Association of Antibiotics and Other Drugs with Clinical Outcomes in Metastatic Melanoma Patients Treated with Immunotherapy
Bibliographic record
Abstract
Immune checkpoint inhibitors (ICIs) targeting the programmed cell death protein-1 (PD-1) and programmed cell death ligand-1 (PD-L1) have improved survival in many advanced cancers including advanced melanoma, renal cell, urothelial, and non-small-cell lung cancers. However, not all patients respond, and immune-related adverse events (irAEs) are common. Commensal gut bacteria may serve as an immunoregulatory link-mediating ICI response and toxicity. Recent studies have shown that a lack of bacterial diversity, known as gut dysbiosis, can have an adverse impact on patients' response to ICIs and predispose to the development of irAEs. Data were collected from 167 patients with metastatic melanoma who received antibiotics within 30 days prior to and/or after initiation of ICI and patients who received NSAIDs, statins, steroids, or proton-pump inhibitors (PPI) within 30 days prior to ICI initiation. The primary outcome was time-to-discontinuation (TTD) of ICI therapy, measured from the date of ICI initiation to the last treatment date. The secondary outcome of interest was toxicity, with incidence of irAEs graded as per the Common Terminology Criteria for Adverse Events (CTCAE), version 5.0. Here, we demonstrate that individuals who received antibiotics had a significantly shorter time-to-discontinuation (TTD) of the ICI therapy as opposed those who were not administered antibiotics. Consistent with results from previous research, we propose that antibiotics have a negative effect on a patient's response to ICI therapy, most likely due to the result of gut dysbiosis, and should be critically assessed in terms of their use in patients undergoing ICI treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".