Neoadjuvant Immunotherapy in Bladder Cancer: Ushering in a New Era of Treatment—A Systematic Review of Current Evidence
Bibliographic record
Abstract
Background and objective: Immune checkpoint inhibitors (ICIs), alone or with platinum-based chemotherapy, have increasingly been studied as neoadjuvant therapy for muscle-invasive bladder cancer (BC). We sought to evaluate the current evidence about neoadjuvant immunotherapy for BC. Methods: In this systematic review, conducted in October 2024, only prospective studies on neoadjuvant immunotherapy for BC were included. Extracted variables encompassed study design, clinical-pathological characteristics, perioperative outcomes, pathological complete response (pCR) rates, overall survival (OS), event-free survival, and immune-related (irAEs) and treatment-related (TRAEs) adverse events. Key findings and limitations: From 726 records, 35 studies met the inclusion criteria. The highest pCR rate observed was 54%, utilizing durvalumab. Perioperative chemoimmunotherapy with durvalumab plus cisplatin/gemcitabine showed greater OS than chemotherapy alone in the NIAGARA trial. The NEMIO trial achieved the highest 12-mo OS rate of 97%, using durvalumab in combination with tremelimumab and dose-dense methotrexate, vinblastine, doxorubicin, and cisplatin, followed by the AURA trial (95%) and the LCCC1520 trial (91%). At 24 mo, the NEBULA trial reported a 100% OS rate with three doses of atezolizumab, while PrECOG PrE0807 reached and OS rate of 89% with nivolumab and lirilumab. The highest rates of grade 3 and 4 irAEs were reported for nivolumab combined with ipilimumab (54%) and for durvalumab combined with tremelimumab (64%). The most common grade 3/4 irAEs were hepatitis (2-27%), kidney injury (2-100%), and skin rash (1.1-41%). Grade 3/4 TRAEs were comparable between the ICI and chemotherapy groups. Conclusions and clinical implications: Neoadjuvant immunotherapy for BC has shown promising efficacy and a manageable adverse event profile. However, financial toxicity, the absence of predictive biomarkers, and the risk of significant irAEs remain challenges. Patient summary: This study reviewed recent clinical trials that tested immunotherapy before surgery in patients with bladder cancer. The results suggest that a combination of immunotherapy and chemotherapy may improve outcomes and reduce the risk of cancer returning. These findings could help shape future treatment options for patients with muscle-invasive bladder cancer.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".