Antibiotic Exposure Does Not Impact Anti-BRAF/Anti-MEK Targeted Therapy Outcome in Patients with Advanced Melanoma
Bibliographic record
Abstract
The gut microbiome is an established predictor of response to immune checkpoint inhibitors (ICI) in melanoma, and antibiotic exposure prior to ICI initiation is a validated negative prognostic factor. About half of melanoma patients harbor BRAF mutations and are treated with BRAF/MEK inhibitors (BRAFi/MEKi). While the detrimental impact of antibiotics is well described in the ICI setting, their effect on BRAFi/MEKi efficacy remains unknown. We retrospectively analyzed 49 advanced BRAF-mutant melanoma patients treated with BRAFi/MEKi. Antibiotic-exposed patients were compared with non-exposed patients across three time windows: within 30, 60, or 90 days before and after therapy initiation. Outcomes included progression-free survival (PFS), overall survival (OS), and overall response rate (ORR). Among the cohort, 41% had antibiotic exposure within ±30 days, 53% within ±60 days, and 57% within ±90 days. Baseline characteristics were comparable between groups, except for worse ECOG scores in antibiotic-exposed patients. Across all windows, ORR, PFS, and OS were comparable between groups. Unlike what was observed in the ICI setting, antibiotic use did not negatively affect outcomes with BRAFi/MEKi. Despite small sample size, these findings suggest that the detrimental prognostic impact of antibiotics is specific to immunotherapy, highlighting the importance of evaluating the microbiome as a predictive biomarker across treatment contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".