Integrating novel immunotherapeutic approaches in organ‐preserving therapies for bladder cancer
Bibliographic record
Abstract
Bladder cancer (BC) is a prevalent malignancy with significant morbidity and mortality. Over the years, the landscape of bladder cancer treatment has witnessed notable advancements, particularly in the realm of immunotherapy. Immunotherapy has emerged as a promising adjunct to organ-preserving approaches, harnessing the immune system's potential to target and eliminate cancer cells. Organ preservation strategies offer viable alternatives to radical cystectomy to avoid the morbidities associated with radical surgery, as well as to respond to the needs of patients unfit for or who have refused surgery. However, the challenge lies in achieving durable disease control while minimizing treatment-related toxicities. This review highlights the significance of immune checkpoint inhibitors, such as anti-programmed cell death 1 (PD-1)/programmed cell death 1 ligand 1 (PD-L1) antibodies, in the treatment of localized bladder cancer. The clinical efficacy of immune checkpoint inhibitors, as both neoadjuvant and adjuvant therapies in combination with radiation or chemotherapy, is discussed. Moreover, the potential of immunotherapies beyond immune checkpoint inhibition, including combinations with bacillus Calmette-Guérin (BCG) instillations and/or investigational gene therapies, is explored. Furthermore, the predictive value of the tumour immune microenvironment for the success of these strategies is examined. Understanding the complex interplay between tumour immunity and therapeutic interventions can aid in identifying predictive biomarkers and tailoring personalized treatment strategies. Further research and clinical trials are warranted to optimize the use of immunotherapy in conjunction with organ-preserving therapies, potentially leading to enhanced patient outcomes and quality of life. LINKED ARTICLES: This article is part of a themed issue Immunotherapy in Cancer. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v183.6/issuetoc.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".