The impact of antibiotic (Ab) exposure on clinical outcomes in patients with metastatic renal cell carcinoma (mRCC) treated with immune checkpoint inhibitors (ICI) or VEGF targeted therapy (VEGF-TT).
Bibliographic record
Abstract
4552 Background: Retrospective studies have shown an association between Ab exposure and inferior clinical outcomes in patients receiving ICI across various tumor types, including mRCC. However, it is unclear whether Ab exposure has a unique interaction with ICI or is an independent prognostic marker, regardless of treatment. We sought to examine Ab exposure and its association with clinical outcomes in patients with mRCC treated with ICI compared to VEGF-TT. Methods: We identified patients treated with ICI (anti-PD-L1 alone or in combination with VEGF or CTLA4 inhibitor) or VEGF-TT alone in first to fourth line settings from 2009-2020 across 3 academic centers in North America. Ab exposure was defined as administration of Ab within 60 days prior to initiation of systemic therapy. Outcomes of interest were response rate (RR), time to treatment failure (TTF) and overall survival (OS). Multivariable Cox regression was performed to control for imbalances in International mRCC Database Consortium (IMDC) risk factors, histology, and treatment line. Results: We identified 748 patients. Among the ICI (n=427) and VEGF-TT (n=321) cohorts, 13% vs 15% (p=0.47) had Ab exposure and 57% vs 48% (p=0.046) were treated in the first line setting. The proportion of favorable, intermediate, and poor risk disease by IMDC criteria differed between Ab exposed and unexposed patients in the ICI (14% vs 18%, 47% vs 62%, 39% vs 21% p=0.03) and VEGF-TT (7% vs 13%, 43% vs 60%, 50% vs 27%, p=0.01) cohorts. RR, TTF and OS results are displayed in Table 1. Multivariable analysis did not show a significant independent association between Ab exposure and OS in both the ICI (HR 1.13, p=0.62) and VEGF-TT (HR 1.32, p=0.16) cohorts. Treatment modality (ICI vs VEGF-TT) did not modify the effect of Ab exposure on OS (p=0.84). Conclusions: Ab exposure was associated with higher IMDC risk scores in both the ICI and VEGF-TT cohorts as well as inferior OS on univariable analysis. After adjusting for IMDC risk factors, histology and treatment line, we were unable to find an independent association between Ab exposure and OS in multivariable analysis for either cohort.[Table: see text]
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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 | 0.002 |
| 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 | 0.000 | 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".