Antibiotics are associated with decreased progression-free survival of advanced melanoma patients treated with immune checkpoint inhibitors
Bibliographic record
Abstract
Background: The gut microbiota has been shown to be an important determinant of the efficacy of immune checkpoint inhibitions (ICI) in cancer. Several lines of evidence suggest that antibiotic (ATB) usage prior to or within the first month of ICI initiation negatively impacts clinical outcomes.Methods: We examined patients with advanced melanoma treated with an anti-PD-1 monoclonal antibody (mAb) or an anti-CTLA-4 mAb alone or in combination with chemotherapy. Those receiving ATB within 30 days of beginning ICI were compared with those who did not receive ATB. Response rates as determined by RECIST 1.1, progression-free survival (PFS), overall survival (OS) and immune-related toxicities were assessed.Results: Of these 74 patients analyzed, a total of 10 patients received ATB (13.5%) within 30 days of initiation of ICI. Patients who received ATB 30 days prior to the administration of ICI experienced more primary resistance (progressive disease) (0% of the objective response rate compared to 34%), and progression-free survival (PFS) was significantly shorter (2.4 vs 7.3 months, HR 0.28, 95% CI (0.10–0.76) p = 0.01). Overall survival (OS) was also shorter; however, this was not statistically significant (10.7 vs 18.3 months, HR:0.52, 95% CI (0.21–1.32) p = 0.17). The multivariate analysis further supported that ATB administration was associated with worse PFS (HR 0.32 (0.13–0.83) 95% CI, p = 0.02).Conclusion: These findings suggest that ATB use within 30 days prior to ICI initiation in patients with advanced melanoma may adversely affect patient outcomes.
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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 | 0.002 |
| 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 | 0.001 |
| 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".