for known prognostic factors in NSCLC, antibiotic exposure was independently associated with worse PFS (HR 1.39, 95%CI 1.35,1.7, p = 0.002) and OS (HR 1.61, 95%CI 1.28,2.03, p < 0.001). Similar results were obtained in the -60 + 42d window, and also in an independent cohort. In a meta-analysis of patients with NSCLC treated with Chemo-IO (N = 4) or IO monotherapy (N = 13 studies) antibiotic exposure before treatment was associated with worse OS among all patients (n = 11,351) (HR 1.93, 95% CI 1.52, 2.45) and Chemo-IO-treated patients (n = 1201) (HR 1.54, 95% CI 1.28, 1.84). Thus, antibiotics exposure prior to Chemo-IO is common and associated with worse outcomes, even after adjusting for other factors. These results highlight the need to implement antibiotic stewardship in routine oncology practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".