Optimizing outcomes for high-risk, non-muscle-invasive bladder cancer: The evolving role of PD-(L)1 inhibition
Bibliographic record
Abstract
Transurethral resection of bladder tumor followed by intravesical Bacillus Calmette-Guérin (BCG) is the standard of care in high-risk, non-muscle-invasive bladder cancer (NMIBC). Although many patients respond, recurrence and progression are common. In addition, patients may be unable to receive induction + maintenance due to intolerance or supply issues. Therefore, alternative treatment options are urgently required. Programmed cell death (ligand) 1 (PD-[L]1) inhibitors show clinical benefit in phase 1/2 trials in BCG-unresponsive NMIBC patients. This review presents the status of PD-(L)1 inhibition in high-risk NMIBC and discusses future directions. PubMed and Google scholar were searched for articles relating to NMIBC immunotherapy and ClinicalTrials.gov for planned and ongoing clinical trials. Preclinical and early clinical studies show that BCG upregulates PD-L1 expression in bladder cancer cells and, when combined with a PD-(L)1 inhibitor, a potent antitumor response is activated. Based on this mechanism, several PD-(L)1 inhibitors are in phase 3 trials in BCG-naïve, high-risk NMIBC in combination with BCG. Whereas PD-(L)1 inhibitors are well characterized in patients with advanced malignancies, the impact of immune-related adverse events (irAE) on the benefit/risk ratio in NMIBC should be determined. Alternative routes to intravenous administration, like subcutaneous and intravesical administration, may facilitate adherence and access. The outcomes of combination of PD-(L)1 inhibitors and BCG in NMIBC are highly anticipated. There will be a need to address treatment resources, optimal management of irAEs and education and training related to use of this therapy in clinical practice.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".