Urinary biomarkers for immunotherapy response in urothelial carcinoma: current status and future outlook
Bibliographic record
Abstract
INTRODUCTION: Immunotherapy treatments, such as intravesical Bacillus Calmette-Guérin (BCG) for non-muscle invasive bladder cancer (NMIBC) and systemic immune checkpoint inhibitors (ICIs) for all stages are central to the management of urothelial carcinoma (UC). Biomarkers that are prognostic or predictive and that help in monitoring these therapies are needed to guide and improve efficacy and tolerability. In this review, we evaluated the current landscape of urinary biomarkers for predicting response to immunotherapy (BCG and ICIs) in UC patients and their potential to guide personalized treatment strategies. AREAS COVERED: This narrative review summarizes current evidence on urinary biomarkers for predicting responses to BCG and ICIs therapies in UC, based on a comprehensive search of PubMed literature. EXPERT OPINION: Urinary biomarkers show significant potential for transforming UC immunotherapy by facilitating personalized treatment. Despite promising initial data for various analytes, large-scale validation and standardization must be addressed. We still need better, faster, easier, cheaper, reliable and valid urine-based biomarkers. Future research should focus on multiplex panels to enhance patient stratification and improve therapeutic outcomes and follow-up.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".