Molecular biomarkers to help select neoadjuvant systemic therapy for urothelial carcinoma of the bladder
Bibliographic record
Abstract
PURPOSE OF REVIEW: In this review, we aimed to summarize the available evidence on pretreatment molecular biomarkers that may help to predict oncologic and pathologic outcomes in patients treated with neoadjuvant systemic therapy (NAST) for urothelial carcinoma of the bladder (UCB). RECENT FINDINGS: Several readily available and easily measurable blood-based biomarkers (e.g., neutrophil to lymphocyte or platelet-lymphocyte ratios) seems to help improve the selection of UCB patients who are most likely to benefit from NAST. Recent evidence suggests liquid biopsy including circulating tumor DNA (ctDNA) to be a promising tool to guide the administration of NAST in UCB patients. Pretreatment molecular and genetic characterization of transurethral resection of the bladder tumor samples may also help understand the tumor biology as luminal and basal tumor subtypes seems to be more responsive to NAST, while claudin-low and luminal-infiltrated tumor subtypes are less. In the context of neoadjuvant immunotherapy, programmed death-ligand 1 (PD-L1) status and ctDNA remain the only biomarker with possible value as the clinical utility of tumor mutational burden remains controversial/poor. SUMMARY: Biomarker approach is a necessary step to usher the age of precision/personalized medicine for muscle-invasive UCB with the overarching good to prevent both over- and under-therapy. The present review may offer a robust framework to compare and assess current and future molecular biomarkers for the selection of NAST in muscle-invasive UCB.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".